Emergency Medicine

From the Department of Medicine, Medical College of Georgia at the Augusta University-University of Georgia Medical Partnership, Athens, GA (Syed H. Ali, Syed Hyder, and Dr. Murrow), and the Department of Cardiology, Piedmont Heart Institute, Piedmont Athens Regional, Athens, GA (Dr. Murrow and Mrs. Davis).

Abstract

Objectives: The aim of this study was to describe the characteristics and in-hospital outcomes of patients with acute ST-segment elevation myocardial infarction (STEMI) during the early COVID-19 pandemic at Piedmont Athens Regional (PAR), a 330-bed tertiary referral center in Northeast Georgia. 

Methods: A retrospective study was conducted at PAR to evaluate patients with acute STEMI admitted over an 8-week period during the initial COVID-19 outbreak. This study group was compared to patients admitted during the corresponding period in 2019. The primary endpoint of this study was defined as a composite of sustained ventricular arrhythmia, congestive heart failure (CHF) with pulmonary congestion, and/or in-hospital mortality. 

Results: This study cohort was composed of 64 patients with acute STEMI; 30 patients (46.9%) were hospitalized during the COVID-19 pandemic. Patients with STEMI in both the COVID-19 and control groups had similar comorbidities, Killip classification score, and clinical presentations. The median (interquartile range) time from symptom onset to reperfusion (total ischemic time) increased from 99.5 minutes (84.8-132) in 2019 to 149 minutes (96.3-231.8; P = .032) in 2020. Hospitalization during the COVID-19 period was associated with an increased risk for combined in-hospital outcome (odds ratio, 3.96; P = .046). 

Conclusion: Patients with STEMI admitted during the first wave of the COVID-19 outbreak experienced longer total ischemic time and increased risk for combined in-hospital outcomes compared to patients admitted during the corresponding period in 2019. 

Keywords: myocardial infarction, acute coronary syndrome, hospitalization, outcomes.

The emergence of the SARS-Cov-2 virus in December 2019 caused a worldwide shift in resource allocation and the restructuring of health care systems within the span of a few months. With the rapid spread of infection, the World Health Organization officially declared a pandemic in March 2020. The pandemic led to the deferral and cancellation of in-person patient visits, routine diagnostic studies, and nonessential surgeries and procedures. This response occurred secondary to a joint effort to reduce transmission via stay-at-home mandates and appropriate social distancing.¹ 

Alongside the reduction in elective procedures and health care visits, significant reductions in hospitalization rates due to decreases in acute ST-segment elevation myocardial infarction (STEMI) and catheterization laboratory utilization have been reported in many studies from around the world.^2-7 Comprehensive data demonstrating the impact of the COVID-19 pandemic on acute STEMI patient characteristics, clinical presentation, and in-hospital outcomes are lacking. Although patients with previously diagnosed cardiovascular disease are more likely to encounter worse outcomes in the setting of COVID-19, there may also be an indirect impact of the pandemic on high-risk patients, including those without the infection.⁸ Several theories have been hypothesized to explain this phenomenon. One theory postulates that the fear of contracting the virus during hospitalization is great enough to prevent patients from seeking care.² Another theory suggests that the increased utilization of telemedicine prevents exacerbation of chronic conditions and the need for hospitalization.⁹ Contrary to this trend, previous studies have shown an increased incidence of acute STEMI following stressful events such as natural disasters.¹⁰ 

The aim of this study was to describe trends pertaining to clinical characteristics and in-hospital outcomes of patients with acute STEMI during the early COVID-19 pandemic at Piedmont Athens Regional (PAR), a 330-bed tertiary referral center in Northeast Georgia. 

Methods

A retrospective cohort study was conducted at PAR to evaluate patients with STEMI admitted to the cardiovascular intensive care unit over an 8-week period (March 5 to May 5, 2020) during the COVID-19 outbreak. COVID-19 was declared a national emergency on March 13, 2020, in the United States. The institutional review board at PAR approved the study; the need for individual consent was waived under the condition that participant data would undergo de-identification and be strictly safeguarded. 

Data Collection

Because there are seasonal variations in cardiovascular admissions, patient data from a control period (March 9 to May 9, 2019) were obtained to compare with data from the 2020 period. The number of patients with the diagnosis of acute STEMI during the COVID-19 period was recorded. Demographic data, clinical characteristics, and primary angiographic findings were gathered for all patients. Time from symptom onset to hospital admission and time from hospital admission to reperfusion (defined as door-to-balloon time) were documented for each patient. Killip classification was used to assess patients’ clinical status on admission. Length of stay was determined as days from hospital admission to discharge or death (if occurring during the same hospitalization).

Adverse in-hospital complications were also recorded. These were selected based on inclusion of the following categories of acute STEMI complications: ischemic, mechanical, arrhythmic, embolic, and inflammatory. The following complications occurred in our patient cohort: sustained ventricular arrhythmia, congestive heart failure (CHF) defined as congestion requiring intravenous diuretics, re-infarction, mechanical complications (free-wall rupture, ventricular septal defect, or mitral regurgitation), second- or third-degree atrioventricular block, atrial fibrillation, stroke, mechanical ventilation, major bleeding, pericarditis, cardiogenic shock, cardiac arrest, and in-hospital mortality. The primary outcome of this study was defined as a composite of sustained ventricular arrhythmia, CHF with congestion requiring intravenous diuretics, and/or in-hospital mortality. Ventricular arrythmia and CHF were included in the composite outcome because they are defined as the 2 most common causes of sudden cardiac death following acute STEMI.^11,12

Statistical Analysis

Normally distributed continuous variables and categorical variables were compared using the paired t-test. A 2-sided P value <.05 was considered to be statistically significant. Mean admission rates for acute STEMI hospitalizations were determined by dividing the number of admissions by the number of days in each time period. The daily rate of COVID-19 cases per 100,000 individuals was obtained from the Centers for Disease Control and Prevention COVID-19 database. All data analyses were performed using Microsoft Excel. 

Results

The study cohort consisted of 64 patients, of whom 30 (46.9%) were hospitalized between March 5 and May 5, 2020, and 34 (53.1%) who were admitted during the analogous time period in 2019. This reflected a 6% decrease in STEMI admissions at PAR in the COVID-19 cohort. 

Acute STEMI Hospitalization Rates and COVID-19 Incidence

The mean daily acute STEMI admission rate was 0.50 during the study period compared to 0.57 during the control period. During the study period in 2020 in the state of Georgia, the daily rate of newly confirmed COVID-19 cases ranged from 0.194 per 100,000 on March 5 to 8.778 per 100,000 on May 5. Results of COVID-19 testing were available for 9 STEMI patients, and of these 0 tests were positive. 

Baseline Characteristics

Baseline characteristics of the acute STEMI cohorts are presented in Table 1. Approximately 75% were male; median (interquartile range [IQR]) age was 60 (51-72) years. There were no significant differences in age and gender between the study periods. Three-quarters of patients had a history of hypertension, and 87.5% had a history of dyslipidemia. There was no significant difference in baseline comorbidity profiles between the 2 study periods; therefore, our sample populations shared similar characteristics.

Clinical Presentation

Significant differences were observed regarding the time intervals of STEMI patients in the COVID-19 period and the control period (Table 2). Median time from symptom onset to hospital admission (patient delay) was extended from 57.5 minutes (IQR, 40.3-106) in 2019 to 93 minutes (IQR, 48.8-132) in 2020; however, this difference was not statistically significant (P = .697). Median time from hospital admission to reperfusion (system delay) was prolonged from 45 minutes (IQR, 28-61) in 2019 to 78 minutes (IQR, 50-110) in 2020 (P < .001). Overall time from symptom onset to reperfusion (total ischemic time) increased from 99.5 minutes (IQR, 84.8-132) in 2019 to 149 minutes (IQR, 96.3-231.8) in 2020 (P = .032). 

Regarding mode of transportation, 23.5% of patients in 2019 were walk-in admissions to the emergency department. During the COVID-19 period, walk-in admissions decreased to 6.7% (P = .065). There were no significant differences between emergency medical service, transfer, or in-patient admissions for STEMI cases between the 2 study periods. 

Killip classification scores were calculated for all patients on admission; 90.6% of patients were classified as Killip Class 1. There was no significant difference between hemodynamic presentations during the COVID-19 period compared to the control period. 

Angiographic Data

Overall, 53 (82.8%) patients admitted with acute STEMI underwent coronary angiography during their hospital stay. The proportion of patients who underwent primary reperfusion was greater in the control period than in the COVID-19 period (85.3% vs 80%; P = .582). Angiographic characteristics and findings were similar between the 2 study groups (Table 2).

In-Hospital Outcomes

In-hospital outcome data were available for all patients. As shown in Table 3, hospitalization during the COVID-19 period was independently associated with an increased risk for combined in-hospital outcome (odds ratio, 3.96; P = .046). The rate of in-hospital mortality was greater in the COVID-19 period (P = .013). We found no significant difference when comparing secondary outcomes from admissions during the COVID-19 period and the control period in 2019. For the 5 patients who died during the study period, the primary diagnosis at death was acute STEMI complicated by CHF (3 patients) or cardiogenic shock (2 patients).

This single-center retrospective study at PAR looks at the impact of COVID-19 on hospitalizations for acute STEMI during the initial peak of the pandemic. The key findings of this study show a significant increase in ischemic time parameters (symptom onset to reperfusion, hospital admission to reperfusion), in-hospital mortality, and combined in-hospital outcomes.

There was a 49.5-minute increase in total ischemic time noted in this study (P = .032). Though there was a numerical increase in time of symptom onset to hospital admission by 23.5 minutes, this difference was not statistically significant (P = .697). However, this study observed a statistically significant 33-minute increase in ischemic time from hospital admission to reperfusion (P < .001). Multiple studies globally have found a similar increase in total ischemic times, including those conducted in China and Europe.^13-15 Every level of potential delay must be considered, including pre-hospital, triage and emergency department, and/or reperfusion team. Pre-hospital sources of delays that have been suggested include “stay-at-home” orders and apprehension to seek medical care due to concern about contracting the virus or overwhelming the health care facilities. There was a clinically significant 4-fold decrease in the number of walk-in acute STEMI cases in the study period. In 2019, there were 8 walk-in cases compared to 2 cases in 2020 (P = .065). However, this change was not statistically significant. In-hospital/systemic sources of delays have been mentioned in other studies; they include increased time taken to rule out COVID-19 (nasopharyngeal swab/chest x-ray) and increased time due to the need for intensive gowning and gloving procedures by staff. It was difficult to objectively determine the sources of system delay by the reperfusion team due to a lack of quantitative data.

In the current study, we found a significant increase in in-hospital mortality during the COVID-19 period compared to a parallel time frame in 2019. This finding is contrary to a multicenter study from Spain that reported no difference in in-hospital outcomes or mortality rates among all acute coronary syndrome cases.¹⁶ The worsening outcomes and prognosis may simply be a result of increased ischemic time; however, the virus that causes COVID-19 itself may play a role as well. Studies have found that SARS-Cov-2 infection places patients at greater risk for cardiovascular conditions such as hypercoagulability, myocarditis, and arrhythmias.¹⁷ In our study, however, there were no acute STEMI patients who tested positive for COVID-19. Therefore, we cannot discuss the impact of increased thrombus burden in patients with COVID-19. Piedmont Healthcare published a STEMI treatment protocol in May 2020 that advised increased use of tissue plasminogen activator (tPA) in COVID-19-positive cases; during the study period, however, there were no occasions when tPA use was deemed appropriate based on clinical judgment.

Our findings align with previous studies that describe an increase in combined in-hospital adverse outcomes during the COVID-19 era. Previous studies detected a higher rate of complications in the COVID-19 cohort, but in the current study, the adverse in-hospital course is unrelated to underlying infection.^18,19 This study reports a higher incidence of major in-hospital outcomes, including a 65% increase in the rate of combined in-hospital outcomes, which is similar to a multicenter study conducted in Israel.¹⁹ There was a 2.3-fold numerical increase in sustained ventricular arrhythmias and a 2.5-fold numerical increase in the incidence of cardiac arrest in the study period. This phenomenon was observed despite a similar rate of reperfusion procedures in both groups. 

Acute STEMI is a highly fatal condition with an incidence of 8.5 in 10,000 annually in the United States. While studies across the world have shown a 25% to 40% reduction in the rate of hospitalized acute coronary syndrome cases during the COVID-19 pandemic, the decrease from 34 to 30 STEMI admissions at PAR is not statistically significant.²⁰ Possible reasons for the reduction globally include increased out-of-hospital mortality and decreased incidence of acute STEMI across the general population as a result of improved access to telemedicine or decreased levels of life stressors.²⁰  

In summary, there was an increase in ischemic time to reperfusion, in-hospital mortality, and combined in-hospital outcomes for acute STEMI patients at PAR during the COVID period.  

Limitations

This study has several limitations. This is a single-center study, so the sample size is small and may not be generalizable to a larger population. This is a retrospective observational study, so causation cannot be inferred. This study analyzed ischemic time parameters as average rates over time rather than in an interrupted time series. Post-reperfusion outcomes were limited to hospital stay. Post-hospital follow-up would provide a better picture of the effects of STEMI intervention. There is no account of patients who died out-of-hospital secondary to acute STEMI. COVID-19 testing was not introduced until midway in our study period. Therefore, we cannot rule out the possibility of the SARS-Cov-2 virus inciting acute STEMI and subsequently leading to worse outcomes and poor prognosis. 

Conclusions

This study provides an analysis of the incidence, characteristics, and clinical outcomes of patients presenting with acute STEMI during the early period of the COVID-19 pandemic. In-hospital mortality and ischemic time to reperfusion increased while combined in-hospital outcomes worsened. 

Acknowledgment: The authors thank Piedmont Athens Regional IRB for approving this project and allowing access to patient data.

Corresponding author: Syed H. Ali; Department of Medicine, Medical College of Georgia at the Augusta University-University of Georgia Medical Partnership, 30606, Athens, GA; [email protected]

Disclosures: None reported.

doi:10.12788/jcom.0085

From the Department of Medicine, Medical College of Georgia at the Augusta University-University of Georgia Medical Partnership, Athens, GA (Syed H. Ali, Syed Hyder, and Dr. Murrow), and the Department of Cardiology, Piedmont Heart Institute, Piedmont Athens Regional, Athens, GA (Dr. Murrow and Mrs. Davis).

Abstract

Objectives: The aim of this study was to describe the characteristics and in-hospital outcomes of patients with acute ST-segment elevation myocardial infarction (STEMI) during the early COVID-19 pandemic at Piedmont Athens Regional (PAR), a 330-bed tertiary referral center in Northeast Georgia. 

Methods: A retrospective study was conducted at PAR to evaluate patients with acute STEMI admitted over an 8-week period during the initial COVID-19 outbreak. This study group was compared to patients admitted during the corresponding period in 2019. The primary endpoint of this study was defined as a composite of sustained ventricular arrhythmia, congestive heart failure (CHF) with pulmonary congestion, and/or in-hospital mortality. 

Results: This study cohort was composed of 64 patients with acute STEMI; 30 patients (46.9%) were hospitalized during the COVID-19 pandemic. Patients with STEMI in both the COVID-19 and control groups had similar comorbidities, Killip classification score, and clinical presentations. The median (interquartile range) time from symptom onset to reperfusion (total ischemic time) increased from 99.5 minutes (84.8-132) in 2019 to 149 minutes (96.3-231.8; P = .032) in 2020. Hospitalization during the COVID-19 period was associated with an increased risk for combined in-hospital outcome (odds ratio, 3.96; P = .046). 

Conclusion: Patients with STEMI admitted during the first wave of the COVID-19 outbreak experienced longer total ischemic time and increased risk for combined in-hospital outcomes compared to patients admitted during the corresponding period in 2019. 

Keywords: myocardial infarction, acute coronary syndrome, hospitalization, outcomes.

The emergence of the SARS-Cov-2 virus in December 2019 caused a worldwide shift in resource allocation and the restructuring of health care systems within the span of a few months. With the rapid spread of infection, the World Health Organization officially declared a pandemic in March 2020. The pandemic led to the deferral and cancellation of in-person patient visits, routine diagnostic studies, and nonessential surgeries and procedures. This response occurred secondary to a joint effort to reduce transmission via stay-at-home mandates and appropriate social distancing.¹ 

Alongside the reduction in elective procedures and health care visits, significant reductions in hospitalization rates due to decreases in acute ST-segment elevation myocardial infarction (STEMI) and catheterization laboratory utilization have been reported in many studies from around the world.^2-7 Comprehensive data demonstrating the impact of the COVID-19 pandemic on acute STEMI patient characteristics, clinical presentation, and in-hospital outcomes are lacking. Although patients with previously diagnosed cardiovascular disease are more likely to encounter worse outcomes in the setting of COVID-19, there may also be an indirect impact of the pandemic on high-risk patients, including those without the infection.⁸ Several theories have been hypothesized to explain this phenomenon. One theory postulates that the fear of contracting the virus during hospitalization is great enough to prevent patients from seeking care.² Another theory suggests that the increased utilization of telemedicine prevents exacerbation of chronic conditions and the need for hospitalization.⁹ Contrary to this trend, previous studies have shown an increased incidence of acute STEMI following stressful events such as natural disasters.¹⁰ 

The aim of this study was to describe trends pertaining to clinical characteristics and in-hospital outcomes of patients with acute STEMI during the early COVID-19 pandemic at Piedmont Athens Regional (PAR), a 330-bed tertiary referral center in Northeast Georgia. 

Methods

A retrospective cohort study was conducted at PAR to evaluate patients with STEMI admitted to the cardiovascular intensive care unit over an 8-week period (March 5 to May 5, 2020) during the COVID-19 outbreak. COVID-19 was declared a national emergency on March 13, 2020, in the United States. The institutional review board at PAR approved the study; the need for individual consent was waived under the condition that participant data would undergo de-identification and be strictly safeguarded. 

Data Collection

Because there are seasonal variations in cardiovascular admissions, patient data from a control period (March 9 to May 9, 2019) were obtained to compare with data from the 2020 period. The number of patients with the diagnosis of acute STEMI during the COVID-19 period was recorded. Demographic data, clinical characteristics, and primary angiographic findings were gathered for all patients. Time from symptom onset to hospital admission and time from hospital admission to reperfusion (defined as door-to-balloon time) were documented for each patient. Killip classification was used to assess patients’ clinical status on admission. Length of stay was determined as days from hospital admission to discharge or death (if occurring during the same hospitalization).

Adverse in-hospital complications were also recorded. These were selected based on inclusion of the following categories of acute STEMI complications: ischemic, mechanical, arrhythmic, embolic, and inflammatory. The following complications occurred in our patient cohort: sustained ventricular arrhythmia, congestive heart failure (CHF) defined as congestion requiring intravenous diuretics, re-infarction, mechanical complications (free-wall rupture, ventricular septal defect, or mitral regurgitation), second- or third-degree atrioventricular block, atrial fibrillation, stroke, mechanical ventilation, major bleeding, pericarditis, cardiogenic shock, cardiac arrest, and in-hospital mortality. The primary outcome of this study was defined as a composite of sustained ventricular arrhythmia, CHF with congestion requiring intravenous diuretics, and/or in-hospital mortality. Ventricular arrythmia and CHF were included in the composite outcome because they are defined as the 2 most common causes of sudden cardiac death following acute STEMI.^11,12

Statistical Analysis

Normally distributed continuous variables and categorical variables were compared using the paired t-test. A 2-sided P value <.05 was considered to be statistically significant. Mean admission rates for acute STEMI hospitalizations were determined by dividing the number of admissions by the number of days in each time period. The daily rate of COVID-19 cases per 100,000 individuals was obtained from the Centers for Disease Control and Prevention COVID-19 database. All data analyses were performed using Microsoft Excel. 

Results

The study cohort consisted of 64 patients, of whom 30 (46.9%) were hospitalized between March 5 and May 5, 2020, and 34 (53.1%) who were admitted during the analogous time period in 2019. This reflected a 6% decrease in STEMI admissions at PAR in the COVID-19 cohort. 

Acute STEMI Hospitalization Rates and COVID-19 Incidence

The mean daily acute STEMI admission rate was 0.50 during the study period compared to 0.57 during the control period. During the study period in 2020 in the state of Georgia, the daily rate of newly confirmed COVID-19 cases ranged from 0.194 per 100,000 on March 5 to 8.778 per 100,000 on May 5. Results of COVID-19 testing were available for 9 STEMI patients, and of these 0 tests were positive. 

Baseline Characteristics

Baseline characteristics of the acute STEMI cohorts are presented in Table 1. Approximately 75% were male; median (interquartile range [IQR]) age was 60 (51-72) years. There were no significant differences in age and gender between the study periods. Three-quarters of patients had a history of hypertension, and 87.5% had a history of dyslipidemia. There was no significant difference in baseline comorbidity profiles between the 2 study periods; therefore, our sample populations shared similar characteristics.

Clinical Presentation

Significant differences were observed regarding the time intervals of STEMI patients in the COVID-19 period and the control period (Table 2). Median time from symptom onset to hospital admission (patient delay) was extended from 57.5 minutes (IQR, 40.3-106) in 2019 to 93 minutes (IQR, 48.8-132) in 2020; however, this difference was not statistically significant (P = .697). Median time from hospital admission to reperfusion (system delay) was prolonged from 45 minutes (IQR, 28-61) in 2019 to 78 minutes (IQR, 50-110) in 2020 (P < .001). Overall time from symptom onset to reperfusion (total ischemic time) increased from 99.5 minutes (IQR, 84.8-132) in 2019 to 149 minutes (IQR, 96.3-231.8) in 2020 (P = .032). 

Regarding mode of transportation, 23.5% of patients in 2019 were walk-in admissions to the emergency department. During the COVID-19 period, walk-in admissions decreased to 6.7% (P = .065). There were no significant differences between emergency medical service, transfer, or in-patient admissions for STEMI cases between the 2 study periods. 

Killip classification scores were calculated for all patients on admission; 90.6% of patients were classified as Killip Class 1. There was no significant difference between hemodynamic presentations during the COVID-19 period compared to the control period. 

Angiographic Data

Overall, 53 (82.8%) patients admitted with acute STEMI underwent coronary angiography during their hospital stay. The proportion of patients who underwent primary reperfusion was greater in the control period than in the COVID-19 period (85.3% vs 80%; P = .582). Angiographic characteristics and findings were similar between the 2 study groups (Table 2).

In-Hospital Outcomes

In-hospital outcome data were available for all patients. As shown in Table 3, hospitalization during the COVID-19 period was independently associated with an increased risk for combined in-hospital outcome (odds ratio, 3.96; P = .046). The rate of in-hospital mortality was greater in the COVID-19 period (P = .013). We found no significant difference when comparing secondary outcomes from admissions during the COVID-19 period and the control period in 2019. For the 5 patients who died during the study period, the primary diagnosis at death was acute STEMI complicated by CHF (3 patients) or cardiogenic shock (2 patients).

This single-center retrospective study at PAR looks at the impact of COVID-19 on hospitalizations for acute STEMI during the initial peak of the pandemic. The key findings of this study show a significant increase in ischemic time parameters (symptom onset to reperfusion, hospital admission to reperfusion), in-hospital mortality, and combined in-hospital outcomes.

There was a 49.5-minute increase in total ischemic time noted in this study (P = .032). Though there was a numerical increase in time of symptom onset to hospital admission by 23.5 minutes, this difference was not statistically significant (P = .697). However, this study observed a statistically significant 33-minute increase in ischemic time from hospital admission to reperfusion (P < .001). Multiple studies globally have found a similar increase in total ischemic times, including those conducted in China and Europe.^13-15 Every level of potential delay must be considered, including pre-hospital, triage and emergency department, and/or reperfusion team. Pre-hospital sources of delays that have been suggested include “stay-at-home” orders and apprehension to seek medical care due to concern about contracting the virus or overwhelming the health care facilities. There was a clinically significant 4-fold decrease in the number of walk-in acute STEMI cases in the study period. In 2019, there were 8 walk-in cases compared to 2 cases in 2020 (P = .065). However, this change was not statistically significant. In-hospital/systemic sources of delays have been mentioned in other studies; they include increased time taken to rule out COVID-19 (nasopharyngeal swab/chest x-ray) and increased time due to the need for intensive gowning and gloving procedures by staff. It was difficult to objectively determine the sources of system delay by the reperfusion team due to a lack of quantitative data.

In the current study, we found a significant increase in in-hospital mortality during the COVID-19 period compared to a parallel time frame in 2019. This finding is contrary to a multicenter study from Spain that reported no difference in in-hospital outcomes or mortality rates among all acute coronary syndrome cases.¹⁶ The worsening outcomes and prognosis may simply be a result of increased ischemic time; however, the virus that causes COVID-19 itself may play a role as well. Studies have found that SARS-Cov-2 infection places patients at greater risk for cardiovascular conditions such as hypercoagulability, myocarditis, and arrhythmias.¹⁷ In our study, however, there were no acute STEMI patients who tested positive for COVID-19. Therefore, we cannot discuss the impact of increased thrombus burden in patients with COVID-19. Piedmont Healthcare published a STEMI treatment protocol in May 2020 that advised increased use of tissue plasminogen activator (tPA) in COVID-19-positive cases; during the study period, however, there were no occasions when tPA use was deemed appropriate based on clinical judgment.

Our findings align with previous studies that describe an increase in combined in-hospital adverse outcomes during the COVID-19 era. Previous studies detected a higher rate of complications in the COVID-19 cohort, but in the current study, the adverse in-hospital course is unrelated to underlying infection.^18,19 This study reports a higher incidence of major in-hospital outcomes, including a 65% increase in the rate of combined in-hospital outcomes, which is similar to a multicenter study conducted in Israel.¹⁹ There was a 2.3-fold numerical increase in sustained ventricular arrhythmias and a 2.5-fold numerical increase in the incidence of cardiac arrest in the study period. This phenomenon was observed despite a similar rate of reperfusion procedures in both groups. 

Acute STEMI is a highly fatal condition with an incidence of 8.5 in 10,000 annually in the United States. While studies across the world have shown a 25% to 40% reduction in the rate of hospitalized acute coronary syndrome cases during the COVID-19 pandemic, the decrease from 34 to 30 STEMI admissions at PAR is not statistically significant.²⁰ Possible reasons for the reduction globally include increased out-of-hospital mortality and decreased incidence of acute STEMI across the general population as a result of improved access to telemedicine or decreased levels of life stressors.²⁰  

In summary, there was an increase in ischemic time to reperfusion, in-hospital mortality, and combined in-hospital outcomes for acute STEMI patients at PAR during the COVID period.  

Limitations

This study has several limitations. This is a single-center study, so the sample size is small and may not be generalizable to a larger population. This is a retrospective observational study, so causation cannot be inferred. This study analyzed ischemic time parameters as average rates over time rather than in an interrupted time series. Post-reperfusion outcomes were limited to hospital stay. Post-hospital follow-up would provide a better picture of the effects of STEMI intervention. There is no account of patients who died out-of-hospital secondary to acute STEMI. COVID-19 testing was not introduced until midway in our study period. Therefore, we cannot rule out the possibility of the SARS-Cov-2 virus inciting acute STEMI and subsequently leading to worse outcomes and poor prognosis. 

Conclusions

This study provides an analysis of the incidence, characteristics, and clinical outcomes of patients presenting with acute STEMI during the early period of the COVID-19 pandemic. In-hospital mortality and ischemic time to reperfusion increased while combined in-hospital outcomes worsened. 

Acknowledgment: The authors thank Piedmont Athens Regional IRB for approving this project and allowing access to patient data.

Corresponding author: Syed H. Ali; Department of Medicine, Medical College of Georgia at the Augusta University-University of Georgia Medical Partnership, 30606, Athens, GA; [email protected]

Disclosures: None reported.

doi:10.12788/jcom.0085

From the Department of Medicine, Medical College of Georgia at the Augusta University-University of Georgia Medical Partnership, Athens, GA (Syed H. Ali, Syed Hyder, and Dr. Murrow), and the Department of Cardiology, Piedmont Heart Institute, Piedmont Athens Regional, Athens, GA (Dr. Murrow and Mrs. Davis).

Abstract

Objectives: The aim of this study was to describe the characteristics and in-hospital outcomes of patients with acute ST-segment elevation myocardial infarction (STEMI) during the early COVID-19 pandemic at Piedmont Athens Regional (PAR), a 330-bed tertiary referral center in Northeast Georgia. 

Methods: A retrospective study was conducted at PAR to evaluate patients with acute STEMI admitted over an 8-week period during the initial COVID-19 outbreak. This study group was compared to patients admitted during the corresponding period in 2019. The primary endpoint of this study was defined as a composite of sustained ventricular arrhythmia, congestive heart failure (CHF) with pulmonary congestion, and/or in-hospital mortality. 

Results: This study cohort was composed of 64 patients with acute STEMI; 30 patients (46.9%) were hospitalized during the COVID-19 pandemic. Patients with STEMI in both the COVID-19 and control groups had similar comorbidities, Killip classification score, and clinical presentations. The median (interquartile range) time from symptom onset to reperfusion (total ischemic time) increased from 99.5 minutes (84.8-132) in 2019 to 149 minutes (96.3-231.8; P = .032) in 2020. Hospitalization during the COVID-19 period was associated with an increased risk for combined in-hospital outcome (odds ratio, 3.96; P = .046). 

Conclusion: Patients with STEMI admitted during the first wave of the COVID-19 outbreak experienced longer total ischemic time and increased risk for combined in-hospital outcomes compared to patients admitted during the corresponding period in 2019. 

Keywords: myocardial infarction, acute coronary syndrome, hospitalization, outcomes.

The emergence of the SARS-Cov-2 virus in December 2019 caused a worldwide shift in resource allocation and the restructuring of health care systems within the span of a few months. With the rapid spread of infection, the World Health Organization officially declared a pandemic in March 2020. The pandemic led to the deferral and cancellation of in-person patient visits, routine diagnostic studies, and nonessential surgeries and procedures. This response occurred secondary to a joint effort to reduce transmission via stay-at-home mandates and appropriate social distancing.¹ 

Alongside the reduction in elective procedures and health care visits, significant reductions in hospitalization rates due to decreases in acute ST-segment elevation myocardial infarction (STEMI) and catheterization laboratory utilization have been reported in many studies from around the world.^2-7 Comprehensive data demonstrating the impact of the COVID-19 pandemic on acute STEMI patient characteristics, clinical presentation, and in-hospital outcomes are lacking. Although patients with previously diagnosed cardiovascular disease are more likely to encounter worse outcomes in the setting of COVID-19, there may also be an indirect impact of the pandemic on high-risk patients, including those without the infection.⁸ Several theories have been hypothesized to explain this phenomenon. One theory postulates that the fear of contracting the virus during hospitalization is great enough to prevent patients from seeking care.² Another theory suggests that the increased utilization of telemedicine prevents exacerbation of chronic conditions and the need for hospitalization.⁹ Contrary to this trend, previous studies have shown an increased incidence of acute STEMI following stressful events such as natural disasters.¹⁰ 

The aim of this study was to describe trends pertaining to clinical characteristics and in-hospital outcomes of patients with acute STEMI during the early COVID-19 pandemic at Piedmont Athens Regional (PAR), a 330-bed tertiary referral center in Northeast Georgia. 

Methods

A retrospective cohort study was conducted at PAR to evaluate patients with STEMI admitted to the cardiovascular intensive care unit over an 8-week period (March 5 to May 5, 2020) during the COVID-19 outbreak. COVID-19 was declared a national emergency on March 13, 2020, in the United States. The institutional review board at PAR approved the study; the need for individual consent was waived under the condition that participant data would undergo de-identification and be strictly safeguarded. 

Data Collection

Because there are seasonal variations in cardiovascular admissions, patient data from a control period (March 9 to May 9, 2019) were obtained to compare with data from the 2020 period. The number of patients with the diagnosis of acute STEMI during the COVID-19 period was recorded. Demographic data, clinical characteristics, and primary angiographic findings were gathered for all patients. Time from symptom onset to hospital admission and time from hospital admission to reperfusion (defined as door-to-balloon time) were documented for each patient. Killip classification was used to assess patients’ clinical status on admission. Length of stay was determined as days from hospital admission to discharge or death (if occurring during the same hospitalization).

Adverse in-hospital complications were also recorded. These were selected based on inclusion of the following categories of acute STEMI complications: ischemic, mechanical, arrhythmic, embolic, and inflammatory. The following complications occurred in our patient cohort: sustained ventricular arrhythmia, congestive heart failure (CHF) defined as congestion requiring intravenous diuretics, re-infarction, mechanical complications (free-wall rupture, ventricular septal defect, or mitral regurgitation), second- or third-degree atrioventricular block, atrial fibrillation, stroke, mechanical ventilation, major bleeding, pericarditis, cardiogenic shock, cardiac arrest, and in-hospital mortality. The primary outcome of this study was defined as a composite of sustained ventricular arrhythmia, CHF with congestion requiring intravenous diuretics, and/or in-hospital mortality. Ventricular arrythmia and CHF were included in the composite outcome because they are defined as the 2 most common causes of sudden cardiac death following acute STEMI.^11,12

Statistical Analysis

Normally distributed continuous variables and categorical variables were compared using the paired t-test. A 2-sided P value <.05 was considered to be statistically significant. Mean admission rates for acute STEMI hospitalizations were determined by dividing the number of admissions by the number of days in each time period. The daily rate of COVID-19 cases per 100,000 individuals was obtained from the Centers for Disease Control and Prevention COVID-19 database. All data analyses were performed using Microsoft Excel. 

Results

The study cohort consisted of 64 patients, of whom 30 (46.9%) were hospitalized between March 5 and May 5, 2020, and 34 (53.1%) who were admitted during the analogous time period in 2019. This reflected a 6% decrease in STEMI admissions at PAR in the COVID-19 cohort. 

Acute STEMI Hospitalization Rates and COVID-19 Incidence

The mean daily acute STEMI admission rate was 0.50 during the study period compared to 0.57 during the control period. During the study period in 2020 in the state of Georgia, the daily rate of newly confirmed COVID-19 cases ranged from 0.194 per 100,000 on March 5 to 8.778 per 100,000 on May 5. Results of COVID-19 testing were available for 9 STEMI patients, and of these 0 tests were positive. 

Baseline Characteristics

Baseline characteristics of the acute STEMI cohorts are presented in Table 1. Approximately 75% were male; median (interquartile range [IQR]) age was 60 (51-72) years. There were no significant differences in age and gender between the study periods. Three-quarters of patients had a history of hypertension, and 87.5% had a history of dyslipidemia. There was no significant difference in baseline comorbidity profiles between the 2 study periods; therefore, our sample populations shared similar characteristics.

Clinical Presentation

Significant differences were observed regarding the time intervals of STEMI patients in the COVID-19 period and the control period (Table 2). Median time from symptom onset to hospital admission (patient delay) was extended from 57.5 minutes (IQR, 40.3-106) in 2019 to 93 minutes (IQR, 48.8-132) in 2020; however, this difference was not statistically significant (P = .697). Median time from hospital admission to reperfusion (system delay) was prolonged from 45 minutes (IQR, 28-61) in 2019 to 78 minutes (IQR, 50-110) in 2020 (P < .001). Overall time from symptom onset to reperfusion (total ischemic time) increased from 99.5 minutes (IQR, 84.8-132) in 2019 to 149 minutes (IQR, 96.3-231.8) in 2020 (P = .032). 

Regarding mode of transportation, 23.5% of patients in 2019 were walk-in admissions to the emergency department. During the COVID-19 period, walk-in admissions decreased to 6.7% (P = .065). There were no significant differences between emergency medical service, transfer, or in-patient admissions for STEMI cases between the 2 study periods. 

Killip classification scores were calculated for all patients on admission; 90.6% of patients were classified as Killip Class 1. There was no significant difference between hemodynamic presentations during the COVID-19 period compared to the control period. 

Angiographic Data

Overall, 53 (82.8%) patients admitted with acute STEMI underwent coronary angiography during their hospital stay. The proportion of patients who underwent primary reperfusion was greater in the control period than in the COVID-19 period (85.3% vs 80%; P = .582). Angiographic characteristics and findings were similar between the 2 study groups (Table 2).

In-Hospital Outcomes

In-hospital outcome data were available for all patients. As shown in Table 3, hospitalization during the COVID-19 period was independently associated with an increased risk for combined in-hospital outcome (odds ratio, 3.96; P = .046). The rate of in-hospital mortality was greater in the COVID-19 period (P = .013). We found no significant difference when comparing secondary outcomes from admissions during the COVID-19 period and the control period in 2019. For the 5 patients who died during the study period, the primary diagnosis at death was acute STEMI complicated by CHF (3 patients) or cardiogenic shock (2 patients).

This single-center retrospective study at PAR looks at the impact of COVID-19 on hospitalizations for acute STEMI during the initial peak of the pandemic. The key findings of this study show a significant increase in ischemic time parameters (symptom onset to reperfusion, hospital admission to reperfusion), in-hospital mortality, and combined in-hospital outcomes.

There was a 49.5-minute increase in total ischemic time noted in this study (P = .032). Though there was a numerical increase in time of symptom onset to hospital admission by 23.5 minutes, this difference was not statistically significant (P = .697). However, this study observed a statistically significant 33-minute increase in ischemic time from hospital admission to reperfusion (P < .001). Multiple studies globally have found a similar increase in total ischemic times, including those conducted in China and Europe.^13-15 Every level of potential delay must be considered, including pre-hospital, triage and emergency department, and/or reperfusion team. Pre-hospital sources of delays that have been suggested include “stay-at-home” orders and apprehension to seek medical care due to concern about contracting the virus or overwhelming the health care facilities. There was a clinically significant 4-fold decrease in the number of walk-in acute STEMI cases in the study period. In 2019, there were 8 walk-in cases compared to 2 cases in 2020 (P = .065). However, this change was not statistically significant. In-hospital/systemic sources of delays have been mentioned in other studies; they include increased time taken to rule out COVID-19 (nasopharyngeal swab/chest x-ray) and increased time due to the need for intensive gowning and gloving procedures by staff. It was difficult to objectively determine the sources of system delay by the reperfusion team due to a lack of quantitative data.

In the current study, we found a significant increase in in-hospital mortality during the COVID-19 period compared to a parallel time frame in 2019. This finding is contrary to a multicenter study from Spain that reported no difference in in-hospital outcomes or mortality rates among all acute coronary syndrome cases.¹⁶ The worsening outcomes and prognosis may simply be a result of increased ischemic time; however, the virus that causes COVID-19 itself may play a role as well. Studies have found that SARS-Cov-2 infection places patients at greater risk for cardiovascular conditions such as hypercoagulability, myocarditis, and arrhythmias.¹⁷ In our study, however, there were no acute STEMI patients who tested positive for COVID-19. Therefore, we cannot discuss the impact of increased thrombus burden in patients with COVID-19. Piedmont Healthcare published a STEMI treatment protocol in May 2020 that advised increased use of tissue plasminogen activator (tPA) in COVID-19-positive cases; during the study period, however, there were no occasions when tPA use was deemed appropriate based on clinical judgment.

Our findings align with previous studies that describe an increase in combined in-hospital adverse outcomes during the COVID-19 era. Previous studies detected a higher rate of complications in the COVID-19 cohort, but in the current study, the adverse in-hospital course is unrelated to underlying infection.^18,19 This study reports a higher incidence of major in-hospital outcomes, including a 65% increase in the rate of combined in-hospital outcomes, which is similar to a multicenter study conducted in Israel.¹⁹ There was a 2.3-fold numerical increase in sustained ventricular arrhythmias and a 2.5-fold numerical increase in the incidence of cardiac arrest in the study period. This phenomenon was observed despite a similar rate of reperfusion procedures in both groups. 

Acute STEMI is a highly fatal condition with an incidence of 8.5 in 10,000 annually in the United States. While studies across the world have shown a 25% to 40% reduction in the rate of hospitalized acute coronary syndrome cases during the COVID-19 pandemic, the decrease from 34 to 30 STEMI admissions at PAR is not statistically significant.²⁰ Possible reasons for the reduction globally include increased out-of-hospital mortality and decreased incidence of acute STEMI across the general population as a result of improved access to telemedicine or decreased levels of life stressors.²⁰  

In summary, there was an increase in ischemic time to reperfusion, in-hospital mortality, and combined in-hospital outcomes for acute STEMI patients at PAR during the COVID period.  

Limitations

This study has several limitations. This is a single-center study, so the sample size is small and may not be generalizable to a larger population. This is a retrospective observational study, so causation cannot be inferred. This study analyzed ischemic time parameters as average rates over time rather than in an interrupted time series. Post-reperfusion outcomes were limited to hospital stay. Post-hospital follow-up would provide a better picture of the effects of STEMI intervention. There is no account of patients who died out-of-hospital secondary to acute STEMI. COVID-19 testing was not introduced until midway in our study period. Therefore, we cannot rule out the possibility of the SARS-Cov-2 virus inciting acute STEMI and subsequently leading to worse outcomes and poor prognosis. 

Conclusions

This study provides an analysis of the incidence, characteristics, and clinical outcomes of patients presenting with acute STEMI during the early period of the COVID-19 pandemic. In-hospital mortality and ischemic time to reperfusion increased while combined in-hospital outcomes worsened. 

Acknowledgment: The authors thank Piedmont Athens Regional IRB for approving this project and allowing access to patient data.

Corresponding author: Syed H. Ali; Department of Medicine, Medical College of Georgia at the Augusta University-University of Georgia Medical Partnership, 30606, Athens, GA; [email protected]

Disclosures: None reported.

doi:10.12788/jcom.0085

From Lahey Hospital and Medical Center, Burlington, MA (Drs. Liesching and Lei), and Tufts University School of Medicine, Boston, MA (Dr. Liesching)

ABSTRACT

Objective: To compare the utilization of oxygen therapies and clinical outcomes of patients admitted for COVID-19 during the second surge of the pandemic to that of patients admitted during the first surge.

Design: Observational study using a registry database.

Setting: Three hospitals (791 inpatient beds and 76 intensive care unit [ICU] beds) within the Beth Israel Lahey Health system in Massachusetts.

Participants: We included 3183 patients with COVID-19 admitted to hospitals.

Measurements: Baseline data included demographics and comorbidities. Treatments included low-flow supplemental oxygen (2-6 L/min), high-flow oxygen via nasal cannula, and invasive mechanical ventilation. Outcomes included ICU admission, length of stay, ventilator days, and mortality.

Results: A total of 3183 patients were included: 1586 during the first surge and 1597 during the second surge. Compared to the first surge, patients admitted during the second surge had a similar rate of receiving low-flow supplemental oxygen (65.8% vs 64.1%, P = .3), a higher rate of receiving high-flow nasal cannula (15.4% vs 10.8%, P = .0001), and a lower ventilation rate (5.6% vs 9.7%, P < .0001). The outcomes during the second surge were better than those during the first surge: lower ICU admission rate (8.1% vs 12.7%, P < .0001), shorter length of hospital stay (5 vs 6 days, P < .0001), fewer ventilator days (10 vs 16, P = .01), and lower mortality (8.3% vs 19.2%, P < .0001). Among ventilated patients, those who received high-flow nasal cannula had lower mortality.

Conclusion: Compared to the first surge of the COVID-19 pandemic, patients admitted during the second surge had similar likelihood of receiving low-flow supplemental oxygen, were more likely to receive high-flow nasal cannula, were less likely to be ventilated, and had better outcomes.

Keywords: supplemental oxygen, high-flow nasal cannula, ventilator.

The respiratory system receives the major impact of SARS-CoV-2 virus, and hypoxemia has been the predominant diagnosis for patients hospitalized with COVID-19.^1,2 During the initial stage of the pandemic, oxygen therapies and mechanical ventilation were the only choices for these patients.^3-6 Standard-of-care treatment for patients with COVID-19 during the initial surge included oxygen therapies and mechanical ventilation for hypoxemia and medications for comorbidities and COVID-19–associated sequelae, such as multi-organ dysfunction and failure. A report from New York during the first surge (May 2020) showed that among 5700 hospitalized patients with COVID-19, 27.8% received supplemental oxygen and 12.2% received invasive mechanical ventilation.⁷ High-flow nasal cannula (HFNC) oxygen delivery has been utilized widely throughout the pandemic due to its superiority over other noninvasive respiratory support techniques.^8-12 Mechanical ventilation is always necessary for critically ill patients with acute respiratory distress syndrome. However, ventilator scarcity has become a bottleneck in caring for severely ill patients with COVID-19 during the pandemic.¹³

The clinical outcomes of hospitalized COVID-19 patients include a high intubation rate, long length of hospital and intensive care unit (ICU) stay, and high mortality.^14,15 As the pandemic evolved, new medications, including remdesivir, hydroxychloroquine, lopinavir, or interferon β-1a, were used in addition to the standard of care, but these did not result in significantly different mortality from standard of care.¹⁶ Steroids are becoming foundational to the treatment of severe COVID-19 pneumonia, but evidence from high-quality randomized controlled clinical trials is lacking.¹⁷ 

During the first surge from March to May 2020, Massachusetts had the third highest number of COVID-19 cases among states in the United States.¹⁸ In early 2021, COVID-19 cases were climbing close to the peak of the second surge in Massachusetts. In this study, we compared utilization of low-flow supplemental oxygen, HFNC, and mechanical ventilation and clinical outcomes of patients admitted to 3 hospitals in Massachusetts during the second surge of the pandemic to that of patients admitted during the first surge.

Methods

Setting

Beth Israel Lahey Health is a system of academic and teaching hospitals with primary care and specialty care providers. We included 3 centers within the Beth Israel Lahey Health system in Massachusetts: Lahey Hospital and Medical Center, with 335 inpatient hospital beds and 52 critical care beds; Beverly Hospital, with 227 beds and 14 critical care beds; and Winchester Hospital, with 229 beds and 10 ICU beds.

Participants

We included patients admitted to the 3 hospitals with COVID-19 as a primary or secondary diagnosis during the first surge of the pandemic (March 1, 2020 to June 15, 2020) and the second surge (November 15, 2020 to January 27, 2021). The timeframe of the first surge was defined as the window between the start date and the end date of data collection. During the time window of the first surge, 1586 patients were included. The start time of the second surge was defined as the date when the data collection was restarted; the end date was set when the number of patients (1597) accumulated was close to the number of patients in the first surge (1586), so that the two groups had similar sample size.

Study Design

A data registry of COVID-19 patients was created by our institution, and the data were prospectively collected starting in March 2020. We retrospectively extracted data on the following from the registry database for this observational study: demographics and baseline comorbidities; the use of low-flow supplemental oxygen, HFNC, and invasive mechanical ventilator; and ICU admission, length of hospital stay, length of ICU stay, and hospital discharge disposition. Start and end times for each oxygen therapy were not entered in the registry. Data about other oxygen therapies, such as noninvasive positive-pressure ventilation, were not collected in the registry database, and therefore were not included in the analysis.

Statistical Analysis

Continuous variables (eg, age) were tested for data distribution normality using the Shapiro-Wilk test. Normally distributed data were tested using unpaired t-tests and displayed as mean (SD). The skewed data were tested using the Wilcoxon rank sum test and displayed as median (interquartile range [IQR]). The categorical variables were compared using chi-square test. Comparisons with P ≤ .05 were considered significantly different. Statistical analysis for this study was generated using Statistical Analysis Software (SAS), version 9.4 for Windows (SAS Institute Inc.).

Results

Baseline Characteristics

We included 3183 patients: 1586 admitted during the first surge and 1597 admitted during the second surge. Baseline characteristics of patients with COVID-19 admitted during the first and second surges are shown in Table 1. Patients admitted during the second surge were older (73 years vs 71 years, P = .01) and had higher rates of hypertension (64.8% vs 59.6%, P = .003) and asthma (12.9% vs 10.7%, P = .049) but a lower rate of interstitial lung disease (3.3% vs 7.7%, P < .001). Sequential organ failure assessment scores at admission and the rates of other comorbidities were not significantly different between the 2 surges.

The number of patients who were hospitalized and received low-flow supplemental oxygen, and/or HFNC, and/or ventilator in the first surge and the second surge is shown in the Figure. Of all patients included, 2067 (64.9%) received low-flow supplemental oxygen; of these, 374 (18.1%) subsequently received HFNC, and 85 (22.7%) of these subsequently received mechanical ventilation. Of all 3183 patients, 417 (13.1%) received HFNC; 43 of these patients received HFNC without receiving low-flow supplemental oxygen, and 98 (23.5%) subsequently received mechanical ventilation. Out of all 3183 patients, 244 (7.7%) received mechanical ventilation; 98 (40.2%) of these received HFNC while the remaining 146 (59.8%) did not. At the beginning of the first surge, the ratio of patients who received invasive mechanical ventilation to patients who received HFNC was close to 1:1 (10/10); the ratio decreased to 6:10 in May and June 2020. At the beginning of the second surge, the ratio was 8:10 and then decreased to 3:10 in December 2020 and January 2021.

As shown in Table 2, the proportion of patients who received low-flow supplemental oxygen during the second surge was similar to that during the first surge (65.8% vs 64.1%, P = .3). Patients admitted during the second surge were more likely to receive HFNC than patients admitted during the first surge (15.4% vs 10.8%, P = .0001). Patients admitted during the second surge were less likely to be ventilated than the patients admitted during the first surge (5.6% vs 9.7%, P < .0001).

Clinical Outcomes

As shown in Table 3, second surge outcomes were much better than first surge outcomes: the ICU admission rate was lower (8.1% vs 12.7%, P < .0001); patients were more likely to be discharged to home (60.2% vs 47.4%, P < .0001), had a shorter length of hospital stay (5 vs 6 days, P < .0001), and had fewer ventilator days (10 vs 16, P = .01); and mortality was lower (8.3% vs 19.2%, P < .0001). There was a trend that length of ICU stay was shorter during the second surge than during the first surge (7 days vs 9 days, P = .09).

As noted (Figure), the ratio of patients who received invasive mechanical ventilation to patients who received HFNC was decreasing during both the first surge and the second surge. To further analyze the relation between ventilator and HFNC, we performed a subgroup analysis for 244 ventilated patients during both surges to compare outcomes between patients who received HFNC and those who did not receive HFNC (Table 4). Ninety-eight (40%) patients received HFNC. Ventilated patients who received HFNC had lower mortality than those patients who did not receive HFNC (31.6% vs 48%, P = .01), but had a longer length of hospital stay (29 days vs 14 days, P < .0001), longer length of ICU stay (17 days vs 9 days, P < .0001), and a higher number of ventilator days (16 vs 11, P = .001).

Our study compared the baseline patient characteristics; utilization of low-flow supplemental oxygen therapy, HFNC, and mechanical ventilation; and clinical outcomes between the first surge (n = 1586) and the second surge (n = 1597) of the COVID-19 pandemic. During both surges, about two-thirds of admitted patients received low-flow supplemental oxygen. A higher proportion of the admitted patients received HFNC during the second surge than during the first surge, while the intubation rate was lower during the second surge than during the first surge.

Reported low-flow supplemental oxygen use ranged from 28% to 63% depending on the cohort characteristics and location during the first surge.^6,7,19 A report from New York during the first surge (March 1 to April 4, 2020) showed that among 5700 hospitalized patients with COVID-19, 27.8% received low-flow supplemental oxygen.⁷ HFNC is recommended in guidelines on management of patients with acute respiratory failure due to COVID-19.²⁰ In our study, HFNC was utilized in a higher proportion of patients admitted for COVID-19 during the second surge (15.5% vs 10.8%, P = .0001). During the early pandemic period in Wuhan, China, 11% to 21% of admitted COVID-19 patients received HFNC.^21,22 Utilization of HFNC in New York during the first surge (March to May 2020) varied from 5% to 14.3% of patients admitted with COVID-19.^23,24 Our subgroup analysis of the ventilated patients showed that patients who received HFNC had lower mortality than those who did not (31.6% vs 48.0%, P = .011). Comparably, a report from Paris, France, showed that among patients admitted to ICUs for acute hypoxemic respiratory failure, those who received HFNC had lower mortality at day 60 than those who did not (21% vs 31%, P = .052).²⁵ Our recent analysis showed that patients treated with HFNC prior to mechanical ventilation had lower mortality than those treated with only conventional oxygen (30% vs 52%, P = .05).²⁶ In this subgroup analysis, we could not determine if HFNC treatment was administered before or after ventilation because HFNC was entered as dichotomous data (“Yes” or “No”) in the registry database. We merely showed the beneficial effect of HFNC on reducing mortality for ventilated COVID-19 patients, but did not mean to focus on how and when to apply HFNC.

We observed that the patients admitted during the second surge were less likely to be ventilated than the patients admitted during the first surge (5.6% vs 9.7%, P < .0001). During the first surge in New York, among 5700 patients admitted with COVID-19, 12.2% received invasive mechanical ventilation.⁷ In another report, also from New York during the first surge, 26.1% of 2015 hospitalized COVID-19 patients received mechanical ventilation.²⁷ In our study, the ventilation rate of 9.7% during the first surge was lower.

Outcomes during the second surge were better than during the first surge, including ICU admission rate, hospital and ICU length of stay, ventilator days, and mortality. The mortality was 19.2% during the first surge vs 8.3% during the second surge (P < .0001). The mortality of 19.2% was lower than the 30.6% mortality reported for 2015 hospitalized COVID-19 patients in New York during the first surge.²⁷ A retrospective study showed that early administration of remdesivir was associated with reduced ICU admission, ventilation use, and mortality.²⁸ The RECOVERY clinical trial showed that dexamethasone improved mortality for COVID-19 patients who received respiratory support, but not for patients who did not receive any respiratory support.²⁹ Perhaps some, if not all, of the improvement in ICU admission and mortality during the second surge was attributed to the new medications, such as antivirals and steroids.

The length of hospital stay for patients with moderate to severe COVID-19 varied from 4 to 53 days at different locations of the world, as shown in a meta-analysis by Rees and colleagues.³⁰ Our results showing a length of stay of 6 days during the first surge and 5 days during the second surge fell into the shorter end of this range. In a retrospective analysis of 1643 adults with severe COVID-19 admitted to hospitals in New York City between March 9, 2020 and April 23, 2020, median hospital length of stay was 7 (IQR, 3-14) days.³¹ For the ventilated patients in our study, the length of stay of 14 days (did not receive HFNC) and 29 days (received HFNC) was much longer. This longer length of stay might be attributed to the patients in our study being older and having more severe comorbidities.

The main purpose of this study was to compare the oxygen therapies and outcomes between 2 surges. It is difficult to associate the clinical outcomes with the oxygen therapies because new therapies and medications were available after the first surge. It was not possible to adjust the outcomes with confounders (other therapies and medications) because the registry data did not include the new therapies and medications.

A strength of this study was that we included a large, balanced number of patients in the first surge and the second surge. We did not plan the sample size in both groups as we could not predict the number of admissions. We set the end date of data collection for analysis as the time when the number of patients admitted during the second surge was similar to the number of patients admitted during the first surge. A limitation was that the registry database was created by the institution and was not designed solely for this study. The data for oxygen therapies were limited to low-flow supplemental oxygen, HFNC, and invasive mechanical ventilation; data for noninvasive ventilation were not included.

Conclusion

At our centers, during the second surge of COVID-19 pandemic, patients hospitalized with COVID-19 infection were more likely to receive HFNC but less likely to be ventilated. Compared to the first surge, the hospitalized patients with COVID-19 infection had a lower ICU admission rate, shorter length of hospital stay, fewer ventilator days, and lower mortality. For ventilated patients, those who received HFNC had lower mortality than those who did not.

Corresponding author: Timothy N. Liesching, MD, 41 Mall Road, Burlington, MA 01805; [email protected]

Disclosures: None reported.

doi:10.12788/jcom.0086

From Lahey Hospital and Medical Center, Burlington, MA (Drs. Liesching and Lei), and Tufts University School of Medicine, Boston, MA (Dr. Liesching)

ABSTRACT

Objective: To compare the utilization of oxygen therapies and clinical outcomes of patients admitted for COVID-19 during the second surge of the pandemic to that of patients admitted during the first surge.

Design: Observational study using a registry database.

Setting: Three hospitals (791 inpatient beds and 76 intensive care unit [ICU] beds) within the Beth Israel Lahey Health system in Massachusetts.

Participants: We included 3183 patients with COVID-19 admitted to hospitals.

Measurements: Baseline data included demographics and comorbidities. Treatments included low-flow supplemental oxygen (2-6 L/min), high-flow oxygen via nasal cannula, and invasive mechanical ventilation. Outcomes included ICU admission, length of stay, ventilator days, and mortality.

Results: A total of 3183 patients were included: 1586 during the first surge and 1597 during the second surge. Compared to the first surge, patients admitted during the second surge had a similar rate of receiving low-flow supplemental oxygen (65.8% vs 64.1%, P = .3), a higher rate of receiving high-flow nasal cannula (15.4% vs 10.8%, P = .0001), and a lower ventilation rate (5.6% vs 9.7%, P < .0001). The outcomes during the second surge were better than those during the first surge: lower ICU admission rate (8.1% vs 12.7%, P < .0001), shorter length of hospital stay (5 vs 6 days, P < .0001), fewer ventilator days (10 vs 16, P = .01), and lower mortality (8.3% vs 19.2%, P < .0001). Among ventilated patients, those who received high-flow nasal cannula had lower mortality.

Conclusion: Compared to the first surge of the COVID-19 pandemic, patients admitted during the second surge had similar likelihood of receiving low-flow supplemental oxygen, were more likely to receive high-flow nasal cannula, were less likely to be ventilated, and had better outcomes.

Keywords: supplemental oxygen, high-flow nasal cannula, ventilator.

The respiratory system receives the major impact of SARS-CoV-2 virus, and hypoxemia has been the predominant diagnosis for patients hospitalized with COVID-19.^1,2 During the initial stage of the pandemic, oxygen therapies and mechanical ventilation were the only choices for these patients.^3-6 Standard-of-care treatment for patients with COVID-19 during the initial surge included oxygen therapies and mechanical ventilation for hypoxemia and medications for comorbidities and COVID-19–associated sequelae, such as multi-organ dysfunction and failure. A report from New York during the first surge (May 2020) showed that among 5700 hospitalized patients with COVID-19, 27.8% received supplemental oxygen and 12.2% received invasive mechanical ventilation.⁷ High-flow nasal cannula (HFNC) oxygen delivery has been utilized widely throughout the pandemic due to its superiority over other noninvasive respiratory support techniques.^8-12 Mechanical ventilation is always necessary for critically ill patients with acute respiratory distress syndrome. However, ventilator scarcity has become a bottleneck in caring for severely ill patients with COVID-19 during the pandemic.¹³

The clinical outcomes of hospitalized COVID-19 patients include a high intubation rate, long length of hospital and intensive care unit (ICU) stay, and high mortality.^14,15 As the pandemic evolved, new medications, including remdesivir, hydroxychloroquine, lopinavir, or interferon β-1a, were used in addition to the standard of care, but these did not result in significantly different mortality from standard of care.¹⁶ Steroids are becoming foundational to the treatment of severe COVID-19 pneumonia, but evidence from high-quality randomized controlled clinical trials is lacking.¹⁷ 

During the first surge from March to May 2020, Massachusetts had the third highest number of COVID-19 cases among states in the United States.¹⁸ In early 2021, COVID-19 cases were climbing close to the peak of the second surge in Massachusetts. In this study, we compared utilization of low-flow supplemental oxygen, HFNC, and mechanical ventilation and clinical outcomes of patients admitted to 3 hospitals in Massachusetts during the second surge of the pandemic to that of patients admitted during the first surge.

Methods

Setting

Beth Israel Lahey Health is a system of academic and teaching hospitals with primary care and specialty care providers. We included 3 centers within the Beth Israel Lahey Health system in Massachusetts: Lahey Hospital and Medical Center, with 335 inpatient hospital beds and 52 critical care beds; Beverly Hospital, with 227 beds and 14 critical care beds; and Winchester Hospital, with 229 beds and 10 ICU beds.

Participants

We included patients admitted to the 3 hospitals with COVID-19 as a primary or secondary diagnosis during the first surge of the pandemic (March 1, 2020 to June 15, 2020) and the second surge (November 15, 2020 to January 27, 2021). The timeframe of the first surge was defined as the window between the start date and the end date of data collection. During the time window of the first surge, 1586 patients were included. The start time of the second surge was defined as the date when the data collection was restarted; the end date was set when the number of patients (1597) accumulated was close to the number of patients in the first surge (1586), so that the two groups had similar sample size.

Study Design

A data registry of COVID-19 patients was created by our institution, and the data were prospectively collected starting in March 2020. We retrospectively extracted data on the following from the registry database for this observational study: demographics and baseline comorbidities; the use of low-flow supplemental oxygen, HFNC, and invasive mechanical ventilator; and ICU admission, length of hospital stay, length of ICU stay, and hospital discharge disposition. Start and end times for each oxygen therapy were not entered in the registry. Data about other oxygen therapies, such as noninvasive positive-pressure ventilation, were not collected in the registry database, and therefore were not included in the analysis.

Statistical Analysis

Continuous variables (eg, age) were tested for data distribution normality using the Shapiro-Wilk test. Normally distributed data were tested using unpaired t-tests and displayed as mean (SD). The skewed data were tested using the Wilcoxon rank sum test and displayed as median (interquartile range [IQR]). The categorical variables were compared using chi-square test. Comparisons with P ≤ .05 were considered significantly different. Statistical analysis for this study was generated using Statistical Analysis Software (SAS), version 9.4 for Windows (SAS Institute Inc.).

Results

Baseline Characteristics

We included 3183 patients: 1586 admitted during the first surge and 1597 admitted during the second surge. Baseline characteristics of patients with COVID-19 admitted during the first and second surges are shown in Table 1. Patients admitted during the second surge were older (73 years vs 71 years, P = .01) and had higher rates of hypertension (64.8% vs 59.6%, P = .003) and asthma (12.9% vs 10.7%, P = .049) but a lower rate of interstitial lung disease (3.3% vs 7.7%, P < .001). Sequential organ failure assessment scores at admission and the rates of other comorbidities were not significantly different between the 2 surges.

The number of patients who were hospitalized and received low-flow supplemental oxygen, and/or HFNC, and/or ventilator in the first surge and the second surge is shown in the Figure. Of all patients included, 2067 (64.9%) received low-flow supplemental oxygen; of these, 374 (18.1%) subsequently received HFNC, and 85 (22.7%) of these subsequently received mechanical ventilation. Of all 3183 patients, 417 (13.1%) received HFNC; 43 of these patients received HFNC without receiving low-flow supplemental oxygen, and 98 (23.5%) subsequently received mechanical ventilation. Out of all 3183 patients, 244 (7.7%) received mechanical ventilation; 98 (40.2%) of these received HFNC while the remaining 146 (59.8%) did not. At the beginning of the first surge, the ratio of patients who received invasive mechanical ventilation to patients who received HFNC was close to 1:1 (10/10); the ratio decreased to 6:10 in May and June 2020. At the beginning of the second surge, the ratio was 8:10 and then decreased to 3:10 in December 2020 and January 2021.

As shown in Table 2, the proportion of patients who received low-flow supplemental oxygen during the second surge was similar to that during the first surge (65.8% vs 64.1%, P = .3). Patients admitted during the second surge were more likely to receive HFNC than patients admitted during the first surge (15.4% vs 10.8%, P = .0001). Patients admitted during the second surge were less likely to be ventilated than the patients admitted during the first surge (5.6% vs 9.7%, P < .0001).

Clinical Outcomes

As shown in Table 3, second surge outcomes were much better than first surge outcomes: the ICU admission rate was lower (8.1% vs 12.7%, P < .0001); patients were more likely to be discharged to home (60.2% vs 47.4%, P < .0001), had a shorter length of hospital stay (5 vs 6 days, P < .0001), and had fewer ventilator days (10 vs 16, P = .01); and mortality was lower (8.3% vs 19.2%, P < .0001). There was a trend that length of ICU stay was shorter during the second surge than during the first surge (7 days vs 9 days, P = .09).

As noted (Figure), the ratio of patients who received invasive mechanical ventilation to patients who received HFNC was decreasing during both the first surge and the second surge. To further analyze the relation between ventilator and HFNC, we performed a subgroup analysis for 244 ventilated patients during both surges to compare outcomes between patients who received HFNC and those who did not receive HFNC (Table 4). Ninety-eight (40%) patients received HFNC. Ventilated patients who received HFNC had lower mortality than those patients who did not receive HFNC (31.6% vs 48%, P = .01), but had a longer length of hospital stay (29 days vs 14 days, P < .0001), longer length of ICU stay (17 days vs 9 days, P < .0001), and a higher number of ventilator days (16 vs 11, P = .001).

Our study compared the baseline patient characteristics; utilization of low-flow supplemental oxygen therapy, HFNC, and mechanical ventilation; and clinical outcomes between the first surge (n = 1586) and the second surge (n = 1597) of the COVID-19 pandemic. During both surges, about two-thirds of admitted patients received low-flow supplemental oxygen. A higher proportion of the admitted patients received HFNC during the second surge than during the first surge, while the intubation rate was lower during the second surge than during the first surge.

Reported low-flow supplemental oxygen use ranged from 28% to 63% depending on the cohort characteristics and location during the first surge.^6,7,19 A report from New York during the first surge (March 1 to April 4, 2020) showed that among 5700 hospitalized patients with COVID-19, 27.8% received low-flow supplemental oxygen.⁷ HFNC is recommended in guidelines on management of patients with acute respiratory failure due to COVID-19.²⁰ In our study, HFNC was utilized in a higher proportion of patients admitted for COVID-19 during the second surge (15.5% vs 10.8%, P = .0001). During the early pandemic period in Wuhan, China, 11% to 21% of admitted COVID-19 patients received HFNC.^21,22 Utilization of HFNC in New York during the first surge (March to May 2020) varied from 5% to 14.3% of patients admitted with COVID-19.^23,24 Our subgroup analysis of the ventilated patients showed that patients who received HFNC had lower mortality than those who did not (31.6% vs 48.0%, P = .011). Comparably, a report from Paris, France, showed that among patients admitted to ICUs for acute hypoxemic respiratory failure, those who received HFNC had lower mortality at day 60 than those who did not (21% vs 31%, P = .052).²⁵ Our recent analysis showed that patients treated with HFNC prior to mechanical ventilation had lower mortality than those treated with only conventional oxygen (30% vs 52%, P = .05).²⁶ In this subgroup analysis, we could not determine if HFNC treatment was administered before or after ventilation because HFNC was entered as dichotomous data (“Yes” or “No”) in the registry database. We merely showed the beneficial effect of HFNC on reducing mortality for ventilated COVID-19 patients, but did not mean to focus on how and when to apply HFNC.

We observed that the patients admitted during the second surge were less likely to be ventilated than the patients admitted during the first surge (5.6% vs 9.7%, P < .0001). During the first surge in New York, among 5700 patients admitted with COVID-19, 12.2% received invasive mechanical ventilation.⁷ In another report, also from New York during the first surge, 26.1% of 2015 hospitalized COVID-19 patients received mechanical ventilation.²⁷ In our study, the ventilation rate of 9.7% during the first surge was lower.

Outcomes during the second surge were better than during the first surge, including ICU admission rate, hospital and ICU length of stay, ventilator days, and mortality. The mortality was 19.2% during the first surge vs 8.3% during the second surge (P < .0001). The mortality of 19.2% was lower than the 30.6% mortality reported for 2015 hospitalized COVID-19 patients in New York during the first surge.²⁷ A retrospective study showed that early administration of remdesivir was associated with reduced ICU admission, ventilation use, and mortality.²⁸ The RECOVERY clinical trial showed that dexamethasone improved mortality for COVID-19 patients who received respiratory support, but not for patients who did not receive any respiratory support.²⁹ Perhaps some, if not all, of the improvement in ICU admission and mortality during the second surge was attributed to the new medications, such as antivirals and steroids.

The length of hospital stay for patients with moderate to severe COVID-19 varied from 4 to 53 days at different locations of the world, as shown in a meta-analysis by Rees and colleagues.³⁰ Our results showing a length of stay of 6 days during the first surge and 5 days during the second surge fell into the shorter end of this range. In a retrospective analysis of 1643 adults with severe COVID-19 admitted to hospitals in New York City between March 9, 2020 and April 23, 2020, median hospital length of stay was 7 (IQR, 3-14) days.³¹ For the ventilated patients in our study, the length of stay of 14 days (did not receive HFNC) and 29 days (received HFNC) was much longer. This longer length of stay might be attributed to the patients in our study being older and having more severe comorbidities.

The main purpose of this study was to compare the oxygen therapies and outcomes between 2 surges. It is difficult to associate the clinical outcomes with the oxygen therapies because new therapies and medications were available after the first surge. It was not possible to adjust the outcomes with confounders (other therapies and medications) because the registry data did not include the new therapies and medications.

A strength of this study was that we included a large, balanced number of patients in the first surge and the second surge. We did not plan the sample size in both groups as we could not predict the number of admissions. We set the end date of data collection for analysis as the time when the number of patients admitted during the second surge was similar to the number of patients admitted during the first surge. A limitation was that the registry database was created by the institution and was not designed solely for this study. The data for oxygen therapies were limited to low-flow supplemental oxygen, HFNC, and invasive mechanical ventilation; data for noninvasive ventilation were not included.

Conclusion

At our centers, during the second surge of COVID-19 pandemic, patients hospitalized with COVID-19 infection were more likely to receive HFNC but less likely to be ventilated. Compared to the first surge, the hospitalized patients with COVID-19 infection had a lower ICU admission rate, shorter length of hospital stay, fewer ventilator days, and lower mortality. For ventilated patients, those who received HFNC had lower mortality than those who did not.

Corresponding author: Timothy N. Liesching, MD, 41 Mall Road, Burlington, MA 01805; [email protected]

Disclosures: None reported.

doi:10.12788/jcom.0086

From Lahey Hospital and Medical Center, Burlington, MA (Drs. Liesching and Lei), and Tufts University School of Medicine, Boston, MA (Dr. Liesching)

ABSTRACT

Objective: To compare the utilization of oxygen therapies and clinical outcomes of patients admitted for COVID-19 during the second surge of the pandemic to that of patients admitted during the first surge.

Design: Observational study using a registry database.

Setting: Three hospitals (791 inpatient beds and 76 intensive care unit [ICU] beds) within the Beth Israel Lahey Health system in Massachusetts.

Participants: We included 3183 patients with COVID-19 admitted to hospitals.

Measurements: Baseline data included demographics and comorbidities. Treatments included low-flow supplemental oxygen (2-6 L/min), high-flow oxygen via nasal cannula, and invasive mechanical ventilation. Outcomes included ICU admission, length of stay, ventilator days, and mortality.

Results: A total of 3183 patients were included: 1586 during the first surge and 1597 during the second surge. Compared to the first surge, patients admitted during the second surge had a similar rate of receiving low-flow supplemental oxygen (65.8% vs 64.1%, P = .3), a higher rate of receiving high-flow nasal cannula (15.4% vs 10.8%, P = .0001), and a lower ventilation rate (5.6% vs 9.7%, P < .0001). The outcomes during the second surge were better than those during the first surge: lower ICU admission rate (8.1% vs 12.7%, P < .0001), shorter length of hospital stay (5 vs 6 days, P < .0001), fewer ventilator days (10 vs 16, P = .01), and lower mortality (8.3% vs 19.2%, P < .0001). Among ventilated patients, those who received high-flow nasal cannula had lower mortality.

Conclusion: Compared to the first surge of the COVID-19 pandemic, patients admitted during the second surge had similar likelihood of receiving low-flow supplemental oxygen, were more likely to receive high-flow nasal cannula, were less likely to be ventilated, and had better outcomes.

Keywords: supplemental oxygen, high-flow nasal cannula, ventilator.

The respiratory system receives the major impact of SARS-CoV-2 virus, and hypoxemia has been the predominant diagnosis for patients hospitalized with COVID-19.^1,2 During the initial stage of the pandemic, oxygen therapies and mechanical ventilation were the only choices for these patients.^3-6 Standard-of-care treatment for patients with COVID-19 during the initial surge included oxygen therapies and mechanical ventilation for hypoxemia and medications for comorbidities and COVID-19–associated sequelae, such as multi-organ dysfunction and failure. A report from New York during the first surge (May 2020) showed that among 5700 hospitalized patients with COVID-19, 27.8% received supplemental oxygen and 12.2% received invasive mechanical ventilation.⁷ High-flow nasal cannula (HFNC) oxygen delivery has been utilized widely throughout the pandemic due to its superiority over other noninvasive respiratory support techniques.^8-12 Mechanical ventilation is always necessary for critically ill patients with acute respiratory distress syndrome. However, ventilator scarcity has become a bottleneck in caring for severely ill patients with COVID-19 during the pandemic.¹³

The clinical outcomes of hospitalized COVID-19 patients include a high intubation rate, long length of hospital and intensive care unit (ICU) stay, and high mortality.^14,15 As the pandemic evolved, new medications, including remdesivir, hydroxychloroquine, lopinavir, or interferon β-1a, were used in addition to the standard of care, but these did not result in significantly different mortality from standard of care.¹⁶ Steroids are becoming foundational to the treatment of severe COVID-19 pneumonia, but evidence from high-quality randomized controlled clinical trials is lacking.¹⁷ 

During the first surge from March to May 2020, Massachusetts had the third highest number of COVID-19 cases among states in the United States.¹⁸ In early 2021, COVID-19 cases were climbing close to the peak of the second surge in Massachusetts. In this study, we compared utilization of low-flow supplemental oxygen, HFNC, and mechanical ventilation and clinical outcomes of patients admitted to 3 hospitals in Massachusetts during the second surge of the pandemic to that of patients admitted during the first surge.

Methods

Setting

Beth Israel Lahey Health is a system of academic and teaching hospitals with primary care and specialty care providers. We included 3 centers within the Beth Israel Lahey Health system in Massachusetts: Lahey Hospital and Medical Center, with 335 inpatient hospital beds and 52 critical care beds; Beverly Hospital, with 227 beds and 14 critical care beds; and Winchester Hospital, with 229 beds and 10 ICU beds.

Participants

We included patients admitted to the 3 hospitals with COVID-19 as a primary or secondary diagnosis during the first surge of the pandemic (March 1, 2020 to June 15, 2020) and the second surge (November 15, 2020 to January 27, 2021). The timeframe of the first surge was defined as the window between the start date and the end date of data collection. During the time window of the first surge, 1586 patients were included. The start time of the second surge was defined as the date when the data collection was restarted; the end date was set when the number of patients (1597) accumulated was close to the number of patients in the first surge (1586), so that the two groups had similar sample size.

Study Design

A data registry of COVID-19 patients was created by our institution, and the data were prospectively collected starting in March 2020. We retrospectively extracted data on the following from the registry database for this observational study: demographics and baseline comorbidities; the use of low-flow supplemental oxygen, HFNC, and invasive mechanical ventilator; and ICU admission, length of hospital stay, length of ICU stay, and hospital discharge disposition. Start and end times for each oxygen therapy were not entered in the registry. Data about other oxygen therapies, such as noninvasive positive-pressure ventilation, were not collected in the registry database, and therefore were not included in the analysis.

Statistical Analysis

Continuous variables (eg, age) were tested for data distribution normality using the Shapiro-Wilk test. Normally distributed data were tested using unpaired t-tests and displayed as mean (SD). The skewed data were tested using the Wilcoxon rank sum test and displayed as median (interquartile range [IQR]). The categorical variables were compared using chi-square test. Comparisons with P ≤ .05 were considered significantly different. Statistical analysis for this study was generated using Statistical Analysis Software (SAS), version 9.4 for Windows (SAS Institute Inc.).

Results

Baseline Characteristics

We included 3183 patients: 1586 admitted during the first surge and 1597 admitted during the second surge. Baseline characteristics of patients with COVID-19 admitted during the first and second surges are shown in Table 1. Patients admitted during the second surge were older (73 years vs 71 years, P = .01) and had higher rates of hypertension (64.8% vs 59.6%, P = .003) and asthma (12.9% vs 10.7%, P = .049) but a lower rate of interstitial lung disease (3.3% vs 7.7%, P < .001). Sequential organ failure assessment scores at admission and the rates of other comorbidities were not significantly different between the 2 surges.

The number of patients who were hospitalized and received low-flow supplemental oxygen, and/or HFNC, and/or ventilator in the first surge and the second surge is shown in the Figure. Of all patients included, 2067 (64.9%) received low-flow supplemental oxygen; of these, 374 (18.1%) subsequently received HFNC, and 85 (22.7%) of these subsequently received mechanical ventilation. Of all 3183 patients, 417 (13.1%) received HFNC; 43 of these patients received HFNC without receiving low-flow supplemental oxygen, and 98 (23.5%) subsequently received mechanical ventilation. Out of all 3183 patients, 244 (7.7%) received mechanical ventilation; 98 (40.2%) of these received HFNC while the remaining 146 (59.8%) did not. At the beginning of the first surge, the ratio of patients who received invasive mechanical ventilation to patients who received HFNC was close to 1:1 (10/10); the ratio decreased to 6:10 in May and June 2020. At the beginning of the second surge, the ratio was 8:10 and then decreased to 3:10 in December 2020 and January 2021.

As shown in Table 2, the proportion of patients who received low-flow supplemental oxygen during the second surge was similar to that during the first surge (65.8% vs 64.1%, P = .3). Patients admitted during the second surge were more likely to receive HFNC than patients admitted during the first surge (15.4% vs 10.8%, P = .0001). Patients admitted during the second surge were less likely to be ventilated than the patients admitted during the first surge (5.6% vs 9.7%, P < .0001).

Clinical Outcomes

As shown in Table 3, second surge outcomes were much better than first surge outcomes: the ICU admission rate was lower (8.1% vs 12.7%, P < .0001); patients were more likely to be discharged to home (60.2% vs 47.4%, P < .0001), had a shorter length of hospital stay (5 vs 6 days, P < .0001), and had fewer ventilator days (10 vs 16, P = .01); and mortality was lower (8.3% vs 19.2%, P < .0001). There was a trend that length of ICU stay was shorter during the second surge than during the first surge (7 days vs 9 days, P = .09).

As noted (Figure), the ratio of patients who received invasive mechanical ventilation to patients who received HFNC was decreasing during both the first surge and the second surge. To further analyze the relation between ventilator and HFNC, we performed a subgroup analysis for 244 ventilated patients during both surges to compare outcomes between patients who received HFNC and those who did not receive HFNC (Table 4). Ninety-eight (40%) patients received HFNC. Ventilated patients who received HFNC had lower mortality than those patients who did not receive HFNC (31.6% vs 48%, P = .01), but had a longer length of hospital stay (29 days vs 14 days, P < .0001), longer length of ICU stay (17 days vs 9 days, P < .0001), and a higher number of ventilator days (16 vs 11, P = .001).

Our study compared the baseline patient characteristics; utilization of low-flow supplemental oxygen therapy, HFNC, and mechanical ventilation; and clinical outcomes between the first surge (n = 1586) and the second surge (n = 1597) of the COVID-19 pandemic. During both surges, about two-thirds of admitted patients received low-flow supplemental oxygen. A higher proportion of the admitted patients received HFNC during the second surge than during the first surge, while the intubation rate was lower during the second surge than during the first surge.

Reported low-flow supplemental oxygen use ranged from 28% to 63% depending on the cohort characteristics and location during the first surge.^6,7,19 A report from New York during the first surge (March 1 to April 4, 2020) showed that among 5700 hospitalized patients with COVID-19, 27.8% received low-flow supplemental oxygen.⁷ HFNC is recommended in guidelines on management of patients with acute respiratory failure due to COVID-19.²⁰ In our study, HFNC was utilized in a higher proportion of patients admitted for COVID-19 during the second surge (15.5% vs 10.8%, P = .0001). During the early pandemic period in Wuhan, China, 11% to 21% of admitted COVID-19 patients received HFNC.^21,22 Utilization of HFNC in New York during the first surge (March to May 2020) varied from 5% to 14.3% of patients admitted with COVID-19.^23,24 Our subgroup analysis of the ventilated patients showed that patients who received HFNC had lower mortality than those who did not (31.6% vs 48.0%, P = .011). Comparably, a report from Paris, France, showed that among patients admitted to ICUs for acute hypoxemic respiratory failure, those who received HFNC had lower mortality at day 60 than those who did not (21% vs 31%, P = .052).²⁵ Our recent analysis showed that patients treated with HFNC prior to mechanical ventilation had lower mortality than those treated with only conventional oxygen (30% vs 52%, P = .05).²⁶ In this subgroup analysis, we could not determine if HFNC treatment was administered before or after ventilation because HFNC was entered as dichotomous data (“Yes” or “No”) in the registry database. We merely showed the beneficial effect of HFNC on reducing mortality for ventilated COVID-19 patients, but did not mean to focus on how and when to apply HFNC.

We observed that the patients admitted during the second surge were less likely to be ventilated than the patients admitted during the first surge (5.6% vs 9.7%, P < .0001). During the first surge in New York, among 5700 patients admitted with COVID-19, 12.2% received invasive mechanical ventilation.⁷ In another report, also from New York during the first surge, 26.1% of 2015 hospitalized COVID-19 patients received mechanical ventilation.²⁷ In our study, the ventilation rate of 9.7% during the first surge was lower.

Outcomes during the second surge were better than during the first surge, including ICU admission rate, hospital and ICU length of stay, ventilator days, and mortality. The mortality was 19.2% during the first surge vs 8.3% during the second surge (P < .0001). The mortality of 19.2% was lower than the 30.6% mortality reported for 2015 hospitalized COVID-19 patients in New York during the first surge.²⁷ A retrospective study showed that early administration of remdesivir was associated with reduced ICU admission, ventilation use, and mortality.²⁸ The RECOVERY clinical trial showed that dexamethasone improved mortality for COVID-19 patients who received respiratory support, but not for patients who did not receive any respiratory support.²⁹ Perhaps some, if not all, of the improvement in ICU admission and mortality during the second surge was attributed to the new medications, such as antivirals and steroids.

The length of hospital stay for patients with moderate to severe COVID-19 varied from 4 to 53 days at different locations of the world, as shown in a meta-analysis by Rees and colleagues.³⁰ Our results showing a length of stay of 6 days during the first surge and 5 days during the second surge fell into the shorter end of this range. In a retrospective analysis of 1643 adults with severe COVID-19 admitted to hospitals in New York City between March 9, 2020 and April 23, 2020, median hospital length of stay was 7 (IQR, 3-14) days.³¹ For the ventilated patients in our study, the length of stay of 14 days (did not receive HFNC) and 29 days (received HFNC) was much longer. This longer length of stay might be attributed to the patients in our study being older and having more severe comorbidities.

The main purpose of this study was to compare the oxygen therapies and outcomes between 2 surges. It is difficult to associate the clinical outcomes with the oxygen therapies because new therapies and medications were available after the first surge. It was not possible to adjust the outcomes with confounders (other therapies and medications) because the registry data did not include the new therapies and medications.

A strength of this study was that we included a large, balanced number of patients in the first surge and the second surge. We did not plan the sample size in both groups as we could not predict the number of admissions. We set the end date of data collection for analysis as the time when the number of patients admitted during the second surge was similar to the number of patients admitted during the first surge. A limitation was that the registry database was created by the institution and was not designed solely for this study. The data for oxygen therapies were limited to low-flow supplemental oxygen, HFNC, and invasive mechanical ventilation; data for noninvasive ventilation were not included.

Conclusion

At our centers, during the second surge of COVID-19 pandemic, patients hospitalized with COVID-19 infection were more likely to receive HFNC but less likely to be ventilated. Compared to the first surge, the hospitalized patients with COVID-19 infection had a lower ICU admission rate, shorter length of hospital stay, fewer ventilator days, and lower mortality. For ventilated patients, those who received HFNC had lower mortality than those who did not.

Corresponding author: Timothy N. Liesching, MD, 41 Mall Road, Burlington, MA 01805; [email protected]

Disclosures: None reported.

doi:10.12788/jcom.0086

From Hassenfeld Children’s Hospital at NYU Langone Health, New York, NY (Dr Gallagher), T1D Exchange, Boston, MA (Saketh Rompicherla; Drs Ebekozien, Noor, Odugbesan, and Mungmode; Nicole Rioles, Emma Ospelt), University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien), Icahn School of Medicine at Mount Sinai, New York, NY (Drs. Wilkes, O’Malley, and Rapaport), Weill Cornell Medicine, New York, NY (Drs. Antal and Feuer), NYU Long Island School of Medicine, Mineola, NY (Dr. Gabriel), NYU Langone Health, New York, NY (Dr. Golden), Barbara Davis Center, Aurora, CO (Dr. Alonso), Texas Children’s Hospital/Baylor College of Medicine, Houston, TX (Dr. Lyons), Stanford University, Stanford, CA (Dr. Prahalad), Children Mercy Kansas City, MO (Dr. Clements), Indiana University School of Medicine, IN (Dr. Neyman), Rady Children’s Hospital, University of California, San Diego, CA (Dr. Demeterco-Berggren).

Background: Patient outcomes of COVID-19 have improved throughout the pandemic. However, because it is not known whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time, we investigated differences in COVID-19 outcomes for patients with T1D in the United States.

Methods: We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. We grouped cases into first surge (April 9, 2020, to July 31, 2020, n = 188) and late surge (August 1, 2020, to January 31, 2021, n = 410), and then compared outcomes between both groups using descriptive statistics and logistic regression models.

Results: Adverse outcomes were more frequent during the first surge, including diabetic ketoacidosis (32% vs 15%, P < .001), severe hypoglycemia (4% vs 1%, P = .04), and hospitalization (52% vs 22%, P < .001). Patients in the first surge were older (28 [SD,18.8] years vs 18.0 [SD, 11.1] years, P < .001), had higher median hemoglobin A1c levels (9.3 [interquartile range {IQR}, 4.0] vs 8.4 (IQR, 2.8), P < .001), and were more likely to use public insurance (107 [57%] vs 154 [38%], P < .001). The odds of hospitalization for adults in the first surge were 5 times higher compared to the late surge (odds ratio, 5.01; 95% CI, 2.11-12.63).

Conclusion: Patients with T1D who presented with COVID-19 during the first surge had a higher proportion of adverse outcomes than those who presented in a later surge.

Keywords: TD1, diabetic ketoacidosis, hypoglycemia.

After the World Health Organization declared the disease caused by the novel coronavirus SARS-CoV-2, COVID-19, a pandemic on March 11, 2020, the Centers for Disease Control and Prevention identified patients with diabetes as high risk for severe illness.^1-7 The case-fatality rate for COVID-19 has significantly improved over the past 2 years. Public health measures, less severe COVID-19 variants, increased access to testing, and new treatments for COVID-19 have contributed to improved outcomes.

The T1D Exchange has previously published findings on COVID-19 outcomes for patients with type 1 diabetes (T1D) using data from the T1D COVID-19 Surveillance Registry.^8-12 Given improved outcomes in COVID-19 in the general population, we sought to determine if outcomes for cases of COVID-19 reported to this registry changed over time.

Methods

This study was coordinated by the T1D Exchange and approved as nonhuman subject research by the Western Institutional Review Board. All participating centers also obtained local institutional review board approval. No identifiable patient information was collected as part of this noninterventional, cross-sectional study.

The T1D Exchange Multi-center COVID-19 Surveillance Study collected data from endocrinology clinics that completed a retrospective chart review and submitted information to T1D Exchange via an online questionnaire for all patients with T1D at their sites who tested positive for COVID-19.^13,14 The questionnaire was administered using the Qualtrics survey platform (www.qualtrics.com version XM) and contained 33 pre-coded and free-text response fields to collect patient and clinical attributes.

Each participating center identified 1 team member for reporting to avoid duplicate case submission. Each submitted case was reviewed for potential errors and incomplete information. The coordinating center verified the number of cases per site for data quality assurance.

Quantitative data were represented as mean (standard deviation) or median (interquartile range). Categorical data were described as the number (percentage) of patients. Summary statistics, including frequency and percentage for categorical variables, were calculated for all patient-related and clinical characteristics. The date August 1, 2021, was selected as the end of the first surge based on a review of national COVID-19 surges.

We used the Fisher’s exact test to assess associations between hospitalization and demographics, HbA1c, diabetes duration, symptoms, and adverse outcomes. In addition, multivariate logistic regression was used to calculate odds ratios (OR). Logistic regression models were used to determine the association between time of surge and hospitalization separately for both the pediatric and adult populations. Each model was adjusted for potential sociodemographic confounders, specifically age, sex, race, insurance, and HbA1c.

All tests were 2-sided, with type 1 error set at 5%. Fisher’s exact test and logistic regression were performed using statistical software R, version 3.6.2 (R Foundation for Statistical Computing).

Results

The characteristics of COVID-19 cases in patients with T1D that were reported early in the pandemic, before August 1, 2020 (first surge), compared with those of cases reported on and after August 1, 2020 (later surges) are shown in Table 1.

Patients with T1D who presented with COVID-19 during the first surge as compared to the later surges were older (mean age 28 [SD, 18.0] years vs 18.8 [SD, 11.1] years; P < .001) and had a longer duration of diabetes (P < .001). The first-surge group also had more patients with >20 years’ diabetes duration (20% vs 9%, P < .001). Obesity, hypertension, and chronic kidney disease were also more commonly reported in first-surge cases (all P < .001).

There was a significant difference in race and ethnicity reported in the first surge vs the later surge cases, with fewer patients identifying as non-Hispanic White (39% vs, 63%, P < .001) and more patients identifying as non-Hispanic Black (29% vs 12%, P < .001). The groups also differed significantly in terms of insurance type, with more people on public insurance in the first-surge group (57% vs 38%, P < .001). In addition, median HbA1c was higher (9.3% vs 8.4%, P < .001) and continuous glucose monitor and insulin pump use were less common (P = .02 and <.001, respectively) in the early surge.

All symptoms and adverse outcomes were reported more often in the first surge, including diabetic ketoacidosis (DKA; 32% vs 15%; P < .001) and severe hypoglycemia (4% vs 1%, P = .04). Hospitalization (52% vs 13%, P < .001) and ICU admission (24% vs 9%, P < .001) were reported more often in the first-surge group.

Regression Analyses

Table 2 shows the results of logistic regression analyses for hospitalization in the pediatric (≤19 years of age) and adult (>19 years of age) groups, along with the odds of hospitalization during the first vs late surge among COVID-positive people with T1D. Adult patients who tested positive in the first surge were about 5 times more likely to be hospitalized than adults who tested positive for infection in the late surge after adjusting for age, insurance type, sex, race, and HbA1c levels. Pediatric patients also had an increased odds for hospitalization during the first surge, but this increase was not statistically significant.

Discussion

Our analysis of COVID-19 cases in patients with T1D reported by diabetes providers across the United States found that adverse outcomes were more prevalent early in the pandemic. There may be a number of reasons for this difference in outcomes between patients who presented in the first surge vs a later surge. First, because testing for COVID-19 was extremely limited and reserved for hospitalized patients early in the pandemic, the first-surge patients with confirmed COVID-19 likely represent a skewed population of higher-acuity patients. This may also explain the relative paucity of cases in younger patients reported early in the pandemic. Second, worse outcomes in the early surge may also have been associated with overwhelmed hospitals in New York City at the start of the outbreak. According to Cummings et al, the abrupt surge of critically ill patients hospitalized with severe acute respiratory distress syndrome initially outpaced their capacity to provide prone-positioning ventilation, which has been expanded since then.¹⁵ While there was very little hypertension, cardiovascular disease, or kidney disease reported in the pediatric groups, there was a higher prevalence of obesity in the pediatric group from the mid-Atlantic region. Obesity has been associated with a worse prognosis for COVID-19 illness in children.¹⁶ Finally, there were 5 deaths reported in this study, all of which were reported during the first surge. Older age and increased rates of cardiovascular disease and chronic kidney disease in the first surge cases likely contributed to worse outcomes for adults in mid-Atlantic region relative to the other regions. Minority race and the use of public insurance, risk factors for more severe outcomes in all regions, were also more common in cases reported from the mid-Atlantic region.

This study has several limitations. First, it is a cross-sectional study that relies upon voluntary provider reports. Second, availability of COVID-19 testing was limited in all regions in spring 2020. Third, different regions of the country experienced subsequent surges at different times within the reported timeframes in this analysis. Fourth, this report time period does not include the impact of the newer COVID-19 variants. Finally, trends in COVID-19 outcomes were affected by the evolution of care that developed throughout 2020.

Conclusion

Adult patients with T1D and COVID-19 who reported during the first surge had about 5 times higher hospitalization odds than those who presented in a later surge.

Corresponding author: Osagie Ebekozien, MD, MPH, 11 Avenue de Lafayette, Boston, MA 02111; [email protected]

Disclosures: Dr Ebekozien reports receiving research grants from Medtronic Diabetes, Eli Lilly, and Dexcom, and receiving honoraria from Medtronic Diabetes.

From Hassenfeld Children’s Hospital at NYU Langone Health, New York, NY (Dr Gallagher), T1D Exchange, Boston, MA (Saketh Rompicherla; Drs Ebekozien, Noor, Odugbesan, and Mungmode; Nicole Rioles, Emma Ospelt), University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien), Icahn School of Medicine at Mount Sinai, New York, NY (Drs. Wilkes, O’Malley, and Rapaport), Weill Cornell Medicine, New York, NY (Drs. Antal and Feuer), NYU Long Island School of Medicine, Mineola, NY (Dr. Gabriel), NYU Langone Health, New York, NY (Dr. Golden), Barbara Davis Center, Aurora, CO (Dr. Alonso), Texas Children’s Hospital/Baylor College of Medicine, Houston, TX (Dr. Lyons), Stanford University, Stanford, CA (Dr. Prahalad), Children Mercy Kansas City, MO (Dr. Clements), Indiana University School of Medicine, IN (Dr. Neyman), Rady Children’s Hospital, University of California, San Diego, CA (Dr. Demeterco-Berggren).

Background: Patient outcomes of COVID-19 have improved throughout the pandemic. However, because it is not known whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time, we investigated differences in COVID-19 outcomes for patients with T1D in the United States.

Methods: We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. We grouped cases into first surge (April 9, 2020, to July 31, 2020, n = 188) and late surge (August 1, 2020, to January 31, 2021, n = 410), and then compared outcomes between both groups using descriptive statistics and logistic regression models.

Results: Adverse outcomes were more frequent during the first surge, including diabetic ketoacidosis (32% vs 15%, P < .001), severe hypoglycemia (4% vs 1%, P = .04), and hospitalization (52% vs 22%, P < .001). Patients in the first surge were older (28 [SD,18.8] years vs 18.0 [SD, 11.1] years, P < .001), had higher median hemoglobin A1c levels (9.3 [interquartile range {IQR}, 4.0] vs 8.4 (IQR, 2.8), P < .001), and were more likely to use public insurance (107 [57%] vs 154 [38%], P < .001). The odds of hospitalization for adults in the first surge were 5 times higher compared to the late surge (odds ratio, 5.01; 95% CI, 2.11-12.63).

Conclusion: Patients with T1D who presented with COVID-19 during the first surge had a higher proportion of adverse outcomes than those who presented in a later surge.

Keywords: TD1, diabetic ketoacidosis, hypoglycemia.

After the World Health Organization declared the disease caused by the novel coronavirus SARS-CoV-2, COVID-19, a pandemic on March 11, 2020, the Centers for Disease Control and Prevention identified patients with diabetes as high risk for severe illness.^1-7 The case-fatality rate for COVID-19 has significantly improved over the past 2 years. Public health measures, less severe COVID-19 variants, increased access to testing, and new treatments for COVID-19 have contributed to improved outcomes.

The T1D Exchange has previously published findings on COVID-19 outcomes for patients with type 1 diabetes (T1D) using data from the T1D COVID-19 Surveillance Registry.^8-12 Given improved outcomes in COVID-19 in the general population, we sought to determine if outcomes for cases of COVID-19 reported to this registry changed over time.

Methods

This study was coordinated by the T1D Exchange and approved as nonhuman subject research by the Western Institutional Review Board. All participating centers also obtained local institutional review board approval. No identifiable patient information was collected as part of this noninterventional, cross-sectional study.

The T1D Exchange Multi-center COVID-19 Surveillance Study collected data from endocrinology clinics that completed a retrospective chart review and submitted information to T1D Exchange via an online questionnaire for all patients with T1D at their sites who tested positive for COVID-19.^13,14 The questionnaire was administered using the Qualtrics survey platform (www.qualtrics.com version XM) and contained 33 pre-coded and free-text response fields to collect patient and clinical attributes.

Each participating center identified 1 team member for reporting to avoid duplicate case submission. Each submitted case was reviewed for potential errors and incomplete information. The coordinating center verified the number of cases per site for data quality assurance.

Quantitative data were represented as mean (standard deviation) or median (interquartile range). Categorical data were described as the number (percentage) of patients. Summary statistics, including frequency and percentage for categorical variables, were calculated for all patient-related and clinical characteristics. The date August 1, 2021, was selected as the end of the first surge based on a review of national COVID-19 surges.

We used the Fisher’s exact test to assess associations between hospitalization and demographics, HbA1c, diabetes duration, symptoms, and adverse outcomes. In addition, multivariate logistic regression was used to calculate odds ratios (OR). Logistic regression models were used to determine the association between time of surge and hospitalization separately for both the pediatric and adult populations. Each model was adjusted for potential sociodemographic confounders, specifically age, sex, race, insurance, and HbA1c.

All tests were 2-sided, with type 1 error set at 5%. Fisher’s exact test and logistic regression were performed using statistical software R, version 3.6.2 (R Foundation for Statistical Computing).

Results

The characteristics of COVID-19 cases in patients with T1D that were reported early in the pandemic, before August 1, 2020 (first surge), compared with those of cases reported on and after August 1, 2020 (later surges) are shown in Table 1.

Patients with T1D who presented with COVID-19 during the first surge as compared to the later surges were older (mean age 28 [SD, 18.0] years vs 18.8 [SD, 11.1] years; P < .001) and had a longer duration of diabetes (P < .001). The first-surge group also had more patients with >20 years’ diabetes duration (20% vs 9%, P < .001). Obesity, hypertension, and chronic kidney disease were also more commonly reported in first-surge cases (all P < .001).

There was a significant difference in race and ethnicity reported in the first surge vs the later surge cases, with fewer patients identifying as non-Hispanic White (39% vs, 63%, P < .001) and more patients identifying as non-Hispanic Black (29% vs 12%, P < .001). The groups also differed significantly in terms of insurance type, with more people on public insurance in the first-surge group (57% vs 38%, P < .001). In addition, median HbA1c was higher (9.3% vs 8.4%, P < .001) and continuous glucose monitor and insulin pump use were less common (P = .02 and <.001, respectively) in the early surge.

All symptoms and adverse outcomes were reported more often in the first surge, including diabetic ketoacidosis (DKA; 32% vs 15%; P < .001) and severe hypoglycemia (4% vs 1%, P = .04). Hospitalization (52% vs 13%, P < .001) and ICU admission (24% vs 9%, P < .001) were reported more often in the first-surge group.

Regression Analyses

Table 2 shows the results of logistic regression analyses for hospitalization in the pediatric (≤19 years of age) and adult (>19 years of age) groups, along with the odds of hospitalization during the first vs late surge among COVID-positive people with T1D. Adult patients who tested positive in the first surge were about 5 times more likely to be hospitalized than adults who tested positive for infection in the late surge after adjusting for age, insurance type, sex, race, and HbA1c levels. Pediatric patients also had an increased odds for hospitalization during the first surge, but this increase was not statistically significant.

Discussion

Our analysis of COVID-19 cases in patients with T1D reported by diabetes providers across the United States found that adverse outcomes were more prevalent early in the pandemic. There may be a number of reasons for this difference in outcomes between patients who presented in the first surge vs a later surge. First, because testing for COVID-19 was extremely limited and reserved for hospitalized patients early in the pandemic, the first-surge patients with confirmed COVID-19 likely represent a skewed population of higher-acuity patients. This may also explain the relative paucity of cases in younger patients reported early in the pandemic. Second, worse outcomes in the early surge may also have been associated with overwhelmed hospitals in New York City at the start of the outbreak. According to Cummings et al, the abrupt surge of critically ill patients hospitalized with severe acute respiratory distress syndrome initially outpaced their capacity to provide prone-positioning ventilation, which has been expanded since then.¹⁵ While there was very little hypertension, cardiovascular disease, or kidney disease reported in the pediatric groups, there was a higher prevalence of obesity in the pediatric group from the mid-Atlantic region. Obesity has been associated with a worse prognosis for COVID-19 illness in children.¹⁶ Finally, there were 5 deaths reported in this study, all of which were reported during the first surge. Older age and increased rates of cardiovascular disease and chronic kidney disease in the first surge cases likely contributed to worse outcomes for adults in mid-Atlantic region relative to the other regions. Minority race and the use of public insurance, risk factors for more severe outcomes in all regions, were also more common in cases reported from the mid-Atlantic region.

This study has several limitations. First, it is a cross-sectional study that relies upon voluntary provider reports. Second, availability of COVID-19 testing was limited in all regions in spring 2020. Third, different regions of the country experienced subsequent surges at different times within the reported timeframes in this analysis. Fourth, this report time period does not include the impact of the newer COVID-19 variants. Finally, trends in COVID-19 outcomes were affected by the evolution of care that developed throughout 2020.

Conclusion

Adult patients with T1D and COVID-19 who reported during the first surge had about 5 times higher hospitalization odds than those who presented in a later surge.

Corresponding author: Osagie Ebekozien, MD, MPH, 11 Avenue de Lafayette, Boston, MA 02111; [email protected]

Disclosures: Dr Ebekozien reports receiving research grants from Medtronic Diabetes, Eli Lilly, and Dexcom, and receiving honoraria from Medtronic Diabetes.

From Hassenfeld Children’s Hospital at NYU Langone Health, New York, NY (Dr Gallagher), T1D Exchange, Boston, MA (Saketh Rompicherla; Drs Ebekozien, Noor, Odugbesan, and Mungmode; Nicole Rioles, Emma Ospelt), University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien), Icahn School of Medicine at Mount Sinai, New York, NY (Drs. Wilkes, O’Malley, and Rapaport), Weill Cornell Medicine, New York, NY (Drs. Antal and Feuer), NYU Long Island School of Medicine, Mineola, NY (Dr. Gabriel), NYU Langone Health, New York, NY (Dr. Golden), Barbara Davis Center, Aurora, CO (Dr. Alonso), Texas Children’s Hospital/Baylor College of Medicine, Houston, TX (Dr. Lyons), Stanford University, Stanford, CA (Dr. Prahalad), Children Mercy Kansas City, MO (Dr. Clements), Indiana University School of Medicine, IN (Dr. Neyman), Rady Children’s Hospital, University of California, San Diego, CA (Dr. Demeterco-Berggren).

Background: Patient outcomes of COVID-19 have improved throughout the pandemic. However, because it is not known whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time, we investigated differences in COVID-19 outcomes for patients with T1D in the United States.

Methods: We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. We grouped cases into first surge (April 9, 2020, to July 31, 2020, n = 188) and late surge (August 1, 2020, to January 31, 2021, n = 410), and then compared outcomes between both groups using descriptive statistics and logistic regression models.

Results: Adverse outcomes were more frequent during the first surge, including diabetic ketoacidosis (32% vs 15%, P < .001), severe hypoglycemia (4% vs 1%, P = .04), and hospitalization (52% vs 22%, P < .001). Patients in the first surge were older (28 [SD,18.8] years vs 18.0 [SD, 11.1] years, P < .001), had higher median hemoglobin A1c levels (9.3 [interquartile range {IQR}, 4.0] vs 8.4 (IQR, 2.8), P < .001), and were more likely to use public insurance (107 [57%] vs 154 [38%], P < .001). The odds of hospitalization for adults in the first surge were 5 times higher compared to the late surge (odds ratio, 5.01; 95% CI, 2.11-12.63).

Conclusion: Patients with T1D who presented with COVID-19 during the first surge had a higher proportion of adverse outcomes than those who presented in a later surge.

Keywords: TD1, diabetic ketoacidosis, hypoglycemia.

After the World Health Organization declared the disease caused by the novel coronavirus SARS-CoV-2, COVID-19, a pandemic on March 11, 2020, the Centers for Disease Control and Prevention identified patients with diabetes as high risk for severe illness.^1-7 The case-fatality rate for COVID-19 has significantly improved over the past 2 years. Public health measures, less severe COVID-19 variants, increased access to testing, and new treatments for COVID-19 have contributed to improved outcomes.

The T1D Exchange has previously published findings on COVID-19 outcomes for patients with type 1 diabetes (T1D) using data from the T1D COVID-19 Surveillance Registry.^8-12 Given improved outcomes in COVID-19 in the general population, we sought to determine if outcomes for cases of COVID-19 reported to this registry changed over time.

Methods

This study was coordinated by the T1D Exchange and approved as nonhuman subject research by the Western Institutional Review Board. All participating centers also obtained local institutional review board approval. No identifiable patient information was collected as part of this noninterventional, cross-sectional study.

The T1D Exchange Multi-center COVID-19 Surveillance Study collected data from endocrinology clinics that completed a retrospective chart review and submitted information to T1D Exchange via an online questionnaire for all patients with T1D at their sites who tested positive for COVID-19.^13,14 The questionnaire was administered using the Qualtrics survey platform (www.qualtrics.com version XM) and contained 33 pre-coded and free-text response fields to collect patient and clinical attributes.

Each participating center identified 1 team member for reporting to avoid duplicate case submission. Each submitted case was reviewed for potential errors and incomplete information. The coordinating center verified the number of cases per site for data quality assurance.

Quantitative data were represented as mean (standard deviation) or median (interquartile range). Categorical data were described as the number (percentage) of patients. Summary statistics, including frequency and percentage for categorical variables, were calculated for all patient-related and clinical characteristics. The date August 1, 2021, was selected as the end of the first surge based on a review of national COVID-19 surges.

We used the Fisher’s exact test to assess associations between hospitalization and demographics, HbA1c, diabetes duration, symptoms, and adverse outcomes. In addition, multivariate logistic regression was used to calculate odds ratios (OR). Logistic regression models were used to determine the association between time of surge and hospitalization separately for both the pediatric and adult populations. Each model was adjusted for potential sociodemographic confounders, specifically age, sex, race, insurance, and HbA1c.

All tests were 2-sided, with type 1 error set at 5%. Fisher’s exact test and logistic regression were performed using statistical software R, version 3.6.2 (R Foundation for Statistical Computing).

Results

The characteristics of COVID-19 cases in patients with T1D that were reported early in the pandemic, before August 1, 2020 (first surge), compared with those of cases reported on and after August 1, 2020 (later surges) are shown in Table 1.

Patients with T1D who presented with COVID-19 during the first surge as compared to the later surges were older (mean age 28 [SD, 18.0] years vs 18.8 [SD, 11.1] years; P < .001) and had a longer duration of diabetes (P < .001). The first-surge group also had more patients with >20 years’ diabetes duration (20% vs 9%, P < .001). Obesity, hypertension, and chronic kidney disease were also more commonly reported in first-surge cases (all P < .001).

There was a significant difference in race and ethnicity reported in the first surge vs the later surge cases, with fewer patients identifying as non-Hispanic White (39% vs, 63%, P < .001) and more patients identifying as non-Hispanic Black (29% vs 12%, P < .001). The groups also differed significantly in terms of insurance type, with more people on public insurance in the first-surge group (57% vs 38%, P < .001). In addition, median HbA1c was higher (9.3% vs 8.4%, P < .001) and continuous glucose monitor and insulin pump use were less common (P = .02 and <.001, respectively) in the early surge.

All symptoms and adverse outcomes were reported more often in the first surge, including diabetic ketoacidosis (DKA; 32% vs 15%; P < .001) and severe hypoglycemia (4% vs 1%, P = .04). Hospitalization (52% vs 13%, P < .001) and ICU admission (24% vs 9%, P < .001) were reported more often in the first-surge group.

Regression Analyses

Table 2 shows the results of logistic regression analyses for hospitalization in the pediatric (≤19 years of age) and adult (>19 years of age) groups, along with the odds of hospitalization during the first vs late surge among COVID-positive people with T1D. Adult patients who tested positive in the first surge were about 5 times more likely to be hospitalized than adults who tested positive for infection in the late surge after adjusting for age, insurance type, sex, race, and HbA1c levels. Pediatric patients also had an increased odds for hospitalization during the first surge, but this increase was not statistically significant.

Discussion

Our analysis of COVID-19 cases in patients with T1D reported by diabetes providers across the United States found that adverse outcomes were more prevalent early in the pandemic. There may be a number of reasons for this difference in outcomes between patients who presented in the first surge vs a later surge. First, because testing for COVID-19 was extremely limited and reserved for hospitalized patients early in the pandemic, the first-surge patients with confirmed COVID-19 likely represent a skewed population of higher-acuity patients. This may also explain the relative paucity of cases in younger patients reported early in the pandemic. Second, worse outcomes in the early surge may also have been associated with overwhelmed hospitals in New York City at the start of the outbreak. According to Cummings et al, the abrupt surge of critically ill patients hospitalized with severe acute respiratory distress syndrome initially outpaced their capacity to provide prone-positioning ventilation, which has been expanded since then.¹⁵ While there was very little hypertension, cardiovascular disease, or kidney disease reported in the pediatric groups, there was a higher prevalence of obesity in the pediatric group from the mid-Atlantic region. Obesity has been associated with a worse prognosis for COVID-19 illness in children.¹⁶ Finally, there were 5 deaths reported in this study, all of which were reported during the first surge. Older age and increased rates of cardiovascular disease and chronic kidney disease in the first surge cases likely contributed to worse outcomes for adults in mid-Atlantic region relative to the other regions. Minority race and the use of public insurance, risk factors for more severe outcomes in all regions, were also more common in cases reported from the mid-Atlantic region.

This study has several limitations. First, it is a cross-sectional study that relies upon voluntary provider reports. Second, availability of COVID-19 testing was limited in all regions in spring 2020. Third, different regions of the country experienced subsequent surges at different times within the reported timeframes in this analysis. Fourth, this report time period does not include the impact of the newer COVID-19 variants. Finally, trends in COVID-19 outcomes were affected by the evolution of care that developed throughout 2020.

Conclusion

Adult patients with T1D and COVID-19 who reported during the first surge had about 5 times higher hospitalization odds than those who presented in a later surge.

Corresponding author: Osagie Ebekozien, MD, MPH, 11 Avenue de Lafayette, Boston, MA 02111; [email protected]

Disclosures: Dr Ebekozien reports receiving research grants from Medtronic Diabetes, Eli Lilly, and Dexcom, and receiving honoraria from Medtronic Diabetes.

From the Department of Emergency Medicine, Santa Croce e Carle Hospital, Cuneo, Italy (Drs. Abram, Tosello, Emanuele Bernardi, Allione, Cavalot, Dutto, Corsini, Martini, Sciolla, Sara Bernardi, and Lauria). From the School of Emergency Medicine, University of Turin, Turin, Italy (Drs. Paglietta and Giamello).

Objective: This retrospective and prospective cohort study was designed to describe the characteristics, treatments, and outcomes of patients with SARS-CoV-2 infection (COVID-19) admitted to subintensive care units (SICU) and to identify the variables associated with outcomes. SICUs have been extremely stressed during the pandemic, but most data regarding critically ill COVID-19 patients come from intensive care units (ICUs). Studies about COVID-19 patients in SICUs are lacking.

Setting and participants: The study included 88 COVID-19 patients admitted to our SICU in Cuneo, Italy, between March and May 2020.

Measurements: Clinical and ventilatory data were collected, and patients were divided by outcome. Multivariable logistic regression analysis examined the variables associated with negative outcomes (transfer to the ICU, palliation, or death in a SICU).

Results: A total of 60 patients (68%) had a positive outcome, and 28 patients (32%) had a negative outcome; 69 patients (78%) underwent continuous positive airway pressure (CPAP). Pronation (n = 37 [42%]) had been more frequently adopted in patients who had a positive outcome vs a negative outcome (n = 30 [50%] vs n = 7 [25%]; P = .048), and the median (interquartile range) Pao₂/Fio₂ ratio after 6 hours of prone positioning was lower in patients who had a negative outcome vs a positive outcome (144 [140-168] vs 249 [195-268], P = .006). Independent predictors of a negative outcome were diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P = .015), higher D-dimer (OR, 1.28; 95% CI, 1.04-1.57; P = .019), higher lactate dehydrogenase level (OR, 1.003; 95% CI, 1.000-1.006; P = .039), and lower lymphocytes count (OR, 0.996; 95% CI, 0.993-0.999; P = .004).

Conclusion: SICUs have a fundamental role in the treatment of critically ill patients with COVID-19, who require long-term CPAP and pronation cycles. Diabetes, lymphopenia, and high D-dimer and LDH levels are associated with negative outcomes.

Keywords: emergency medicine, noninvasive ventilation, prone position, continuous positive airway pressure.

The COVID-19 pandemic has led to large increases in hospital admissions. Subintensive care units (SICUs) are among the wards most under pressure worldwide,¹ dealing with the increased number of critically ill patients who need noninvasive ventilation, as well as serving as the best alternative to overfilled intensive care units (ICUs). In Italy, SICUs are playing a fundamental role in the management of COVID-19 patients, providing early treatment of respiratory failure by continuous noninvasive ventilation in order to reduce the need for intubation.^2-5 Nevertheless, the great majority of available data about critically ill COVID-19 patients comes from ICUs. Full studies about outcomes of patients in SICUs are lacking and need to be conducted.

We sought to evaluate the characteristics and outcomes of patients admitted to our SICU for COVID-19 to describe the treatments they needed and their impact on prognosis, and to identify the variables associated with patient outcomes.

Methods

Study Design

This cohort study used data from patients who were admitted in the very first weeks of the pandemic. Data were collected retrospectively as well as prospectively, since the ethical committee approved our project. The quality and quantity of data in the 2 groups were comparable.

Data were collected from electronic and written medical records gathered during the patient’s entire stay in our SICU. Data were entered in a database with limited and controlled access. This study complied with the Declaration of Helsinki and was approved by the local ethics committees (ID: MEDURG10).

Study Population

We studied 88 consecutive patients admitted to the SICU of the Santa Croce e Carle Teaching Hospital, Cuneo, Italy, for COVID-19, from March 8 to May 1, 2020. The diagnosis was based on acute respiratory failure associated with SARS-CoV-2 RNA detection on nasopharyngeal swab or tracheal aspirate and/or typical COVID-19 features on a pulmonary computed tomography (CT) scan.⁶ Exclusion criteria were age younger than 18 years and patient denial of permission to use their data for research purposes (the great majority of patients could actively give consent; for patients who were too sick to do so, family members were asked whether they were aware of any reason why the patient would deny consent).

The past medical history and recent symptoms description were obtained by manually reviewing medical records. Epidemiological exposure was defined as contact with SARS-CoV-2–positive people or staying in an epidemic outbreak area. Initial vital parameters, venous blood tests, arterial blood gas analysis, chest x-ray, as well as the result of the nasopharyngeal swab were gathered from the emergency department (ED) examination. (Additional swabs could be requested when the first one was negative but clinical suspicion for COVID-19 was high.) Upon admission to the SICU, a standardized panel of blood tests was performed, which was repeated the next day and then every 48 hours. Arterial blood gas analysis was performed when clinically indicated, at least twice a day, or following a scheduled time in patients undergoing pronation. Charlson Comorbidity Index⁷ and MuLBSTA score⁸ were calculated based on the collected data.

Imaging

Chest ultrasonography was performed in the ED at the time of hospitalization and once a day in the SICU. Pulmonary high-resolution computed tomography (HRCT) was performed when clinically indicated or when the results of nasopharyngeal swabs and/or x-ray results were discordant with COVID-19 clinical suspicion. Contrast CT was performed when pulmonary embolism was suspected.

Medical Therapy

Hydroxychloroquine, antiviral agents, tocilizumab, and ruxolitinib were used in the early phase of the pandemic, then were dismissed after evidence of no efficacy.^9-11 Steroids and low-molecular-weight heparin were used afterward. Enoxaparin was used at the standard prophylactic dosage, and 70% of the anticoagulant dosage was also adopted in patients with moderate-to-severe COVID-19 and D-dimer values >3 times the normal value.^12-14 Antibiotics were given when a bacterial superinfection was suspected.

Oxygen and Ventilatory Therapy

Oxygen support or noninvasive ventilation were started based on patients’ respiratory efficacy, estimated by respiratory rate and the ratio of partial pressure of arterial oxygen and fraction of inspired oxygen (P/F ratio).^15,16 Oxygen support was delivered through nasal cannula, Venturi mask, or reservoir mask. Noninvasive ventilation was performed by continuous positive airway pressure (CPAP) when the P/F ratio was <250 or the respiratory rate was >25 breaths per minute, using the helmet interface.^5,17 Prone positioning during CPAP^18-20 was adopted in patients meeting the acute respiratory distress syndrome (ARDS) criteria²¹ and having persistence of respiratory distress and P/F <300 after a 1-hour trial of CPAP.

The prone position was maintained based on patient tolerance. P/F ratio was measured before pronation (T0), after 1 hour of prone position (T1), before resupination (T2), and 6 hours after resupination (T3). With the same timing, the patient was asked to rate their comfort in each position, from 0 (lack of comfort) to 10 (optimal comfort). Delta P/F was defined as the difference between P/F at T3 and basal P/F at T0.

Outcomes

Positive outcomes were defined as patient discharge from the SICU or transfer to a lower-intensity care ward for treatment continuation. Negative outcomes were defined as need for transfer to the ICU, transfer to another ward for palliation, or death in the SICU.

Statistical Analysis

Continuous data are reported as median and interquartile range (IQR); normal distribution of variables was tested using the Shapiro-Wilk test. Categorical variables were reported as absolute number and percentage. The Mann-Whitney test was used to compare continuous variables between groups, and chi-square test with continuity correction was used for categorical variables. The variables that were most significantly associated with a negative outcome on the univariate analysis were included in a stepwise logistic regression analysis, in order to identify independent predictors of patient outcome. Statistical analysis was performed using JASP (JASP Team) software.

Study Population

Of the 88 patients included in the study, 70% were male; the median age was 66 years (IQR, 60-77). In most patients, the diagnosis of COVID-19 was derived from a positive SARS-CoV-2 nasopharyngeal swab. Six patients, however, maintained a negative swab at all determinations but had clinical and imaging features strongly suggesting COVID-19. No patients met the exclusion criteria. Most patients came from the ED (n = 58 [66%]) or general wards (n = 22 [25%]), while few were transferred from the ICU (n = 8 [9%]). The median length of stay in the SICU was 4 days (IQR, 2-7). An epidemiological link to affected persons or a known virus exposure was identifiable in 37 patients (42%).

Clinical, Laboratory, and Imaging Data

The clinical and anthropometric characteristics of patients are shown in Table 1. Hypertension and smoking habits were prevalent in our population, and the median Charlson Comorbidity Index was 3. Most patients experienced fever, dyspnea, and cough during the days before hospitalization.

Laboratory data showed a marked inflammatory milieu in all studied patients, both at baseline and after 24 and 72 hours. Lymphopenia was observed, along with a significant increase of lactate dehydrogenase (LDH), C-reactive protein (CPR), and D-dimer, and a mild increase of procalcitonin. N-terminal pro-brain natriuretic peptide (NT-proBNP) values were also increased, with normal troponin I values (Table 2).

Medical Therapy

Most patients (89%) received hydroxychloroquine, whereas steroids were used in one-third of the population (36%). Immunomodulators (tocilizumab and ruxolitinib) were restricted to 12 patients (14%). Empirical antiviral therapy was introduced in the first 41 patients (47%). Enoxaparin was the default agent for thromboembolism prophylaxis, and 6 patients (7%) received 70% of the anticoagulating dose.

Oxygen and Ventilatory Therapy

Basal median P/F ratio was 253 (IQR, 218-291), and respiratory rate at triage was 20 breaths/min (IQR, 16-28), underlining a moderate-to-severe respiratory insufficiency at presentation. A total of 69 patients (78%) underwent CPAP, with a median positive end-expiratory pressure (PEEP) of 10.0 cm H₂O (IQR, 7.5-10.0) and fraction of inspired oxygen (Fio₂) of 0.40 (IQR, 0.40-0.50). In 37 patients (42%) who received ongoing CPAP, prone positioning was adopted. In this subgroup, respiratory rate was not significantly different from baseline to resupination (24 vs 25 breaths/min). The median P/F improved from 197 (IQR, 154-236) at baseline to 217 (IQR, 180-262) after pronation (the duration of the prone position was variable, depending on patients’ tolerance: 1 to 6 hours or every pronation cycle). The median delta P/F ratio was 39.4 (IQR, –17.0 to 78.0).

Outcomes

A total of 28 patients (32%) had a negative outcome in the SICU: 8 patients (9%) died, having no clinical indication for higher-intensity care; 6 patients (7%) were transferred to general wards for palliation; and 14 patients (16%) needed an upgrade of cure intensity and were transferred to the ICU. Of these 14 patients, 9 died in the ICU. The total in-hospital mortality of COVID-19 patients, including patients transferred from the SICU to general wards in fair condition, was 27% (n = 24). Clinical, laboratory, and therapeutic characteristics between the 2 groups are shown in Table 4.

Patients who had a negative outcome were significantly older and had more comorbidities, as suggested by a significantly higher prevalence of diabetes and higher Charlson Comorbidity scores (reflecting the mortality risk based on age and comorbidities). The median MuLBSTA score, which estimates the 90-day mortality risk from viral pneumonia, was also higher in patients who had a negative outcome (9.33%). Symptom occurrence was not different in patients with a negative outcome (apart from cough, which was less frequent), but these patients underwent hospitalization earlier—since the appearance of their first COVID-19 symptoms—compared to patients who had a positive outcome. No difference was found in antihypertensive therapy with angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers among outcome groups.

More pronounced laboratory abnormalities were found in patients who had a negative outcome, compared to patients who had a positive outcome: lower lymphocytes and higher C-reactive protein (CRP), procalcitonin, D-dimer, LDH, and NT-proBNP. We found no differences in the radiological distribution of pulmonary involvement in patients who had negative or positive outcomes, nor in the adopted medical treatment.

Data showed no difference in CPAP implementation in the 2 groups. However, prone positioning had been more frequently adopted in the group of patients who had a positive outcome, compared with patients who had a negative outcome. No differences of basal P/F were found in patients who had a negative or positive outcome, but the median P/F after 6 hours of prone position was significantly lower in patients who had a negative outcome. The delta P/F ratio did not differ in the 2 groups of patients.

Multivariate Analysis

A logistic regression model was created, including the variables significantly associated with outcomes in the univariate analysis (age, sex, history of diabetes, lymphocytes, CRP, procalcitonin, LDH, NT-proBNP, and D-dimer). In the multivariate analysis, independent predictors of a negative outcome were history of diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P =.015), high D-dimer values (OR, 1.28; CI, 1.04-1.57; P = .019), high LDH values (OR, 1.003; CI, 1.000-1.006; P = .039), and low lymphocytes count (OR, 0.996; CI, 0.993-0.999; P = .004).

Discussion

Role of Subintensive Units and Mortality

The novelty of our report is its attempt to investigate the specific group of COVID-19 patients admitted to a SICU. In Italy, SICUs receive acutely ill, spontaneously breathing patients who need (invasive) hemodynamic monitoring, vasoactive medication, renal replacement therapy, chest- tube placement, thrombolysis, and respiratory noninvasive support. The nurse-to-patient ratio is higher than for general wards (usually 1 nurse to every 4 or 5 patients), though lower than for ICUs. In northern Italy, a great number of COVID-19 patients have required this kind of high-intensity care during the pandemic: Noninvasive ventilation support had to be maintained for several days, pronation maneuvers required a high number of people 2 or 3 times a day, and strict monitoring had to be assured. The SICU setting allows patients to buy time as a bridge to progressive reduction of pulmonary involvement, sometimes preventing the need for intubation.

The high prevalence of negative outcomes in the SICU underlines the complexity of COVID-19 patients in this setting. In fact, published data about mortality for patients with severe COVID-19 pneumonia are similar to ours.^22,23

Clinical, Laboratory, and Imaging Data

Our analysis confirmed a high rate of comorbidities in COVID-19 patients²⁴ and their prognostic role with age.^25,26 A marked inflammatory milieu was a negative prognostic indicator, and associated concomitant bacterial superinfection could have led to a worse prognosis (procalcitonin was associated with negative outcomes).²⁷ The cardiovascular system was nevertheless stressed, as suggested by higher values of NT-proBNP in patients with negative outcomes, which could reflect sepsis-related systemic involvement.²⁸

It is known that the pulmonary damage caused by SARS-CoV-2 has a dynamic radiological and clinical course, with early areas of subsegmental consolidation, and bilateral ground-glass opacities predominating later in the course of the disease.²⁹ This could explain why in our population we found no specific radiological pattern leading to a worse outcome.

Medical Therapy

No specific pharmacological therapy was found to be associated with a positive outcome in our study, just like antiviral and immunomodulator therapies failed to demonstrate effectiveness in subsequent pandemic surges. The low statistical power of our study did not allow us to give insight into the effectiveness of steroids and heparin at any dosage.

PEEP Support and Prone Positioning

Continuous positive airway pressure was initiated in the majority of patients and maintained for several days. This was an absolute novelty, because we rarely had to keep patients in helmets for long. This was feasible thanks to the SICU’s high nurse-to-patient ratio and the possibility of providing monitored sedation. Patients who could no longer tolerate CPAP helmets or did not improve with CPAP support were evaluated with anesthetists for programming further management. No initial data on respiratory rate, level of hypoxemia, or oxygen support need (level of PEEP and Fio₂) could discriminate between outcomes.

Prone positioning during CPAP was implemented in 42% of our study population: P/F ratio amelioration after prone positioning was highly variable, ranging from very good P/F ratio improvements to few responses or no response. No significantly greater delta P/F ratio was seen after the first prone positioning cycle in patients who had a positive outcome, probably due to the small size of our population, but we observed a clear positive trend. Interestingly, patients showing a negative outcome had a lower percentage of long-term responses to prone positioning: 6 hours after resupination, they lost the benefit of prone positioning in terms of P/F ratio amelioration. Similarly, a greater number of patients tolerating prone positioning had a positive outcome. These data give insight on the possible benefits of prone positioning in a noninvasively supported cohort of patients, which has been mentioned in previous studies.^30,31

Outcomes and Variables Associated With Negative Outcomes

After correction for age and sex, we found in multiple regression analysis that higher D-dimer and LDH values, lymphopenia, and history of diabetes were independently associated with a worse outcome. Although our results had low statistical significance, we consider the trend of the obtained odds ratios important from a clinical point of view. These results could lead to greater attention being placed on COVID-19 patients who present with these characteristics upon their arrival to the ED because they have increased risk of death or intensive care need. Clinicians should consider SICU admission for these patients in order to guarantee closer monitoring and possibly more aggressive ventilatory treatments, earlier pronation, or earlier transfer to the ICU.

Limitations

The major limitation to our study is undoubtedly its statistical power, due to its relatively low patient population. Particularly, the small number of patients who underwent pronation did not allow speculation about the efficacy of this technique, although preliminary data seem promising. However, ours is among the first studies regarding patients with COVID-19 admitted to a SICU, and these preliminary data truthfully describe the Italian, and perhaps international, experience with the first surge of the pandemic.

Conclusions

Our data highlight the primary role of the SICU in COVID-19 in adequately treating critically ill patients who have high care needs different from intubation, and who require noninvasive ventilation for prolonged times as well as frequent pronation cycles. This setting of care may represent a valid, reliable, and effective option for critically ill respiratory patients. History of diabetes, lymphopenia, and high D-dimer and LDH values are independently associated with negative outcomes, and patients presenting with these characteristics should be strictly monitored.

Acknowledgments: The authors thank the Informatica System S.R.L., as well as Allessando Mendolia for the pro bono creation of the ISCovidCollect data collecting app.

Corresponding author: Sara Abram, MD, via Coppino, 12100 Cuneo, Italy; [email protected].

Disclosures: None.

From the Department of Emergency Medicine, Santa Croce e Carle Hospital, Cuneo, Italy (Drs. Abram, Tosello, Emanuele Bernardi, Allione, Cavalot, Dutto, Corsini, Martini, Sciolla, Sara Bernardi, and Lauria). From the School of Emergency Medicine, University of Turin, Turin, Italy (Drs. Paglietta and Giamello).

Objective: This retrospective and prospective cohort study was designed to describe the characteristics, treatments, and outcomes of patients with SARS-CoV-2 infection (COVID-19) admitted to subintensive care units (SICU) and to identify the variables associated with outcomes. SICUs have been extremely stressed during the pandemic, but most data regarding critically ill COVID-19 patients come from intensive care units (ICUs). Studies about COVID-19 patients in SICUs are lacking.

Setting and participants: The study included 88 COVID-19 patients admitted to our SICU in Cuneo, Italy, between March and May 2020.

Measurements: Clinical and ventilatory data were collected, and patients were divided by outcome. Multivariable logistic regression analysis examined the variables associated with negative outcomes (transfer to the ICU, palliation, or death in a SICU).

Results: A total of 60 patients (68%) had a positive outcome, and 28 patients (32%) had a negative outcome; 69 patients (78%) underwent continuous positive airway pressure (CPAP). Pronation (n = 37 [42%]) had been more frequently adopted in patients who had a positive outcome vs a negative outcome (n = 30 [50%] vs n = 7 [25%]; P = .048), and the median (interquartile range) Pao₂/Fio₂ ratio after 6 hours of prone positioning was lower in patients who had a negative outcome vs a positive outcome (144 [140-168] vs 249 [195-268], P = .006). Independent predictors of a negative outcome were diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P = .015), higher D-dimer (OR, 1.28; 95% CI, 1.04-1.57; P = .019), higher lactate dehydrogenase level (OR, 1.003; 95% CI, 1.000-1.006; P = .039), and lower lymphocytes count (OR, 0.996; 95% CI, 0.993-0.999; P = .004).

Conclusion: SICUs have a fundamental role in the treatment of critically ill patients with COVID-19, who require long-term CPAP and pronation cycles. Diabetes, lymphopenia, and high D-dimer and LDH levels are associated with negative outcomes.

Keywords: emergency medicine, noninvasive ventilation, prone position, continuous positive airway pressure.

The COVID-19 pandemic has led to large increases in hospital admissions. Subintensive care units (SICUs) are among the wards most under pressure worldwide,¹ dealing with the increased number of critically ill patients who need noninvasive ventilation, as well as serving as the best alternative to overfilled intensive care units (ICUs). In Italy, SICUs are playing a fundamental role in the management of COVID-19 patients, providing early treatment of respiratory failure by continuous noninvasive ventilation in order to reduce the need for intubation.^2-5 Nevertheless, the great majority of available data about critically ill COVID-19 patients comes from ICUs. Full studies about outcomes of patients in SICUs are lacking and need to be conducted.

We sought to evaluate the characteristics and outcomes of patients admitted to our SICU for COVID-19 to describe the treatments they needed and their impact on prognosis, and to identify the variables associated with patient outcomes.

Methods

Study Design

This cohort study used data from patients who were admitted in the very first weeks of the pandemic. Data were collected retrospectively as well as prospectively, since the ethical committee approved our project. The quality and quantity of data in the 2 groups were comparable.

Data were collected from electronic and written medical records gathered during the patient’s entire stay in our SICU. Data were entered in a database with limited and controlled access. This study complied with the Declaration of Helsinki and was approved by the local ethics committees (ID: MEDURG10).

Study Population

We studied 88 consecutive patients admitted to the SICU of the Santa Croce e Carle Teaching Hospital, Cuneo, Italy, for COVID-19, from March 8 to May 1, 2020. The diagnosis was based on acute respiratory failure associated with SARS-CoV-2 RNA detection on nasopharyngeal swab or tracheal aspirate and/or typical COVID-19 features on a pulmonary computed tomography (CT) scan.⁶ Exclusion criteria were age younger than 18 years and patient denial of permission to use their data for research purposes (the great majority of patients could actively give consent; for patients who were too sick to do so, family members were asked whether they were aware of any reason why the patient would deny consent).

The past medical history and recent symptoms description were obtained by manually reviewing medical records. Epidemiological exposure was defined as contact with SARS-CoV-2–positive people or staying in an epidemic outbreak area. Initial vital parameters, venous blood tests, arterial blood gas analysis, chest x-ray, as well as the result of the nasopharyngeal swab were gathered from the emergency department (ED) examination. (Additional swabs could be requested when the first one was negative but clinical suspicion for COVID-19 was high.) Upon admission to the SICU, a standardized panel of blood tests was performed, which was repeated the next day and then every 48 hours. Arterial blood gas analysis was performed when clinically indicated, at least twice a day, or following a scheduled time in patients undergoing pronation. Charlson Comorbidity Index⁷ and MuLBSTA score⁸ were calculated based on the collected data.

Imaging

Chest ultrasonography was performed in the ED at the time of hospitalization and once a day in the SICU. Pulmonary high-resolution computed tomography (HRCT) was performed when clinically indicated or when the results of nasopharyngeal swabs and/or x-ray results were discordant with COVID-19 clinical suspicion. Contrast CT was performed when pulmonary embolism was suspected.

Medical Therapy

Hydroxychloroquine, antiviral agents, tocilizumab, and ruxolitinib were used in the early phase of the pandemic, then were dismissed after evidence of no efficacy.^9-11 Steroids and low-molecular-weight heparin were used afterward. Enoxaparin was used at the standard prophylactic dosage, and 70% of the anticoagulant dosage was also adopted in patients with moderate-to-severe COVID-19 and D-dimer values >3 times the normal value.^12-14 Antibiotics were given when a bacterial superinfection was suspected.

Oxygen and Ventilatory Therapy

Oxygen support or noninvasive ventilation were started based on patients’ respiratory efficacy, estimated by respiratory rate and the ratio of partial pressure of arterial oxygen and fraction of inspired oxygen (P/F ratio).^15,16 Oxygen support was delivered through nasal cannula, Venturi mask, or reservoir mask. Noninvasive ventilation was performed by continuous positive airway pressure (CPAP) when the P/F ratio was <250 or the respiratory rate was >25 breaths per minute, using the helmet interface.^5,17 Prone positioning during CPAP^18-20 was adopted in patients meeting the acute respiratory distress syndrome (ARDS) criteria²¹ and having persistence of respiratory distress and P/F <300 after a 1-hour trial of CPAP.

The prone position was maintained based on patient tolerance. P/F ratio was measured before pronation (T0), after 1 hour of prone position (T1), before resupination (T2), and 6 hours after resupination (T3). With the same timing, the patient was asked to rate their comfort in each position, from 0 (lack of comfort) to 10 (optimal comfort). Delta P/F was defined as the difference between P/F at T3 and basal P/F at T0.

Outcomes

Positive outcomes were defined as patient discharge from the SICU or transfer to a lower-intensity care ward for treatment continuation. Negative outcomes were defined as need for transfer to the ICU, transfer to another ward for palliation, or death in the SICU.

Statistical Analysis

Continuous data are reported as median and interquartile range (IQR); normal distribution of variables was tested using the Shapiro-Wilk test. Categorical variables were reported as absolute number and percentage. The Mann-Whitney test was used to compare continuous variables between groups, and chi-square test with continuity correction was used for categorical variables. The variables that were most significantly associated with a negative outcome on the univariate analysis were included in a stepwise logistic regression analysis, in order to identify independent predictors of patient outcome. Statistical analysis was performed using JASP (JASP Team) software.

Study Population

Of the 88 patients included in the study, 70% were male; the median age was 66 years (IQR, 60-77). In most patients, the diagnosis of COVID-19 was derived from a positive SARS-CoV-2 nasopharyngeal swab. Six patients, however, maintained a negative swab at all determinations but had clinical and imaging features strongly suggesting COVID-19. No patients met the exclusion criteria. Most patients came from the ED (n = 58 [66%]) or general wards (n = 22 [25%]), while few were transferred from the ICU (n = 8 [9%]). The median length of stay in the SICU was 4 days (IQR, 2-7). An epidemiological link to affected persons or a known virus exposure was identifiable in 37 patients (42%).

Clinical, Laboratory, and Imaging Data

The clinical and anthropometric characteristics of patients are shown in Table 1. Hypertension and smoking habits were prevalent in our population, and the median Charlson Comorbidity Index was 3. Most patients experienced fever, dyspnea, and cough during the days before hospitalization.

Laboratory data showed a marked inflammatory milieu in all studied patients, both at baseline and after 24 and 72 hours. Lymphopenia was observed, along with a significant increase of lactate dehydrogenase (LDH), C-reactive protein (CPR), and D-dimer, and a mild increase of procalcitonin. N-terminal pro-brain natriuretic peptide (NT-proBNP) values were also increased, with normal troponin I values (Table 2).

Medical Therapy

Most patients (89%) received hydroxychloroquine, whereas steroids were used in one-third of the population (36%). Immunomodulators (tocilizumab and ruxolitinib) were restricted to 12 patients (14%). Empirical antiviral therapy was introduced in the first 41 patients (47%). Enoxaparin was the default agent for thromboembolism prophylaxis, and 6 patients (7%) received 70% of the anticoagulating dose.

Oxygen and Ventilatory Therapy

Basal median P/F ratio was 253 (IQR, 218-291), and respiratory rate at triage was 20 breaths/min (IQR, 16-28), underlining a moderate-to-severe respiratory insufficiency at presentation. A total of 69 patients (78%) underwent CPAP, with a median positive end-expiratory pressure (PEEP) of 10.0 cm H₂O (IQR, 7.5-10.0) and fraction of inspired oxygen (Fio₂) of 0.40 (IQR, 0.40-0.50). In 37 patients (42%) who received ongoing CPAP, prone positioning was adopted. In this subgroup, respiratory rate was not significantly different from baseline to resupination (24 vs 25 breaths/min). The median P/F improved from 197 (IQR, 154-236) at baseline to 217 (IQR, 180-262) after pronation (the duration of the prone position was variable, depending on patients’ tolerance: 1 to 6 hours or every pronation cycle). The median delta P/F ratio was 39.4 (IQR, –17.0 to 78.0).

Outcomes

A total of 28 patients (32%) had a negative outcome in the SICU: 8 patients (9%) died, having no clinical indication for higher-intensity care; 6 patients (7%) were transferred to general wards for palliation; and 14 patients (16%) needed an upgrade of cure intensity and were transferred to the ICU. Of these 14 patients, 9 died in the ICU. The total in-hospital mortality of COVID-19 patients, including patients transferred from the SICU to general wards in fair condition, was 27% (n = 24). Clinical, laboratory, and therapeutic characteristics between the 2 groups are shown in Table 4.

Patients who had a negative outcome were significantly older and had more comorbidities, as suggested by a significantly higher prevalence of diabetes and higher Charlson Comorbidity scores (reflecting the mortality risk based on age and comorbidities). The median MuLBSTA score, which estimates the 90-day mortality risk from viral pneumonia, was also higher in patients who had a negative outcome (9.33%). Symptom occurrence was not different in patients with a negative outcome (apart from cough, which was less frequent), but these patients underwent hospitalization earlier—since the appearance of their first COVID-19 symptoms—compared to patients who had a positive outcome. No difference was found in antihypertensive therapy with angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers among outcome groups.

More pronounced laboratory abnormalities were found in patients who had a negative outcome, compared to patients who had a positive outcome: lower lymphocytes and higher C-reactive protein (CRP), procalcitonin, D-dimer, LDH, and NT-proBNP. We found no differences in the radiological distribution of pulmonary involvement in patients who had negative or positive outcomes, nor in the adopted medical treatment.

Data showed no difference in CPAP implementation in the 2 groups. However, prone positioning had been more frequently adopted in the group of patients who had a positive outcome, compared with patients who had a negative outcome. No differences of basal P/F were found in patients who had a negative or positive outcome, but the median P/F after 6 hours of prone position was significantly lower in patients who had a negative outcome. The delta P/F ratio did not differ in the 2 groups of patients.

Multivariate Analysis

A logistic regression model was created, including the variables significantly associated with outcomes in the univariate analysis (age, sex, history of diabetes, lymphocytes, CRP, procalcitonin, LDH, NT-proBNP, and D-dimer). In the multivariate analysis, independent predictors of a negative outcome were history of diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P =.015), high D-dimer values (OR, 1.28; CI, 1.04-1.57; P = .019), high LDH values (OR, 1.003; CI, 1.000-1.006; P = .039), and low lymphocytes count (OR, 0.996; CI, 0.993-0.999; P = .004).

Discussion

Role of Subintensive Units and Mortality

The novelty of our report is its attempt to investigate the specific group of COVID-19 patients admitted to a SICU. In Italy, SICUs receive acutely ill, spontaneously breathing patients who need (invasive) hemodynamic monitoring, vasoactive medication, renal replacement therapy, chest- tube placement, thrombolysis, and respiratory noninvasive support. The nurse-to-patient ratio is higher than for general wards (usually 1 nurse to every 4 or 5 patients), though lower than for ICUs. In northern Italy, a great number of COVID-19 patients have required this kind of high-intensity care during the pandemic: Noninvasive ventilation support had to be maintained for several days, pronation maneuvers required a high number of people 2 or 3 times a day, and strict monitoring had to be assured. The SICU setting allows patients to buy time as a bridge to progressive reduction of pulmonary involvement, sometimes preventing the need for intubation.

The high prevalence of negative outcomes in the SICU underlines the complexity of COVID-19 patients in this setting. In fact, published data about mortality for patients with severe COVID-19 pneumonia are similar to ours.^22,23

Clinical, Laboratory, and Imaging Data

Our analysis confirmed a high rate of comorbidities in COVID-19 patients²⁴ and their prognostic role with age.^25,26 A marked inflammatory milieu was a negative prognostic indicator, and associated concomitant bacterial superinfection could have led to a worse prognosis (procalcitonin was associated with negative outcomes).²⁷ The cardiovascular system was nevertheless stressed, as suggested by higher values of NT-proBNP in patients with negative outcomes, which could reflect sepsis-related systemic involvement.²⁸

It is known that the pulmonary damage caused by SARS-CoV-2 has a dynamic radiological and clinical course, with early areas of subsegmental consolidation, and bilateral ground-glass opacities predominating later in the course of the disease.²⁹ This could explain why in our population we found no specific radiological pattern leading to a worse outcome.

Medical Therapy

No specific pharmacological therapy was found to be associated with a positive outcome in our study, just like antiviral and immunomodulator therapies failed to demonstrate effectiveness in subsequent pandemic surges. The low statistical power of our study did not allow us to give insight into the effectiveness of steroids and heparin at any dosage.

PEEP Support and Prone Positioning

Continuous positive airway pressure was initiated in the majority of patients and maintained for several days. This was an absolute novelty, because we rarely had to keep patients in helmets for long. This was feasible thanks to the SICU’s high nurse-to-patient ratio and the possibility of providing monitored sedation. Patients who could no longer tolerate CPAP helmets or did not improve with CPAP support were evaluated with anesthetists for programming further management. No initial data on respiratory rate, level of hypoxemia, or oxygen support need (level of PEEP and Fio₂) could discriminate between outcomes.

Prone positioning during CPAP was implemented in 42% of our study population: P/F ratio amelioration after prone positioning was highly variable, ranging from very good P/F ratio improvements to few responses or no response. No significantly greater delta P/F ratio was seen after the first prone positioning cycle in patients who had a positive outcome, probably due to the small size of our population, but we observed a clear positive trend. Interestingly, patients showing a negative outcome had a lower percentage of long-term responses to prone positioning: 6 hours after resupination, they lost the benefit of prone positioning in terms of P/F ratio amelioration. Similarly, a greater number of patients tolerating prone positioning had a positive outcome. These data give insight on the possible benefits of prone positioning in a noninvasively supported cohort of patients, which has been mentioned in previous studies.^30,31

Outcomes and Variables Associated With Negative Outcomes

After correction for age and sex, we found in multiple regression analysis that higher D-dimer and LDH values, lymphopenia, and history of diabetes were independently associated with a worse outcome. Although our results had low statistical significance, we consider the trend of the obtained odds ratios important from a clinical point of view. These results could lead to greater attention being placed on COVID-19 patients who present with these characteristics upon their arrival to the ED because they have increased risk of death or intensive care need. Clinicians should consider SICU admission for these patients in order to guarantee closer monitoring and possibly more aggressive ventilatory treatments, earlier pronation, or earlier transfer to the ICU.

Limitations

The major limitation to our study is undoubtedly its statistical power, due to its relatively low patient population. Particularly, the small number of patients who underwent pronation did not allow speculation about the efficacy of this technique, although preliminary data seem promising. However, ours is among the first studies regarding patients with COVID-19 admitted to a SICU, and these preliminary data truthfully describe the Italian, and perhaps international, experience with the first surge of the pandemic.

Conclusions

Our data highlight the primary role of the SICU in COVID-19 in adequately treating critically ill patients who have high care needs different from intubation, and who require noninvasive ventilation for prolonged times as well as frequent pronation cycles. This setting of care may represent a valid, reliable, and effective option for critically ill respiratory patients. History of diabetes, lymphopenia, and high D-dimer and LDH values are independently associated with negative outcomes, and patients presenting with these characteristics should be strictly monitored.

Acknowledgments: The authors thank the Informatica System S.R.L., as well as Allessando Mendolia for the pro bono creation of the ISCovidCollect data collecting app.

Corresponding author: Sara Abram, MD, via Coppino, 12100 Cuneo, Italy; [email protected].

Disclosures: None.

From the Department of Emergency Medicine, Santa Croce e Carle Hospital, Cuneo, Italy (Drs. Abram, Tosello, Emanuele Bernardi, Allione, Cavalot, Dutto, Corsini, Martini, Sciolla, Sara Bernardi, and Lauria). From the School of Emergency Medicine, University of Turin, Turin, Italy (Drs. Paglietta and Giamello).

Objective: This retrospective and prospective cohort study was designed to describe the characteristics, treatments, and outcomes of patients with SARS-CoV-2 infection (COVID-19) admitted to subintensive care units (SICU) and to identify the variables associated with outcomes. SICUs have been extremely stressed during the pandemic, but most data regarding critically ill COVID-19 patients come from intensive care units (ICUs). Studies about COVID-19 patients in SICUs are lacking.

Setting and participants: The study included 88 COVID-19 patients admitted to our SICU in Cuneo, Italy, between March and May 2020.

Measurements: Clinical and ventilatory data were collected, and patients were divided by outcome. Multivariable logistic regression analysis examined the variables associated with negative outcomes (transfer to the ICU, palliation, or death in a SICU).

Results: A total of 60 patients (68%) had a positive outcome, and 28 patients (32%) had a negative outcome; 69 patients (78%) underwent continuous positive airway pressure (CPAP). Pronation (n = 37 [42%]) had been more frequently adopted in patients who had a positive outcome vs a negative outcome (n = 30 [50%] vs n = 7 [25%]; P = .048), and the median (interquartile range) Pao₂/Fio₂ ratio after 6 hours of prone positioning was lower in patients who had a negative outcome vs a positive outcome (144 [140-168] vs 249 [195-268], P = .006). Independent predictors of a negative outcome were diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P = .015), higher D-dimer (OR, 1.28; 95% CI, 1.04-1.57; P = .019), higher lactate dehydrogenase level (OR, 1.003; 95% CI, 1.000-1.006; P = .039), and lower lymphocytes count (OR, 0.996; 95% CI, 0.993-0.999; P = .004).

Conclusion: SICUs have a fundamental role in the treatment of critically ill patients with COVID-19, who require long-term CPAP and pronation cycles. Diabetes, lymphopenia, and high D-dimer and LDH levels are associated with negative outcomes.

Keywords: emergency medicine, noninvasive ventilation, prone position, continuous positive airway pressure.

The COVID-19 pandemic has led to large increases in hospital admissions. Subintensive care units (SICUs) are among the wards most under pressure worldwide,¹ dealing with the increased number of critically ill patients who need noninvasive ventilation, as well as serving as the best alternative to overfilled intensive care units (ICUs). In Italy, SICUs are playing a fundamental role in the management of COVID-19 patients, providing early treatment of respiratory failure by continuous noninvasive ventilation in order to reduce the need for intubation.^2-5 Nevertheless, the great majority of available data about critically ill COVID-19 patients comes from ICUs. Full studies about outcomes of patients in SICUs are lacking and need to be conducted.

We sought to evaluate the characteristics and outcomes of patients admitted to our SICU for COVID-19 to describe the treatments they needed and their impact on prognosis, and to identify the variables associated with patient outcomes.

Methods

Study Design

This cohort study used data from patients who were admitted in the very first weeks of the pandemic. Data were collected retrospectively as well as prospectively, since the ethical committee approved our project. The quality and quantity of data in the 2 groups were comparable.

Data were collected from electronic and written medical records gathered during the patient’s entire stay in our SICU. Data were entered in a database with limited and controlled access. This study complied with the Declaration of Helsinki and was approved by the local ethics committees (ID: MEDURG10).

Study Population

We studied 88 consecutive patients admitted to the SICU of the Santa Croce e Carle Teaching Hospital, Cuneo, Italy, for COVID-19, from March 8 to May 1, 2020. The diagnosis was based on acute respiratory failure associated with SARS-CoV-2 RNA detection on nasopharyngeal swab or tracheal aspirate and/or typical COVID-19 features on a pulmonary computed tomography (CT) scan.⁶ Exclusion criteria were age younger than 18 years and patient denial of permission to use their data for research purposes (the great majority of patients could actively give consent; for patients who were too sick to do so, family members were asked whether they were aware of any reason why the patient would deny consent).

The past medical history and recent symptoms description were obtained by manually reviewing medical records. Epidemiological exposure was defined as contact with SARS-CoV-2–positive people or staying in an epidemic outbreak area. Initial vital parameters, venous blood tests, arterial blood gas analysis, chest x-ray, as well as the result of the nasopharyngeal swab were gathered from the emergency department (ED) examination. (Additional swabs could be requested when the first one was negative but clinical suspicion for COVID-19 was high.) Upon admission to the SICU, a standardized panel of blood tests was performed, which was repeated the next day and then every 48 hours. Arterial blood gas analysis was performed when clinically indicated, at least twice a day, or following a scheduled time in patients undergoing pronation. Charlson Comorbidity Index⁷ and MuLBSTA score⁸ were calculated based on the collected data.

Imaging

Chest ultrasonography was performed in the ED at the time of hospitalization and once a day in the SICU. Pulmonary high-resolution computed tomography (HRCT) was performed when clinically indicated or when the results of nasopharyngeal swabs and/or x-ray results were discordant with COVID-19 clinical suspicion. Contrast CT was performed when pulmonary embolism was suspected.

Medical Therapy

Hydroxychloroquine, antiviral agents, tocilizumab, and ruxolitinib were used in the early phase of the pandemic, then were dismissed after evidence of no efficacy.^9-11 Steroids and low-molecular-weight heparin were used afterward. Enoxaparin was used at the standard prophylactic dosage, and 70% of the anticoagulant dosage was also adopted in patients with moderate-to-severe COVID-19 and D-dimer values >3 times the normal value.^12-14 Antibiotics were given when a bacterial superinfection was suspected.

Oxygen and Ventilatory Therapy

Oxygen support or noninvasive ventilation were started based on patients’ respiratory efficacy, estimated by respiratory rate and the ratio of partial pressure of arterial oxygen and fraction of inspired oxygen (P/F ratio).^15,16 Oxygen support was delivered through nasal cannula, Venturi mask, or reservoir mask. Noninvasive ventilation was performed by continuous positive airway pressure (CPAP) when the P/F ratio was <250 or the respiratory rate was >25 breaths per minute, using the helmet interface.^5,17 Prone positioning during CPAP^18-20 was adopted in patients meeting the acute respiratory distress syndrome (ARDS) criteria²¹ and having persistence of respiratory distress and P/F <300 after a 1-hour trial of CPAP.

The prone position was maintained based on patient tolerance. P/F ratio was measured before pronation (T0), after 1 hour of prone position (T1), before resupination (T2), and 6 hours after resupination (T3). With the same timing, the patient was asked to rate their comfort in each position, from 0 (lack of comfort) to 10 (optimal comfort). Delta P/F was defined as the difference between P/F at T3 and basal P/F at T0.

Outcomes

Positive outcomes were defined as patient discharge from the SICU or transfer to a lower-intensity care ward for treatment continuation. Negative outcomes were defined as need for transfer to the ICU, transfer to another ward for palliation, or death in the SICU.

Statistical Analysis

Continuous data are reported as median and interquartile range (IQR); normal distribution of variables was tested using the Shapiro-Wilk test. Categorical variables were reported as absolute number and percentage. The Mann-Whitney test was used to compare continuous variables between groups, and chi-square test with continuity correction was used for categorical variables. The variables that were most significantly associated with a negative outcome on the univariate analysis were included in a stepwise logistic regression analysis, in order to identify independent predictors of patient outcome. Statistical analysis was performed using JASP (JASP Team) software.

Study Population

Of the 88 patients included in the study, 70% were male; the median age was 66 years (IQR, 60-77). In most patients, the diagnosis of COVID-19 was derived from a positive SARS-CoV-2 nasopharyngeal swab. Six patients, however, maintained a negative swab at all determinations but had clinical and imaging features strongly suggesting COVID-19. No patients met the exclusion criteria. Most patients came from the ED (n = 58 [66%]) or general wards (n = 22 [25%]), while few were transferred from the ICU (n = 8 [9%]). The median length of stay in the SICU was 4 days (IQR, 2-7). An epidemiological link to affected persons or a known virus exposure was identifiable in 37 patients (42%).

Clinical, Laboratory, and Imaging Data

The clinical and anthropometric characteristics of patients are shown in Table 1. Hypertension and smoking habits were prevalent in our population, and the median Charlson Comorbidity Index was 3. Most patients experienced fever, dyspnea, and cough during the days before hospitalization.

Laboratory data showed a marked inflammatory milieu in all studied patients, both at baseline and after 24 and 72 hours. Lymphopenia was observed, along with a significant increase of lactate dehydrogenase (LDH), C-reactive protein (CPR), and D-dimer, and a mild increase of procalcitonin. N-terminal pro-brain natriuretic peptide (NT-proBNP) values were also increased, with normal troponin I values (Table 2).

Medical Therapy

Most patients (89%) received hydroxychloroquine, whereas steroids were used in one-third of the population (36%). Immunomodulators (tocilizumab and ruxolitinib) were restricted to 12 patients (14%). Empirical antiviral therapy was introduced in the first 41 patients (47%). Enoxaparin was the default agent for thromboembolism prophylaxis, and 6 patients (7%) received 70% of the anticoagulating dose.

Oxygen and Ventilatory Therapy

Basal median P/F ratio was 253 (IQR, 218-291), and respiratory rate at triage was 20 breaths/min (IQR, 16-28), underlining a moderate-to-severe respiratory insufficiency at presentation. A total of 69 patients (78%) underwent CPAP, with a median positive end-expiratory pressure (PEEP) of 10.0 cm H₂O (IQR, 7.5-10.0) and fraction of inspired oxygen (Fio₂) of 0.40 (IQR, 0.40-0.50). In 37 patients (42%) who received ongoing CPAP, prone positioning was adopted. In this subgroup, respiratory rate was not significantly different from baseline to resupination (24 vs 25 breaths/min). The median P/F improved from 197 (IQR, 154-236) at baseline to 217 (IQR, 180-262) after pronation (the duration of the prone position was variable, depending on patients’ tolerance: 1 to 6 hours or every pronation cycle). The median delta P/F ratio was 39.4 (IQR, –17.0 to 78.0).

Outcomes

A total of 28 patients (32%) had a negative outcome in the SICU: 8 patients (9%) died, having no clinical indication for higher-intensity care; 6 patients (7%) were transferred to general wards for palliation; and 14 patients (16%) needed an upgrade of cure intensity and were transferred to the ICU. Of these 14 patients, 9 died in the ICU. The total in-hospital mortality of COVID-19 patients, including patients transferred from the SICU to general wards in fair condition, was 27% (n = 24). Clinical, laboratory, and therapeutic characteristics between the 2 groups are shown in Table 4.

Patients who had a negative outcome were significantly older and had more comorbidities, as suggested by a significantly higher prevalence of diabetes and higher Charlson Comorbidity scores (reflecting the mortality risk based on age and comorbidities). The median MuLBSTA score, which estimates the 90-day mortality risk from viral pneumonia, was also higher in patients who had a negative outcome (9.33%). Symptom occurrence was not different in patients with a negative outcome (apart from cough, which was less frequent), but these patients underwent hospitalization earlier—since the appearance of their first COVID-19 symptoms—compared to patients who had a positive outcome. No difference was found in antihypertensive therapy with angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers among outcome groups.

More pronounced laboratory abnormalities were found in patients who had a negative outcome, compared to patients who had a positive outcome: lower lymphocytes and higher C-reactive protein (CRP), procalcitonin, D-dimer, LDH, and NT-proBNP. We found no differences in the radiological distribution of pulmonary involvement in patients who had negative or positive outcomes, nor in the adopted medical treatment.

Data showed no difference in CPAP implementation in the 2 groups. However, prone positioning had been more frequently adopted in the group of patients who had a positive outcome, compared with patients who had a negative outcome. No differences of basal P/F were found in patients who had a negative or positive outcome, but the median P/F after 6 hours of prone position was significantly lower in patients who had a negative outcome. The delta P/F ratio did not differ in the 2 groups of patients.

Multivariate Analysis

A logistic regression model was created, including the variables significantly associated with outcomes in the univariate analysis (age, sex, history of diabetes, lymphocytes, CRP, procalcitonin, LDH, NT-proBNP, and D-dimer). In the multivariate analysis, independent predictors of a negative outcome were history of diabetes (odds ratio [OR], 8.22; 95% CI, 1.50-44.70; P =.015), high D-dimer values (OR, 1.28; CI, 1.04-1.57; P = .019), high LDH values (OR, 1.003; CI, 1.000-1.006; P = .039), and low lymphocytes count (OR, 0.996; CI, 0.993-0.999; P = .004).

Discussion

Role of Subintensive Units and Mortality

The novelty of our report is its attempt to investigate the specific group of COVID-19 patients admitted to a SICU. In Italy, SICUs receive acutely ill, spontaneously breathing patients who need (invasive) hemodynamic monitoring, vasoactive medication, renal replacement therapy, chest- tube placement, thrombolysis, and respiratory noninvasive support. The nurse-to-patient ratio is higher than for general wards (usually 1 nurse to every 4 or 5 patients), though lower than for ICUs. In northern Italy, a great number of COVID-19 patients have required this kind of high-intensity care during the pandemic: Noninvasive ventilation support had to be maintained for several days, pronation maneuvers required a high number of people 2 or 3 times a day, and strict monitoring had to be assured. The SICU setting allows patients to buy time as a bridge to progressive reduction of pulmonary involvement, sometimes preventing the need for intubation.

The high prevalence of negative outcomes in the SICU underlines the complexity of COVID-19 patients in this setting. In fact, published data about mortality for patients with severe COVID-19 pneumonia are similar to ours.^22,23

Clinical, Laboratory, and Imaging Data

Our analysis confirmed a high rate of comorbidities in COVID-19 patients²⁴ and their prognostic role with age.^25,26 A marked inflammatory milieu was a negative prognostic indicator, and associated concomitant bacterial superinfection could have led to a worse prognosis (procalcitonin was associated with negative outcomes).²⁷ The cardiovascular system was nevertheless stressed, as suggested by higher values of NT-proBNP in patients with negative outcomes, which could reflect sepsis-related systemic involvement.²⁸

It is known that the pulmonary damage caused by SARS-CoV-2 has a dynamic radiological and clinical course, with early areas of subsegmental consolidation, and bilateral ground-glass opacities predominating later in the course of the disease.²⁹ This could explain why in our population we found no specific radiological pattern leading to a worse outcome.

Medical Therapy

No specific pharmacological therapy was found to be associated with a positive outcome in our study, just like antiviral and immunomodulator therapies failed to demonstrate effectiveness in subsequent pandemic surges. The low statistical power of our study did not allow us to give insight into the effectiveness of steroids and heparin at any dosage.

PEEP Support and Prone Positioning

Continuous positive airway pressure was initiated in the majority of patients and maintained for several days. This was an absolute novelty, because we rarely had to keep patients in helmets for long. This was feasible thanks to the SICU’s high nurse-to-patient ratio and the possibility of providing monitored sedation. Patients who could no longer tolerate CPAP helmets or did not improve with CPAP support were evaluated with anesthetists for programming further management. No initial data on respiratory rate, level of hypoxemia, or oxygen support need (level of PEEP and Fio₂) could discriminate between outcomes.

Prone positioning during CPAP was implemented in 42% of our study population: P/F ratio amelioration after prone positioning was highly variable, ranging from very good P/F ratio improvements to few responses or no response. No significantly greater delta P/F ratio was seen after the first prone positioning cycle in patients who had a positive outcome, probably due to the small size of our population, but we observed a clear positive trend. Interestingly, patients showing a negative outcome had a lower percentage of long-term responses to prone positioning: 6 hours after resupination, they lost the benefit of prone positioning in terms of P/F ratio amelioration. Similarly, a greater number of patients tolerating prone positioning had a positive outcome. These data give insight on the possible benefits of prone positioning in a noninvasively supported cohort of patients, which has been mentioned in previous studies.^30,31

Outcomes and Variables Associated With Negative Outcomes

After correction for age and sex, we found in multiple regression analysis that higher D-dimer and LDH values, lymphopenia, and history of diabetes were independently associated with a worse outcome. Although our results had low statistical significance, we consider the trend of the obtained odds ratios important from a clinical point of view. These results could lead to greater attention being placed on COVID-19 patients who present with these characteristics upon their arrival to the ED because they have increased risk of death or intensive care need. Clinicians should consider SICU admission for these patients in order to guarantee closer monitoring and possibly more aggressive ventilatory treatments, earlier pronation, or earlier transfer to the ICU.

Limitations

The major limitation to our study is undoubtedly its statistical power, due to its relatively low patient population. Particularly, the small number of patients who underwent pronation did not allow speculation about the efficacy of this technique, although preliminary data seem promising. However, ours is among the first studies regarding patients with COVID-19 admitted to a SICU, and these preliminary data truthfully describe the Italian, and perhaps international, experience with the first surge of the pandemic.

Conclusions

Our data highlight the primary role of the SICU in COVID-19 in adequately treating critically ill patients who have high care needs different from intubation, and who require noninvasive ventilation for prolonged times as well as frequent pronation cycles. This setting of care may represent a valid, reliable, and effective option for critically ill respiratory patients. History of diabetes, lymphopenia, and high D-dimer and LDH values are independently associated with negative outcomes, and patients presenting with these characteristics should be strictly monitored.

Acknowledgments: The authors thank the Informatica System S.R.L., as well as Allessando Mendolia for the pro bono creation of the ISCovidCollect data collecting app.

Corresponding author: Sara Abram, MD, via Coppino, 12100 Cuneo, Italy; [email protected].

Disclosures: None.

Equitable Standards for All Patients in a Crisis

Health care delivered during a pandemic instantiates medicine’s perspectives on the value of human life in clinical scenarios where resource allocation is limited. The COVID-19 pandemic has fostered dialogue and debate around the ethical principles that underly such resource allocation, which generally balance (1) utilitarian optimization of resources, (2) equality or equity in health access, (3) the instrumental value of individuals as agents in society, and (4) prioritizing the “worst off” in their natural history of disease.^1,2 State legislatures and health systems have responded to the challeges posed by COVID-19 by considering both the scarcity of intensive care resources, such as mechanical ventilation and hemodialysis, and the clinical criteria to be used for determining which patients should receive said resources. These crisis guidelines have yielded several concerning themes vis-à-vis equitable distribution of health care resources, particularly when the disability status of patients is considered alongside life-expectancy or quality of life.³

Crisis standards of care (CSC) prioritize population-level health under a utilitarian paradigm, explicitly maximizing “life-years” within a population of patients rather than the life of any individual patient.⁴ Debated during initial COVID surges, these CSC guidelines have recently been enacted at the state level in several settings, including Alaska and Idaho.⁵ In a setting with scarce intensive care resources, balancing health equity in access to these resources against population-based survival metrics has been a challenge for commissions considering CSC.^6,7 This need for balance has further promoted systemic views of “disability,” raising concern for structural “ableism” and highlighting the need for greater “ability awareness” in clinicians’ continued professional learning.

Structural Ableism: Defining Perspectives to Address Health Equity

Ableism has been defined as “a system that places value on people’s bodies and minds, based on societally constructed ideas of normalcy, intelligence, excellence, and productivity…[and] leads to people and society determining who is valuable and worthy based on their appearance and/or their ability to satisfactorily [re]produce, excel, and ‘behave.’”⁸ Regarding CSC, concerns about systemic bias in guideline design were raised early by disability advocacy groups during comment periods.^9,10 More broadly, concerns about ableism sit alongside many deeply rooted societal perspectives of disabled individuals as pitiable or, conversely, heroic for having “overcome” their disability in some way. As a physician who sits in a manual wheelchair with paraplegia and mobility impairment, I have equally been subject to inappropriate bias and inappropriate praise for living in a wheelchair. I have also wondered, alongside my patients living with different levels of mobility or ability, why others often view us as “worse off.” Addressing directly whether disabled individuals are “worse off,” disability rights attorney and advocate Harriet McBryde Johnson has articulated a predominant sentiment among persons living with unique or different abilities:

Are we “worse off”? I don’t think so. Not in any meaningful way. There are too many variables. For those of us with congenital conditions, disability shapes all we are. Those disabled later in life adapt. We take constraints that no one would choose and build rich and satisfying lives within them. We enjoy pleasures other people enjoy and pleasures peculiarly our own. We have something the world needs.¹¹

Many physician colleagues have common, invisible diseases such as diabetes and heart disease; fewer colleagues share conditions that are as visible as my spinal cord injury, as readily apparent to patients upon my entry to their hospital rooms. This simultaneous and inescapable identity as both patient and provider has afforded me wonderful doctor-patient interactions, particularly with those patients who appreciate how my patient experience impacts my ability to partially understand theirs. However, this simultaneous identity as doctor and patient also informed my personal and professional concerns regarding structural ableism as I considered scoring my own acutely ill hospital medicine patients with CSC triage scores in April 2020.

As a practicing hospital medicine physician, I have been emboldened by the efforts of my fellow clinicians amid COVID-19; their efforts have reaffirmed all the reasons I pursued a career in medicine. However, when I heard my clinical colleagues’ first explanation of the Massachusetts CSC guidelines in April 2020, I raised my hand to ask whether the “life-years” to which the guidelines referred were quality-adjusted. My concern regarding the implicit use of quality-adjusted life years (QALY) or disability-adjusted life years in clinical decision-making and implementation of these guidelines was validated when no clinical leaders could address this question directly. Sitting on the CSC committee for my hospital during this time was an honor. However, it was disconcerting to hear many clinicians’ unease when estimating mean survival for common chronic diseases, ranging from end-stage renal disease to advanced heart failure. If my expert colleagues, clinical specialists in kidney and heart disease, could not confidently apply mean survival estimates to multimorbid hospital patients, then idiosyncratic clinical judgment was sure to have a heavy hand in any calculation of “life-years.” Thus, my primary concern was that clinicians using triage heuristics would be subject to bias, regardless of their intention, and negatively adjust for the quality of a disabled life in their CSC triage scoring. My secondary concern was that the CSC guidelines themselves included systemic bias against disabled individuals.

According to CSC schema, triage scores index heavily on Sequential Organ Failure Assessment (SOFA) scores to define short-term survival; SOFA scores are partially driven by the Glasgow Coma Scale (GCS). Following professional and public comment periods, CSC guidelines in Massachusetts were revised to, among other critical points of revision, change prognostic estimation via “life years” in favor of generic estimation of short-term survival (Table). I wondered, if I presented to an emergency department with severe COVID-19 and was scored with the GCS for the purpose of making a CSC ventilator triage decision, how would my complete paraplegia and lower-extremity motor impairment be accounted for by a clinician assessing “best motor response” in the GCS? The purpose of these scores is to act algorithmically, to guide clinicians whose cognitive load and time limitations may not allow for adjustment of these algorithms based on the individual patient in front of them. Individualization of clinical decisions is part of medicine’s art, but is difficult in the best of times and no easier during a crisis in care delivery. As CSC triage scores were amended and addended throughout 2020, I returned to the COVID wards, time and again wondering, “What have we learned about systemic bias and health inequity in the CSC process and the pandemic broadly, with specific regard to disability?”

Ability Awareness: Room for Our Improvement

Unfortunately, there is reason to believe that clinical judgment is impaired by structural ableism. In seminal work on this topic, Gerhart et al¹² demonstrated that clinicians considered spinal cord injury (SCI) survivors to have low self-perceptions of worthiness, overall negative attitudes, and low self-esteem as compared to able-bodied individuals. However, surveyed SCI survivors generally had similar self-perceptions of worth and positivity as compared to ”able-bodied” clinicians.¹² For providers who care for persons with disabilities, the majority (82.4%) have rated their disabled patients’ quality of life as worse.¹³ It is no wonder that patients with disabilities are more likely to feel that their doctor-patient relationship is impacted by lack of understanding, negative sentiment, or simple lack of listening.¹⁴ Generally, this poor doctor-patient relationship with disabled patients is exacerbated by poor exposure of medical trainees to disability education; only 34.2% of internal medicine residents recall any form of disability education in medical school, while only 52% of medical school deans report having disability educational content in their curricula.^15,16 There is a similar lack of disability representation in the population of medical trainees themselves. While approximately 20% of the American population lives with a disability, less than 2% of American medical students have a disability.^17-19

While representation of disabled populations in medical practice remains poor, disabled patients are generally less likely to receive age-appropriate prevention, appropriate access to care, and equal access to treatment.^20-22 “Diagnostic overshadowing” refers to clinicians’ attribution of nonspecific signs or symptoms to a patient’s chronic disability as opposed to acute illness.²³ This phenomenon has led to higher rates of preventable malignancy in disabled patients and misattribution of common somatic symptoms to intellectual disability.^24,25 With this disparity in place as status quo for health care delivery to disabled populations, it is no surprise that certain portions of the disabled population have accounted for disproportionate mortality due to COVID-19.^26,27Disability advocates have called for “nothing about us without us,” a phrase associated with the United Nations Convention on the Rights of Persons with Disabilities. Understanding the profound neurodiversity among several forms of sensory and cognitive disabilities, as well as the functional difference between cognitive disabilities, mobility impairment, and inability to meet one’s instrumental activities of daily living independently, others have proposed a unique approach to certain disabled populations in COVID care.²⁸ My own perspective is that definite progress may require a more general understanding of the prevalence of disability by clinicians, both via medical training and by directly addressing health equity for disabled populations in such calculations as the CSC. Systemic ableism is apparent in our most common clinical scoring systems, ranging from the GCS and Functional Assessment Staging Table to the Eastern Cooperative Oncology Group and Karnofsky Performance Status scales. I have reexamined these scoring systems in my own understanding given their general equation of ambulation with ability or normalcy. As a doctor in a manual wheelchair who values greatly my personal quality of life and professional contribution to patient care, I worry that these scoring systems inherently discount my own equitable access to care. Individualization of patients’ particular abilities in the context of these scales must occur alongside evidence-based, guideline-directed management via these scoring systems.

Conclusion: Future Orientation

Updated CSC guidelines have accounted for the unique considerations of disabled patients by effectively caveating their scoring algorithms, directing clinicians via disclaimers to uniquely consider their disabled patients in clinical judgement. This is a first step, but it is also one that erodes the value of algorithms, which generally obviate more deliberative thinking and individualization. For our patients who lack certain abilities, as CSC continue to be activated in several states, we have an opportunity to pursue more inherently equitable solutions before further suffering accrues.²⁹ By way of example, adaptations to scoring systems that leverage QALYs for value-based drug pricing indices have been proposed by organizations like the Institute for Clinical and Economic Review, which proposed the Equal-Value-of Life-Years-Gained framework to inform QALY-based arbitration of drug pricing.³⁰ This is not a perfect rubric but instead represents an attempt to balance consideration of drugs, as has been done with ventilators during the pandemic, as a scare and expensive resource while addressing the just concerns of advocacy groups in structural ableism.

Resource stewardship during a crisis should not discount those states of human life that are perceived to be less desirable, particularly if they are not experienced as less desirable but are experienced uniquely. Instead, we should consider equitably measuring our intervention to match a patient’s needs, as we would dose-adjust a medication for renal function or consider minimally invasive procedures for multimorbid patients. COVID-19 has reflected our profession’s ethical adaptation during crisis as resources have become scarce; there is no better time to define solutions for health equity. We should now be concerned equally by the influence our personal biases have on our clinical practice and by the way in which these crisis standards will influence patients’ perception of and trust in their care providers during periods of perceived plentiful resources in the future. Health care resources are always limited, allocated according to societal values; if we value health equity for people of all abilities, then we will consider these abilities equitably as we pursue new standards for health care delivery.

Corresponding author: Gregory D. Snyder, MD, MBA, 2014 Washington Street, Newton, MA 02462; [email protected].

Disclosures: None.
 

Equitable Standards for All Patients in a Crisis

Health care delivered during a pandemic instantiates medicine’s perspectives on the value of human life in clinical scenarios where resource allocation is limited. The COVID-19 pandemic has fostered dialogue and debate around the ethical principles that underly such resource allocation, which generally balance (1) utilitarian optimization of resources, (2) equality or equity in health access, (3) the instrumental value of individuals as agents in society, and (4) prioritizing the “worst off” in their natural history of disease.^1,2 State legislatures and health systems have responded to the challeges posed by COVID-19 by considering both the scarcity of intensive care resources, such as mechanical ventilation and hemodialysis, and the clinical criteria to be used for determining which patients should receive said resources. These crisis guidelines have yielded several concerning themes vis-à-vis equitable distribution of health care resources, particularly when the disability status of patients is considered alongside life-expectancy or quality of life.³

Crisis standards of care (CSC) prioritize population-level health under a utilitarian paradigm, explicitly maximizing “life-years” within a population of patients rather than the life of any individual patient.⁴ Debated during initial COVID surges, these CSC guidelines have recently been enacted at the state level in several settings, including Alaska and Idaho.⁵ In a setting with scarce intensive care resources, balancing health equity in access to these resources against population-based survival metrics has been a challenge for commissions considering CSC.^6,7 This need for balance has further promoted systemic views of “disability,” raising concern for structural “ableism” and highlighting the need for greater “ability awareness” in clinicians’ continued professional learning.

Structural Ableism: Defining Perspectives to Address Health Equity

Ableism has been defined as “a system that places value on people’s bodies and minds, based on societally constructed ideas of normalcy, intelligence, excellence, and productivity…[and] leads to people and society determining who is valuable and worthy based on their appearance and/or their ability to satisfactorily [re]produce, excel, and ‘behave.’”⁸ Regarding CSC, concerns about systemic bias in guideline design were raised early by disability advocacy groups during comment periods.^9,10 More broadly, concerns about ableism sit alongside many deeply rooted societal perspectives of disabled individuals as pitiable or, conversely, heroic for having “overcome” their disability in some way. As a physician who sits in a manual wheelchair with paraplegia and mobility impairment, I have equally been subject to inappropriate bias and inappropriate praise for living in a wheelchair. I have also wondered, alongside my patients living with different levels of mobility or ability, why others often view us as “worse off.” Addressing directly whether disabled individuals are “worse off,” disability rights attorney and advocate Harriet McBryde Johnson has articulated a predominant sentiment among persons living with unique or different abilities:

Are we “worse off”? I don’t think so. Not in any meaningful way. There are too many variables. For those of us with congenital conditions, disability shapes all we are. Those disabled later in life adapt. We take constraints that no one would choose and build rich and satisfying lives within them. We enjoy pleasures other people enjoy and pleasures peculiarly our own. We have something the world needs.¹¹

Many physician colleagues have common, invisible diseases such as diabetes and heart disease; fewer colleagues share conditions that are as visible as my spinal cord injury, as readily apparent to patients upon my entry to their hospital rooms. This simultaneous and inescapable identity as both patient and provider has afforded me wonderful doctor-patient interactions, particularly with those patients who appreciate how my patient experience impacts my ability to partially understand theirs. However, this simultaneous identity as doctor and patient also informed my personal and professional concerns regarding structural ableism as I considered scoring my own acutely ill hospital medicine patients with CSC triage scores in April 2020.

As a practicing hospital medicine physician, I have been emboldened by the efforts of my fellow clinicians amid COVID-19; their efforts have reaffirmed all the reasons I pursued a career in medicine. However, when I heard my clinical colleagues’ first explanation of the Massachusetts CSC guidelines in April 2020, I raised my hand to ask whether the “life-years” to which the guidelines referred were quality-adjusted. My concern regarding the implicit use of quality-adjusted life years (QALY) or disability-adjusted life years in clinical decision-making and implementation of these guidelines was validated when no clinical leaders could address this question directly. Sitting on the CSC committee for my hospital during this time was an honor. However, it was disconcerting to hear many clinicians’ unease when estimating mean survival for common chronic diseases, ranging from end-stage renal disease to advanced heart failure. If my expert colleagues, clinical specialists in kidney and heart disease, could not confidently apply mean survival estimates to multimorbid hospital patients, then idiosyncratic clinical judgment was sure to have a heavy hand in any calculation of “life-years.” Thus, my primary concern was that clinicians using triage heuristics would be subject to bias, regardless of their intention, and negatively adjust for the quality of a disabled life in their CSC triage scoring. My secondary concern was that the CSC guidelines themselves included systemic bias against disabled individuals.

According to CSC schema, triage scores index heavily on Sequential Organ Failure Assessment (SOFA) scores to define short-term survival; SOFA scores are partially driven by the Glasgow Coma Scale (GCS). Following professional and public comment periods, CSC guidelines in Massachusetts were revised to, among other critical points of revision, change prognostic estimation via “life years” in favor of generic estimation of short-term survival (Table). I wondered, if I presented to an emergency department with severe COVID-19 and was scored with the GCS for the purpose of making a CSC ventilator triage decision, how would my complete paraplegia and lower-extremity motor impairment be accounted for by a clinician assessing “best motor response” in the GCS? The purpose of these scores is to act algorithmically, to guide clinicians whose cognitive load and time limitations may not allow for adjustment of these algorithms based on the individual patient in front of them. Individualization of clinical decisions is part of medicine’s art, but is difficult in the best of times and no easier during a crisis in care delivery. As CSC triage scores were amended and addended throughout 2020, I returned to the COVID wards, time and again wondering, “What have we learned about systemic bias and health inequity in the CSC process and the pandemic broadly, with specific regard to disability?”

Ability Awareness: Room for Our Improvement

Unfortunately, there is reason to believe that clinical judgment is impaired by structural ableism. In seminal work on this topic, Gerhart et al¹² demonstrated that clinicians considered spinal cord injury (SCI) survivors to have low self-perceptions of worthiness, overall negative attitudes, and low self-esteem as compared to able-bodied individuals. However, surveyed SCI survivors generally had similar self-perceptions of worth and positivity as compared to ”able-bodied” clinicians.¹² For providers who care for persons with disabilities, the majority (82.4%) have rated their disabled patients’ quality of life as worse.¹³ It is no wonder that patients with disabilities are more likely to feel that their doctor-patient relationship is impacted by lack of understanding, negative sentiment, or simple lack of listening.¹⁴ Generally, this poor doctor-patient relationship with disabled patients is exacerbated by poor exposure of medical trainees to disability education; only 34.2% of internal medicine residents recall any form of disability education in medical school, while only 52% of medical school deans report having disability educational content in their curricula.^15,16 There is a similar lack of disability representation in the population of medical trainees themselves. While approximately 20% of the American population lives with a disability, less than 2% of American medical students have a disability.^17-19

While representation of disabled populations in medical practice remains poor, disabled patients are generally less likely to receive age-appropriate prevention, appropriate access to care, and equal access to treatment.^20-22 “Diagnostic overshadowing” refers to clinicians’ attribution of nonspecific signs or symptoms to a patient’s chronic disability as opposed to acute illness.²³ This phenomenon has led to higher rates of preventable malignancy in disabled patients and misattribution of common somatic symptoms to intellectual disability.^24,25 With this disparity in place as status quo for health care delivery to disabled populations, it is no surprise that certain portions of the disabled population have accounted for disproportionate mortality due to COVID-19.^26,27Disability advocates have called for “nothing about us without us,” a phrase associated with the United Nations Convention on the Rights of Persons with Disabilities. Understanding the profound neurodiversity among several forms of sensory and cognitive disabilities, as well as the functional difference between cognitive disabilities, mobility impairment, and inability to meet one’s instrumental activities of daily living independently, others have proposed a unique approach to certain disabled populations in COVID care.²⁸ My own perspective is that definite progress may require a more general understanding of the prevalence of disability by clinicians, both via medical training and by directly addressing health equity for disabled populations in such calculations as the CSC. Systemic ableism is apparent in our most common clinical scoring systems, ranging from the GCS and Functional Assessment Staging Table to the Eastern Cooperative Oncology Group and Karnofsky Performance Status scales. I have reexamined these scoring systems in my own understanding given their general equation of ambulation with ability or normalcy. As a doctor in a manual wheelchair who values greatly my personal quality of life and professional contribution to patient care, I worry that these scoring systems inherently discount my own equitable access to care. Individualization of patients’ particular abilities in the context of these scales must occur alongside evidence-based, guideline-directed management via these scoring systems.

Conclusion: Future Orientation

Updated CSC guidelines have accounted for the unique considerations of disabled patients by effectively caveating their scoring algorithms, directing clinicians via disclaimers to uniquely consider their disabled patients in clinical judgement. This is a first step, but it is also one that erodes the value of algorithms, which generally obviate more deliberative thinking and individualization. For our patients who lack certain abilities, as CSC continue to be activated in several states, we have an opportunity to pursue more inherently equitable solutions before further suffering accrues.²⁹ By way of example, adaptations to scoring systems that leverage QALYs for value-based drug pricing indices have been proposed by organizations like the Institute for Clinical and Economic Review, which proposed the Equal-Value-of Life-Years-Gained framework to inform QALY-based arbitration of drug pricing.³⁰ This is not a perfect rubric but instead represents an attempt to balance consideration of drugs, as has been done with ventilators during the pandemic, as a scare and expensive resource while addressing the just concerns of advocacy groups in structural ableism.

Resource stewardship during a crisis should not discount those states of human life that are perceived to be less desirable, particularly if they are not experienced as less desirable but are experienced uniquely. Instead, we should consider equitably measuring our intervention to match a patient’s needs, as we would dose-adjust a medication for renal function or consider minimally invasive procedures for multimorbid patients. COVID-19 has reflected our profession’s ethical adaptation during crisis as resources have become scarce; there is no better time to define solutions for health equity. We should now be concerned equally by the influence our personal biases have on our clinical practice and by the way in which these crisis standards will influence patients’ perception of and trust in their care providers during periods of perceived plentiful resources in the future. Health care resources are always limited, allocated according to societal values; if we value health equity for people of all abilities, then we will consider these abilities equitably as we pursue new standards for health care delivery.

Corresponding author: Gregory D. Snyder, MD, MBA, 2014 Washington Street, Newton, MA 02462; [email protected].

Disclosures: None.
 

Equitable Standards for All Patients in a Crisis

Health care delivered during a pandemic instantiates medicine’s perspectives on the value of human life in clinical scenarios where resource allocation is limited. The COVID-19 pandemic has fostered dialogue and debate around the ethical principles that underly such resource allocation, which generally balance (1) utilitarian optimization of resources, (2) equality or equity in health access, (3) the instrumental value of individuals as agents in society, and (4) prioritizing the “worst off” in their natural history of disease.^1,2 State legislatures and health systems have responded to the challeges posed by COVID-19 by considering both the scarcity of intensive care resources, such as mechanical ventilation and hemodialysis, and the clinical criteria to be used for determining which patients should receive said resources. These crisis guidelines have yielded several concerning themes vis-à-vis equitable distribution of health care resources, particularly when the disability status of patients is considered alongside life-expectancy or quality of life.³

Crisis standards of care (CSC) prioritize population-level health under a utilitarian paradigm, explicitly maximizing “life-years” within a population of patients rather than the life of any individual patient.⁴ Debated during initial COVID surges, these CSC guidelines have recently been enacted at the state level in several settings, including Alaska and Idaho.⁵ In a setting with scarce intensive care resources, balancing health equity in access to these resources against population-based survival metrics has been a challenge for commissions considering CSC.^6,7 This need for balance has further promoted systemic views of “disability,” raising concern for structural “ableism” and highlighting the need for greater “ability awareness” in clinicians’ continued professional learning.

Structural Ableism: Defining Perspectives to Address Health Equity

Ableism has been defined as “a system that places value on people’s bodies and minds, based on societally constructed ideas of normalcy, intelligence, excellence, and productivity…[and] leads to people and society determining who is valuable and worthy based on their appearance and/or their ability to satisfactorily [re]produce, excel, and ‘behave.’”⁸ Regarding CSC, concerns about systemic bias in guideline design were raised early by disability advocacy groups during comment periods.^9,10 More broadly, concerns about ableism sit alongside many deeply rooted societal perspectives of disabled individuals as pitiable or, conversely, heroic for having “overcome” their disability in some way. As a physician who sits in a manual wheelchair with paraplegia and mobility impairment, I have equally been subject to inappropriate bias and inappropriate praise for living in a wheelchair. I have also wondered, alongside my patients living with different levels of mobility or ability, why others often view us as “worse off.” Addressing directly whether disabled individuals are “worse off,” disability rights attorney and advocate Harriet McBryde Johnson has articulated a predominant sentiment among persons living with unique or different abilities:

Are we “worse off”? I don’t think so. Not in any meaningful way. There are too many variables. For those of us with congenital conditions, disability shapes all we are. Those disabled later in life adapt. We take constraints that no one would choose and build rich and satisfying lives within them. We enjoy pleasures other people enjoy and pleasures peculiarly our own. We have something the world needs.¹¹

Many physician colleagues have common, invisible diseases such as diabetes and heart disease; fewer colleagues share conditions that are as visible as my spinal cord injury, as readily apparent to patients upon my entry to their hospital rooms. This simultaneous and inescapable identity as both patient and provider has afforded me wonderful doctor-patient interactions, particularly with those patients who appreciate how my patient experience impacts my ability to partially understand theirs. However, this simultaneous identity as doctor and patient also informed my personal and professional concerns regarding structural ableism as I considered scoring my own acutely ill hospital medicine patients with CSC triage scores in April 2020.

As a practicing hospital medicine physician, I have been emboldened by the efforts of my fellow clinicians amid COVID-19; their efforts have reaffirmed all the reasons I pursued a career in medicine. However, when I heard my clinical colleagues’ first explanation of the Massachusetts CSC guidelines in April 2020, I raised my hand to ask whether the “life-years” to which the guidelines referred were quality-adjusted. My concern regarding the implicit use of quality-adjusted life years (QALY) or disability-adjusted life years in clinical decision-making and implementation of these guidelines was validated when no clinical leaders could address this question directly. Sitting on the CSC committee for my hospital during this time was an honor. However, it was disconcerting to hear many clinicians’ unease when estimating mean survival for common chronic diseases, ranging from end-stage renal disease to advanced heart failure. If my expert colleagues, clinical specialists in kidney and heart disease, could not confidently apply mean survival estimates to multimorbid hospital patients, then idiosyncratic clinical judgment was sure to have a heavy hand in any calculation of “life-years.” Thus, my primary concern was that clinicians using triage heuristics would be subject to bias, regardless of their intention, and negatively adjust for the quality of a disabled life in their CSC triage scoring. My secondary concern was that the CSC guidelines themselves included systemic bias against disabled individuals.

According to CSC schema, triage scores index heavily on Sequential Organ Failure Assessment (SOFA) scores to define short-term survival; SOFA scores are partially driven by the Glasgow Coma Scale (GCS). Following professional and public comment periods, CSC guidelines in Massachusetts were revised to, among other critical points of revision, change prognostic estimation via “life years” in favor of generic estimation of short-term survival (Table). I wondered, if I presented to an emergency department with severe COVID-19 and was scored with the GCS for the purpose of making a CSC ventilator triage decision, how would my complete paraplegia and lower-extremity motor impairment be accounted for by a clinician assessing “best motor response” in the GCS? The purpose of these scores is to act algorithmically, to guide clinicians whose cognitive load and time limitations may not allow for adjustment of these algorithms based on the individual patient in front of them. Individualization of clinical decisions is part of medicine’s art, but is difficult in the best of times and no easier during a crisis in care delivery. As CSC triage scores were amended and addended throughout 2020, I returned to the COVID wards, time and again wondering, “What have we learned about systemic bias and health inequity in the CSC process and the pandemic broadly, with specific regard to disability?”

Ability Awareness: Room for Our Improvement

Unfortunately, there is reason to believe that clinical judgment is impaired by structural ableism. In seminal work on this topic, Gerhart et al¹² demonstrated that clinicians considered spinal cord injury (SCI) survivors to have low self-perceptions of worthiness, overall negative attitudes, and low self-esteem as compared to able-bodied individuals. However, surveyed SCI survivors generally had similar self-perceptions of worth and positivity as compared to ”able-bodied” clinicians.¹² For providers who care for persons with disabilities, the majority (82.4%) have rated their disabled patients’ quality of life as worse.¹³ It is no wonder that patients with disabilities are more likely to feel that their doctor-patient relationship is impacted by lack of understanding, negative sentiment, or simple lack of listening.¹⁴ Generally, this poor doctor-patient relationship with disabled patients is exacerbated by poor exposure of medical trainees to disability education; only 34.2% of internal medicine residents recall any form of disability education in medical school, while only 52% of medical school deans report having disability educational content in their curricula.^15,16 There is a similar lack of disability representation in the population of medical trainees themselves. While approximately 20% of the American population lives with a disability, less than 2% of American medical students have a disability.^17-19

While representation of disabled populations in medical practice remains poor, disabled patients are generally less likely to receive age-appropriate prevention, appropriate access to care, and equal access to treatment.^20-22 “Diagnostic overshadowing” refers to clinicians’ attribution of nonspecific signs or symptoms to a patient’s chronic disability as opposed to acute illness.²³ This phenomenon has led to higher rates of preventable malignancy in disabled patients and misattribution of common somatic symptoms to intellectual disability.^24,25 With this disparity in place as status quo for health care delivery to disabled populations, it is no surprise that certain portions of the disabled population have accounted for disproportionate mortality due to COVID-19.^26,27Disability advocates have called for “nothing about us without us,” a phrase associated with the United Nations Convention on the Rights of Persons with Disabilities. Understanding the profound neurodiversity among several forms of sensory and cognitive disabilities, as well as the functional difference between cognitive disabilities, mobility impairment, and inability to meet one’s instrumental activities of daily living independently, others have proposed a unique approach to certain disabled populations in COVID care.²⁸ My own perspective is that definite progress may require a more general understanding of the prevalence of disability by clinicians, both via medical training and by directly addressing health equity for disabled populations in such calculations as the CSC. Systemic ableism is apparent in our most common clinical scoring systems, ranging from the GCS and Functional Assessment Staging Table to the Eastern Cooperative Oncology Group and Karnofsky Performance Status scales. I have reexamined these scoring systems in my own understanding given their general equation of ambulation with ability or normalcy. As a doctor in a manual wheelchair who values greatly my personal quality of life and professional contribution to patient care, I worry that these scoring systems inherently discount my own equitable access to care. Individualization of patients’ particular abilities in the context of these scales must occur alongside evidence-based, guideline-directed management via these scoring systems.

Conclusion: Future Orientation

Updated CSC guidelines have accounted for the unique considerations of disabled patients by effectively caveating their scoring algorithms, directing clinicians via disclaimers to uniquely consider their disabled patients in clinical judgement. This is a first step, but it is also one that erodes the value of algorithms, which generally obviate more deliberative thinking and individualization. For our patients who lack certain abilities, as CSC continue to be activated in several states, we have an opportunity to pursue more inherently equitable solutions before further suffering accrues.²⁹ By way of example, adaptations to scoring systems that leverage QALYs for value-based drug pricing indices have been proposed by organizations like the Institute for Clinical and Economic Review, which proposed the Equal-Value-of Life-Years-Gained framework to inform QALY-based arbitration of drug pricing.³⁰ This is not a perfect rubric but instead represents an attempt to balance consideration of drugs, as has been done with ventilators during the pandemic, as a scare and expensive resource while addressing the just concerns of advocacy groups in structural ableism.

Resource stewardship during a crisis should not discount those states of human life that are perceived to be less desirable, particularly if they are not experienced as less desirable but are experienced uniquely. Instead, we should consider equitably measuring our intervention to match a patient’s needs, as we would dose-adjust a medication for renal function or consider minimally invasive procedures for multimorbid patients. COVID-19 has reflected our profession’s ethical adaptation during crisis as resources have become scarce; there is no better time to define solutions for health equity. We should now be concerned equally by the influence our personal biases have on our clinical practice and by the way in which these crisis standards will influence patients’ perception of and trust in their care providers during periods of perceived plentiful resources in the future. Health care resources are always limited, allocated according to societal values; if we value health equity for people of all abilities, then we will consider these abilities equitably as we pursue new standards for health care delivery.

Corresponding author: Gregory D. Snyder, MD, MBA, 2014 Washington Street, Newton, MA 02462; [email protected].

Disclosures: None.
 

From the Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, CA (Drs. Chow and Chang, Mazaya Soundara), University of California Irvine School of Medicine, Irvine, CA (Ruchi Desai), Division of Infectious Diseases, University of California, Irvine, CA (Dr. Gohil), and the Department of Medicine and Hospital Medicine Program, University of California, Irvine, CA (Dr. Amin).

Background: The COVID-19 pandemic has placed substantial strain on hospital resources and has been responsible for more than 733 000 deaths in the United States. The US Food and Drug Administration has granted emergency use authorization (EUA) for monoclonal antibody (mAb) therapy in the US for patients with early-stage high-risk COVID-19.

Methods: In this retrospective cohort study, we studied the emergency department (ED) during a massive COVID-19 surge in Orange County, California, from December 4, 2020, to January 29, 2021, as a potential setting for efficient mAb delivery by evaluating the impact of bamlanivimab use in high-risk COVID-19 patients. All patients included in this study had positive results on nucleic acid amplification detection from nasopharyngeal or throat swabs, presented with 1 or more mild or moderate symptom, and met EUA criteria for mAb treatment. The primary outcome analyzed among this cohort of ED patients was overall improvement, which included subsequent ED/hospital visits, inpatient hospitalization, and death related to COVID-19.

Results: We identified 1278 ED patients with COVID-19 not treated with bamlanivimab and 73 patients with COVID-19 treated with bamlanivimab during the treatment period. Of these patients, 239 control patients and 63 treatment patients met EUA criteria. Overall, 7.9% (5/63) of patients receiving bamlanivimab had a subsequent ED/hospital visit, hospitalization, or death compared with 19.2% (46/239) in the control group (P = .03).

Conclusion: Targeting ED patients for mAb treatment may be an effective strategy to prevent progression to severe COVID-19 illness and substantially reduce the composite end point of repeat ED visits, hospitalizations, and deaths, especially for individuals of underserved populations who may not have access to ambulatory care.

Keywords: COVID-19; mAb; bamlanivimab; surge management.

Since December 2019, the novel pathogen SARS-CoV-2 has spread rapidly, culminating in a pandemic that has caused more than 4.9 million deaths worldwide and claimed more than 733 000 lives in the United States.¹ The scale of the COVID-19 pandemic has placed an immense strain on hospital resources, including personal protective equipment (PPE), beds, ventilators and personnel.^2,3 A previous analysis demonstrated that hospital capacity strain is associated with increased mortality and worsened health outcomes.⁴ A more recent analysis in light of the COVID-19 pandemic found that strains on critical care capacity were associated with increased COVID-19 intensive care unit (ICU) mortality.⁵ While more studies are needed to understand the association between hospital resources and COVID-19 mortality, efforts to decrease COVID-19 hospitalizations by early targeted treatment of patients in outpatient and emergency department (ED) settings may help to relieve the burden on hospital personnel and resources and decrease subsequent mortality.

Current therapeutic options focus on inpatient management of patients who progress to acute respiratory illness while patients with mild presentations are managed with outpatient monitoring, even those at high risk for progression. At the moment, only remdesivir, a viral RNA-dependent RNA polymerase inhibitor, has been approved by the US Food and Drug Administration (FDA) for treatment of hospitalized COVID-19 patients.⁶ However, in November 2020, the FDA granted emergency use authorization (EUA) for monoclonal antibodies (mAbs), monotherapy, and combination therapy in a broad range of early-stage, high-risk patients.^7-9 Neutralizing mAbs include bamlanivimab (LY-CoV555), etesevimab (LY-CoV016), sotrovimab (VIR-7831), and casirivimab/imdevimab (REGN-COV2). These anti–spike protein antibodies prevent viral attachment to the human angiotensin-converting enzyme 2 receptor (hACE2) and subsequently prevent viral entry.¹⁰ mAb therapy has been shown to be effective in substantially reducing viral load, hospitalizations, and ED visits.¹¹

Despite these promising results, uptake of mAb therapy has been slow, with more than 600 000 available doses remaining unused as of mid-January 2021, despite very high infection rates across the United States.¹² In addition to the logistical challenges associated with intravenous (IV) therapy in the ambulatory setting, identifying, notifying, and scheduling appointments for ambulatory patients hamper efficient delivery to high-risk patients and limit access to underserved patients without primary care providers. For patients not treated in the ambulatory setting, the ED may serve as an ideal location for early implementation of mAb treatment in high-risk patients with mild to moderate COVID-19.

The University of California, Irvine (UCI) Medical Center is not only the major premium academic medical center in Orange County, California, but also the primary safety net hospital for vulnerable populations in Orange County. During the surge period from December 2020 through January 2021, we were over 100% capacity and had built an onsite mobile hospital to expand the number of beds available. Given the severity of the impact of COVID-19 on our resources, implementing a strategy to reduce hospital admissions, patient death, and subsequent ED visits was imperative. Our goal was to implement a strategy on the front end through the ED to optimize care for patients and reduce the strain on hospital resources.

We sought to study the ED during this massive surge as a potential setting for efficient mAb delivery by evaluating the impact of bamlanivimab use in high risk COVID-19 patients.

Methods

We conducted a retrospective cohort study (approved by UCI institutional review board) of sequential COVID-19 adult patients who were evaluated and discharged from the ED between December 4, 2020, and January 29, 2021, and received bamlanivimab treatment (cases) compared with a nontreatment group (control) of ED patients.

Using the UCI electronic medical record (EMR) system, we identified 1278 ED patients with COVID-19 not treated with bamlanivimab and 73 patients with COVID-19 treated with bamlanivimab during the months of December 2020 and January 2021. All patients included in this study met the EUA criteria for mAb therapy. According to the Centers for Disease Control and Prevention (CDC), during the period of this study, patients met EUA criteria if they had mild to moderate COVID-19, a positive direct SARS-CoV-2 viral testing, and a high risk for progressing to severe COVID-19 or hospitalization.¹³ High risk for progressing to severe COVID-19 and/or hospitalization is defined as meeting at least 1 of the following criteria: a body mass index of 35 or higher, chronic kidney disease (CKD), diabetes, immunosuppressive disease, currently receiving immunosuppressive treatment, aged 65 years or older, aged 55 years or older and have cardiovascular disease or hypertension, or chronic obstructive pulmonary disease (COPD)/other chronic respiratory diseases.¹³ All patients in the ED who met EUA criteria were offered mAb treatment; those who accepted the treatment were included in the treatment group, and those who refused were included in the control group.

All patients included in this study had positive results on nucleic acid amplification detection from nasopharyngeal or throat swabs and presented with 1 or more mild or moderate symptom, defined as: fever, cough, sore throat, malaise, headache, muscle pain, gastrointestinal symptoms, or shortness of breath. We excluded patients admitted to the hospital on that ED visit and those discharged to hospice. In addition, we excluded patients who presented 2 weeks after symptom onset and those who did not meet EUA criteria. Demographic data (age and gender) and comorbid conditions were obtained by EMR review. Comorbid conditions obtained included diabetes, hypertension, cardiovascular disease, coronary artery disease, CKD/end-stage renal disease (ESRD), COPD, obesity, and immunocompromised status.

Bamlanivimab infusion therapy in the ED followed CDC guidelines. Each patient received 700 mg of bamlanivimab diluted in 0.9% sodium chloride and administered as a single IV infusion. We established protocols to give patients IV immunoglobulin (IVIG) infusions directly in the ED.

The primary outcome analyzed among this cohort of ED patients was overall improvement, which included subsequent ED/hospital visits, inpatient hospitalization, and death related to COVID-19 within 90 days of initial ED visit. Each patient was only counted once. Data analysis and statistical tests were conducted using SPSS statistical software (SPSS Inc). Treatment effects were compared using χ² test with an α level of 0.05. A t test was used for continuous variables, including age. A P value of less than .05 was considered significant.

Results

We screened a total of 1351 patients with COVID-19. Of these, 1278 patients did not receive treatment with bamlanivimab. Two hundred thirty-nine patients met inclusion criteria and were included in the control group. Seventy-three patients were treated with bamlanivimab in the ED; 63 of these patients met EUA criteria and comprised the treatment group (Figure 1).

Demographic details of the trial groups are provided in Table 1. The median age of the treatment group was 61 years (interquartile range [IQR], 55-73), while the median age of the control group was 57 years (IQR, 48-68). The difference in median age between the treatment and control individuals was significantly different (P = .03). There was no significant difference found in terms of gender between the control and treatment groups (P = .07). In addition, no significant difference was seen among racial and ethnic groups in the control and treatment groups. Comorbidities and demographics of all patients in the treatment and control groups are provided in Table 1. The only comorbidity that was found to be significantly different between the treatment and control groups was CKD/ESRD. Among those treated with bamlanivimab, 20.6% (13/63) had CKD/ESRD compared with 10.5% (25/239) in the control group (P = .02).

Overall, 7.9% (5/63) of patients receiving bamlanivimab had a subsequent ED/hospital visit, hospitalization, or death compared with 19.2% (46/239) in the control group (P = .03) (Table 2).

While the primary outcome of overall improvement was significantly different between the 2 groups, comparison of the individual components, including subsequent ED visits, hospitalizations, or death, were not significant. No treatment patients were hospitalized, compared with 5.4% (13/239) in the control group (P = .05). In the treatment group, 6.3% (4/63) returned to the ED compared with 12.6% (30/239) of the control group (P = .17). Finally, 1.6% (1/63) of the treatment group had a subsequent death that was due to COVID-19 compared with 1.3% (3/239) in the control group (P = .84) (Figure 2).

Discussion

In this retrospective cohort study, we observed a significant difference in rates of COVID-19 patients requiring repeat ED visits, hospitalizations, and deaths among those who received bamlanivimab compared with those who did not. Our study focused on high-risk patients with mild or moderate COVID-19, a unique subset of individuals who would normally be followed and treated via outpatient monitoring. We propose that treating high-risk patients earlier in their disease process with mAb therapy can have a major impact on overall outcomes, as defined by decreased subsequent hospitalizations, ED visits, and death.

Compared to clinical trials such as BLAZE-1 or REGN-COV2, every patient in this trial had at least 1 high-risk characteristic.^9,11 This may explain why a greater proportion of our patients in both the control and treatment groups had subsequent hospitalization, ED visits, and deaths. COVID-19 patients seen in the ED may be a uniquely self-selected population of individuals likely to benefit from mAb therapy since they may be more likely to be sicker, have more comorbidities, or have less readily available primary care access for testing and treatment.¹⁴

Despite conducting a thorough literature review, we were unable to find any similar studies describing the ED as an appropriate setting for mAb treatment in patients with COVID-19. Multiple studies have used outpatient clinics as a setting for mAb treatment, and 1 retrospective analysis found that neutralizing mAb treatment in COVID-19 patients in an outpatient setting reduced hospital utilization.¹⁵ However, many Americans do not have access to primary care, with 1 study finding that only 75% of Americans had an identified source of primary care in 2015.¹⁶ Obstacles to primary care access include disabilities, lack of health insurance, language-related barriers, race/ethnicity, and homelessness.¹⁷ Barriers to access for primary care services and timely care make these populations more likely to frequent the ED.¹⁷ This makes the ED a unique location for early and targeted treatment of COVID-19 patients with a high risk for progression to severe COVID-19.

During surge periods in the COVID-19 pandemic, many hospitals met capacity or superseded their capacity for patients, with 4423 hospitals reporting more than 90% of hospital beds occupied and 2591 reporting more than 90% of ICU beds occupied during the peak surge week of January 1, 2021, to January 7, 2021.¹⁸ The main goals of lockdowns and masking have been to decrease the transmission of COVID-19 and hopefully flatten the curve to alleviate the burden on hospitals and decrease patient mortality. However, in surge situations when hospitals have already been pushed to their limits, we need to find ways to circumvent these shortages. This was particularly true at our academic medical center during the surge period of December 2020 through January 2021, necessitating the need for an innovative approach to improve patient outcomes and reduce the strain on resources. Utilizing the ED and implementing early treatment strategies with mAbs, especially during a surge crisis, can decrease severity of illness, hospitalizations, and deaths, as demonstrated in our article.

This study had several limitations. First, it is plausible that some ED patients may have gone to a different hospital after discharge from the UCI ED rather than returning to our institution. Given the constraints of using the EMR, we were only able to assess hospitalizations and subsequent ED visits at UCI. Second, there were 2 confounding variables identified when analyzing the demographic differences between the control and treatment group among those who met EUA criteria. The median age among those in the treatment group was greater than those in the control group (P = .03), and the proportion of individuals with CKD/ESRD was also greater in those in the treatment group (P = .02). It is well known that older patients and those with renal disease have higher incidences of morbidity and mortality. Achieving statistically significant differences overall between control and treatment groups despite greater numbers of older individuals and patients with renal disease in the treatment group supports our strategy and the usage of mAb.^19,20

Finally, as of April 16, 2021, the FDA revoked EUA for bamlanivimab when administered alone. However, alternative mAb therapies remain available under the EUA, including REGEN-COV (casirivimab and imdevimab), sotrovimab, and the combination therapy of bamlanivimab and etesevimab.²¹ This decision was made in light of the increased frequency of resistant variants of SARS-CoV-2 with bamlanivimab treatment alone.²¹ Our study was conducted prior to this announcement. However, as treatment with other mAbs is still permissible, we believe our findings can translate to treatment with mAbs in general. In fact, combination therapy with bamlanivimab and etesevimab has been found to be more effective than monotherapy alone, suggesting that our results may be even more robust with combination mAb therapy.¹¹ Overall, while additional studies are needed with larger sample sizes and combination mAb treatment to fully elucidate the impact of administering mAb treatment in the ED, our results suggest that targeting ED patients for mAb treatment may be an effective strategy to prevent the composite end point of repeat ED visits, hospitalizations, or deaths.

Conclusion

Targeting ED patients for mAb treatment may be an effective strategy to prevent progression to severe COVID-19 illness and substantially reduce the composite end point of repeat ED visits, hospitalizations, and deaths, especially for individuals of underserved populations who may not have access to ambulatory care.

Corresponding author: Alpesh Amin, MD, MBA, Department of Medicine and Hospital Medicine Program, University of California, Irvine, 333 City Tower West, Ste 500, Orange, CA 92868; [email protected].

Financial disclosures: This manuscript was generously supported by multiple donors, including the Mehra Family, the Yang Family, and the Chao Family. Dr. Amin reported serving as Principal Investigator or Co-Investigator of clinical trials sponsored by NIH/NIAID, NeuroRX Pharma, Pulmotect, Blade Therapeutics, Novartis, Takeda, Humanigen, Eli Lilly, PTC Therapeutics, OctaPharma, Fulcrum Therapeutics, and Alexion, unrelated to the present study. He has served as speaker and/or consultant for BMS, Pfizer, BI, Portola, Sunovion, Mylan, Salix, Alexion, AstraZeneca, Novartis, Nabriva, Paratek, Bayer, Tetraphase, Achaogen La Jolla, Ferring, Seres, Millennium, PeraHealth, HeartRite, Aseptiscope, and Sprightly, unrelated to the present study.

From the Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, CA (Drs. Chow and Chang, Mazaya Soundara), University of California Irvine School of Medicine, Irvine, CA (Ruchi Desai), Division of Infectious Diseases, University of California, Irvine, CA (Dr. Gohil), and the Department of Medicine and Hospital Medicine Program, University of California, Irvine, CA (Dr. Amin).

Background: The COVID-19 pandemic has placed substantial strain on hospital resources and has been responsible for more than 733 000 deaths in the United States. The US Food and Drug Administration has granted emergency use authorization (EUA) for monoclonal antibody (mAb) therapy in the US for patients with early-stage high-risk COVID-19.

Methods: In this retrospective cohort study, we studied the emergency department (ED) during a massive COVID-19 surge in Orange County, California, from December 4, 2020, to January 29, 2021, as a potential setting for efficient mAb delivery by evaluating the impact of bamlanivimab use in high-risk COVID-19 patients. All patients included in this study had positive results on nucleic acid amplification detection from nasopharyngeal or throat swabs, presented with 1 or more mild or moderate symptom, and met EUA criteria for mAb treatment. The primary outcome analyzed among this cohort of ED patients was overall improvement, which included subsequent ED/hospital visits, inpatient hospitalization, and death related to COVID-19.

Results: We identified 1278 ED patients with COVID-19 not treated with bamlanivimab and 73 patients with COVID-19 treated with bamlanivimab during the treatment period. Of these patients, 239 control patients and 63 treatment patients met EUA criteria. Overall, 7.9% (5/63) of patients receiving bamlanivimab had a subsequent ED/hospital visit, hospitalization, or death compared with 19.2% (46/239) in the control group (P = .03).

Conclusion: Targeting ED patients for mAb treatment may be an effective strategy to prevent progression to severe COVID-19 illness and substantially reduce the composite end point of repeat ED visits, hospitalizations, and deaths, especially for individuals of underserved populations who may not have access to ambulatory care.

Keywords: COVID-19; mAb; bamlanivimab; surge management.

Since December 2019, the novel pathogen SARS-CoV-2 has spread rapidly, culminating in a pandemic that has caused more than 4.9 million deaths worldwide and claimed more than 733 000 lives in the United States.¹ The scale of the COVID-19 pandemic has placed an immense strain on hospital resources, including personal protective equipment (PPE), beds, ventilators and personnel.^2,3 A previous analysis demonstrated that hospital capacity strain is associated with increased mortality and worsened health outcomes.⁴ A more recent analysis in light of the COVID-19 pandemic found that strains on critical care capacity were associated with increased COVID-19 intensive care unit (ICU) mortality.⁵ While more studies are needed to understand the association between hospital resources and COVID-19 mortality, efforts to decrease COVID-19 hospitalizations by early targeted treatment of patients in outpatient and emergency department (ED) settings may help to relieve the burden on hospital personnel and resources and decrease subsequent mortality.

Current therapeutic options focus on inpatient management of patients who progress to acute respiratory illness while patients with mild presentations are managed with outpatient monitoring, even those at high risk for progression. At the moment, only remdesivir, a viral RNA-dependent RNA polymerase inhibitor, has been approved by the US Food and Drug Administration (FDA) for treatment of hospitalized COVID-19 patients.⁶ However, in November 2020, the FDA granted emergency use authorization (EUA) for monoclonal antibodies (mAbs), monotherapy, and combination therapy in a broad range of early-stage, high-risk patients.^7-9 Neutralizing mAbs include bamlanivimab (LY-CoV555), etesevimab (LY-CoV016), sotrovimab (VIR-7831), and casirivimab/imdevimab (REGN-COV2). These anti–spike protein antibodies prevent viral attachment to the human angiotensin-converting enzyme 2 receptor (hACE2) and subsequently prevent viral entry.¹⁰ mAb therapy has been shown to be effective in substantially reducing viral load, hospitalizations, and ED visits.¹¹

Despite these promising results, uptake of mAb therapy has been slow, with more than 600 000 available doses remaining unused as of mid-January 2021, despite very high infection rates across the United States.¹² In addition to the logistical challenges associated with intravenous (IV) therapy in the ambulatory setting, identifying, notifying, and scheduling appointments for ambulatory patients hamper efficient delivery to high-risk patients and limit access to underserved patients without primary care providers. For patients not treated in the ambulatory setting, the ED may serve as an ideal location for early implementation of mAb treatment in high-risk patients with mild to moderate COVID-19.

The University of California, Irvine (UCI) Medical Center is not only the major premium academic medical center in Orange County, California, but also the primary safety net hospital for vulnerable populations in Orange County. During the surge period from December 2020 through January 2021, we were over 100% capacity and had built an onsite mobile hospital to expand the number of beds available. Given the severity of the impact of COVID-19 on our resources, implementing a strategy to reduce hospital admissions, patient death, and subsequent ED visits was imperative. Our goal was to implement a strategy on the front end through the ED to optimize care for patients and reduce the strain on hospital resources.

We sought to study the ED during this massive surge as a potential setting for efficient mAb delivery by evaluating the impact of bamlanivimab use in high risk COVID-19 patients.

Methods

We conducted a retrospective cohort study (approved by UCI institutional review board) of sequential COVID-19 adult patients who were evaluated and discharged from the ED between December 4, 2020, and January 29, 2021, and received bamlanivimab treatment (cases) compared with a nontreatment group (control) of ED patients.

Using the UCI electronic medical record (EMR) system, we identified 1278 ED patients with COVID-19 not treated with bamlanivimab and 73 patients with COVID-19 treated with bamlanivimab during the months of December 2020 and January 2021. All patients included in this study met the EUA criteria for mAb therapy. According to the Centers for Disease Control and Prevention (CDC), during the period of this study, patients met EUA criteria if they had mild to moderate COVID-19, a positive direct SARS-CoV-2 viral testing, and a high risk for progressing to severe COVID-19 or hospitalization.¹³ High risk for progressing to severe COVID-19 and/or hospitalization is defined as meeting at least 1 of the following criteria: a body mass index of 35 or higher, chronic kidney disease (CKD), diabetes, immunosuppressive disease, currently receiving immunosuppressive treatment, aged 65 years or older, aged 55 years or older and have cardiovascular disease or hypertension, or chronic obstructive pulmonary disease (COPD)/other chronic respiratory diseases.¹³ All patients in the ED who met EUA criteria were offered mAb treatment; those who accepted the treatment were included in the treatment group, and those who refused were included in the control group.

All patients included in this study had positive results on nucleic acid amplification detection from nasopharyngeal or throat swabs and presented with 1 or more mild or moderate symptom, defined as: fever, cough, sore throat, malaise, headache, muscle pain, gastrointestinal symptoms, or shortness of breath. We excluded patients admitted to the hospital on that ED visit and those discharged to hospice. In addition, we excluded patients who presented 2 weeks after symptom onset and those who did not meet EUA criteria. Demographic data (age and gender) and comorbid conditions were obtained by EMR review. Comorbid conditions obtained included diabetes, hypertension, cardiovascular disease, coronary artery disease, CKD/end-stage renal disease (ESRD), COPD, obesity, and immunocompromised status.

Bamlanivimab infusion therapy in the ED followed CDC guidelines. Each patient received 700 mg of bamlanivimab diluted in 0.9% sodium chloride and administered as a single IV infusion. We established protocols to give patients IV immunoglobulin (IVIG) infusions directly in the ED.

The primary outcome analyzed among this cohort of ED patients was overall improvement, which included subsequent ED/hospital visits, inpatient hospitalization, and death related to COVID-19 within 90 days of initial ED visit. Each patient was only counted once. Data analysis and statistical tests were conducted using SPSS statistical software (SPSS Inc). Treatment effects were compared using χ² test with an α level of 0.05. A t test was used for continuous variables, including age. A P value of less than .05 was considered significant.

Results

We screened a total of 1351 patients with COVID-19. Of these, 1278 patients did not receive treatment with bamlanivimab. Two hundred thirty-nine patients met inclusion criteria and were included in the control group. Seventy-three patients were treated with bamlanivimab in the ED; 63 of these patients met EUA criteria and comprised the treatment group (Figure 1).

Demographic details of the trial groups are provided in Table 1. The median age of the treatment group was 61 years (interquartile range [IQR], 55-73), while the median age of the control group was 57 years (IQR, 48-68). The difference in median age between the treatment and control individuals was significantly different (P = .03). There was no significant difference found in terms of gender between the control and treatment groups (P = .07). In addition, no significant difference was seen among racial and ethnic groups in the control and treatment groups. Comorbidities and demographics of all patients in the treatment and control groups are provided in Table 1. The only comorbidity that was found to be significantly different between the treatment and control groups was CKD/ESRD. Among those treated with bamlanivimab, 20.6% (13/63) had CKD/ESRD compared with 10.5% (25/239) in the control group (P = .02).

Overall, 7.9% (5/63) of patients receiving bamlanivimab had a subsequent ED/hospital visit, hospitalization, or death compared with 19.2% (46/239) in the control group (P = .03) (Table 2).

While the primary outcome of overall improvement was significantly different between the 2 groups, comparison of the individual components, including subsequent ED visits, hospitalizations, or death, were not significant. No treatment patients were hospitalized, compared with 5.4% (13/239) in the control group (P = .05). In the treatment group, 6.3% (4/63) returned to the ED compared with 12.6% (30/239) of the control group (P = .17). Finally, 1.6% (1/63) of the treatment group had a subsequent death that was due to COVID-19 compared with 1.3% (3/239) in the control group (P = .84) (Figure 2).

Discussion

In this retrospective cohort study, we observed a significant difference in rates of COVID-19 patients requiring repeat ED visits, hospitalizations, and deaths among those who received bamlanivimab compared with those who did not. Our study focused on high-risk patients with mild or moderate COVID-19, a unique subset of individuals who would normally be followed and treated via outpatient monitoring. We propose that treating high-risk patients earlier in their disease process with mAb therapy can have a major impact on overall outcomes, as defined by decreased subsequent hospitalizations, ED visits, and death.

Compared to clinical trials such as BLAZE-1 or REGN-COV2, every patient in this trial had at least 1 high-risk characteristic.^9,11 This may explain why a greater proportion of our patients in both the control and treatment groups had subsequent hospitalization, ED visits, and deaths. COVID-19 patients seen in the ED may be a uniquely self-selected population of individuals likely to benefit from mAb therapy since they may be more likely to be sicker, have more comorbidities, or have less readily available primary care access for testing and treatment.¹⁴

Despite conducting a thorough literature review, we were unable to find any similar studies describing the ED as an appropriate setting for mAb treatment in patients with COVID-19. Multiple studies have used outpatient clinics as a setting for mAb treatment, and 1 retrospective analysis found that neutralizing mAb treatment in COVID-19 patients in an outpatient setting reduced hospital utilization.¹⁵ However, many Americans do not have access to primary care, with 1 study finding that only 75% of Americans had an identified source of primary care in 2015.¹⁶ Obstacles to primary care access include disabilities, lack of health insurance, language-related barriers, race/ethnicity, and homelessness.¹⁷ Barriers to access for primary care services and timely care make these populations more likely to frequent the ED.¹⁷ This makes the ED a unique location for early and targeted treatment of COVID-19 patients with a high risk for progression to severe COVID-19.

During surge periods in the COVID-19 pandemic, many hospitals met capacity or superseded their capacity for patients, with 4423 hospitals reporting more than 90% of hospital beds occupied and 2591 reporting more than 90% of ICU beds occupied during the peak surge week of January 1, 2021, to January 7, 2021.¹⁸ The main goals of lockdowns and masking have been to decrease the transmission of COVID-19 and hopefully flatten the curve to alleviate the burden on hospitals and decrease patient mortality. However, in surge situations when hospitals have already been pushed to their limits, we need to find ways to circumvent these shortages. This was particularly true at our academic medical center during the surge period of December 2020 through January 2021, necessitating the need for an innovative approach to improve patient outcomes and reduce the strain on resources. Utilizing the ED and implementing early treatment strategies with mAbs, especially during a surge crisis, can decrease severity of illness, hospitalizations, and deaths, as demonstrated in our article.

This study had several limitations. First, it is plausible that some ED patients may have gone to a different hospital after discharge from the UCI ED rather than returning to our institution. Given the constraints of using the EMR, we were only able to assess hospitalizations and subsequent ED visits at UCI. Second, there were 2 confounding variables identified when analyzing the demographic differences between the control and treatment group among those who met EUA criteria. The median age among those in the treatment group was greater than those in the control group (P = .03), and the proportion of individuals with CKD/ESRD was also greater in those in the treatment group (P = .02). It is well known that older patients and those with renal disease have higher incidences of morbidity and mortality. Achieving statistically significant differences overall between control and treatment groups despite greater numbers of older individuals and patients with renal disease in the treatment group supports our strategy and the usage of mAb.^19,20

Finally, as of April 16, 2021, the FDA revoked EUA for bamlanivimab when administered alone. However, alternative mAb therapies remain available under the EUA, including REGEN-COV (casirivimab and imdevimab), sotrovimab, and the combination therapy of bamlanivimab and etesevimab.²¹ This decision was made in light of the increased frequency of resistant variants of SARS-CoV-2 with bamlanivimab treatment alone.²¹ Our study was conducted prior to this announcement. However, as treatment with other mAbs is still permissible, we believe our findings can translate to treatment with mAbs in general. In fact, combination therapy with bamlanivimab and etesevimab has been found to be more effective than monotherapy alone, suggesting that our results may be even more robust with combination mAb therapy.¹¹ Overall, while additional studies are needed with larger sample sizes and combination mAb treatment to fully elucidate the impact of administering mAb treatment in the ED, our results suggest that targeting ED patients for mAb treatment may be an effective strategy to prevent the composite end point of repeat ED visits, hospitalizations, or deaths.

Conclusion

Targeting ED patients for mAb treatment may be an effective strategy to prevent progression to severe COVID-19 illness and substantially reduce the composite end point of repeat ED visits, hospitalizations, and deaths, especially for individuals of underserved populations who may not have access to ambulatory care.

Corresponding author: Alpesh Amin, MD, MBA, Department of Medicine and Hospital Medicine Program, University of California, Irvine, 333 City Tower West, Ste 500, Orange, CA 92868; [email protected].

Financial disclosures: This manuscript was generously supported by multiple donors, including the Mehra Family, the Yang Family, and the Chao Family. Dr. Amin reported serving as Principal Investigator or Co-Investigator of clinical trials sponsored by NIH/NIAID, NeuroRX Pharma, Pulmotect, Blade Therapeutics, Novartis, Takeda, Humanigen, Eli Lilly, PTC Therapeutics, OctaPharma, Fulcrum Therapeutics, and Alexion, unrelated to the present study. He has served as speaker and/or consultant for BMS, Pfizer, BI, Portola, Sunovion, Mylan, Salix, Alexion, AstraZeneca, Novartis, Nabriva, Paratek, Bayer, Tetraphase, Achaogen La Jolla, Ferring, Seres, Millennium, PeraHealth, HeartRite, Aseptiscope, and Sprightly, unrelated to the present study.

From the Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, CA (Drs. Chow and Chang, Mazaya Soundara), University of California Irvine School of Medicine, Irvine, CA (Ruchi Desai), Division of Infectious Diseases, University of California, Irvine, CA (Dr. Gohil), and the Department of Medicine and Hospital Medicine Program, University of California, Irvine, CA (Dr. Amin).

Background: The COVID-19 pandemic has placed substantial strain on hospital resources and has been responsible for more than 733 000 deaths in the United States. The US Food and Drug Administration has granted emergency use authorization (EUA) for monoclonal antibody (mAb) therapy in the US for patients with early-stage high-risk COVID-19.

Methods: In this retrospective cohort study, we studied the emergency department (ED) during a massive COVID-19 surge in Orange County, California, from December 4, 2020, to January 29, 2021, as a potential setting for efficient mAb delivery by evaluating the impact of bamlanivimab use in high-risk COVID-19 patients. All patients included in this study had positive results on nucleic acid amplification detection from nasopharyngeal or throat swabs, presented with 1 or more mild or moderate symptom, and met EUA criteria for mAb treatment. The primary outcome analyzed among this cohort of ED patients was overall improvement, which included subsequent ED/hospital visits, inpatient hospitalization, and death related to COVID-19.

Results: We identified 1278 ED patients with COVID-19 not treated with bamlanivimab and 73 patients with COVID-19 treated with bamlanivimab during the treatment period. Of these patients, 239 control patients and 63 treatment patients met EUA criteria. Overall, 7.9% (5/63) of patients receiving bamlanivimab had a subsequent ED/hospital visit, hospitalization, or death compared with 19.2% (46/239) in the control group (P = .03).

Conclusion: Targeting ED patients for mAb treatment may be an effective strategy to prevent progression to severe COVID-19 illness and substantially reduce the composite end point of repeat ED visits, hospitalizations, and deaths, especially for individuals of underserved populations who may not have access to ambulatory care.

Keywords: COVID-19; mAb; bamlanivimab; surge management.

Since December 2019, the novel pathogen SARS-CoV-2 has spread rapidly, culminating in a pandemic that has caused more than 4.9 million deaths worldwide and claimed more than 733 000 lives in the United States.¹ The scale of the COVID-19 pandemic has placed an immense strain on hospital resources, including personal protective equipment (PPE), beds, ventilators and personnel.^2,3 A previous analysis demonstrated that hospital capacity strain is associated with increased mortality and worsened health outcomes.⁴ A more recent analysis in light of the COVID-19 pandemic found that strains on critical care capacity were associated with increased COVID-19 intensive care unit (ICU) mortality.⁵ While more studies are needed to understand the association between hospital resources and COVID-19 mortality, efforts to decrease COVID-19 hospitalizations by early targeted treatment of patients in outpatient and emergency department (ED) settings may help to relieve the burden on hospital personnel and resources and decrease subsequent mortality.

Current therapeutic options focus on inpatient management of patients who progress to acute respiratory illness while patients with mild presentations are managed with outpatient monitoring, even those at high risk for progression. At the moment, only remdesivir, a viral RNA-dependent RNA polymerase inhibitor, has been approved by the US Food and Drug Administration (FDA) for treatment of hospitalized COVID-19 patients.⁶ However, in November 2020, the FDA granted emergency use authorization (EUA) for monoclonal antibodies (mAbs), monotherapy, and combination therapy in a broad range of early-stage, high-risk patients.^7-9 Neutralizing mAbs include bamlanivimab (LY-CoV555), etesevimab (LY-CoV016), sotrovimab (VIR-7831), and casirivimab/imdevimab (REGN-COV2). These anti–spike protein antibodies prevent viral attachment to the human angiotensin-converting enzyme 2 receptor (hACE2) and subsequently prevent viral entry.¹⁰ mAb therapy has been shown to be effective in substantially reducing viral load, hospitalizations, and ED visits.¹¹

Despite these promising results, uptake of mAb therapy has been slow, with more than 600 000 available doses remaining unused as of mid-January 2021, despite very high infection rates across the United States.¹² In addition to the logistical challenges associated with intravenous (IV) therapy in the ambulatory setting, identifying, notifying, and scheduling appointments for ambulatory patients hamper efficient delivery to high-risk patients and limit access to underserved patients without primary care providers. For patients not treated in the ambulatory setting, the ED may serve as an ideal location for early implementation of mAb treatment in high-risk patients with mild to moderate COVID-19.

The University of California, Irvine (UCI) Medical Center is not only the major premium academic medical center in Orange County, California, but also the primary safety net hospital for vulnerable populations in Orange County. During the surge period from December 2020 through January 2021, we were over 100% capacity and had built an onsite mobile hospital to expand the number of beds available. Given the severity of the impact of COVID-19 on our resources, implementing a strategy to reduce hospital admissions, patient death, and subsequent ED visits was imperative. Our goal was to implement a strategy on the front end through the ED to optimize care for patients and reduce the strain on hospital resources.

We sought to study the ED during this massive surge as a potential setting for efficient mAb delivery by evaluating the impact of bamlanivimab use in high risk COVID-19 patients.

Methods

We conducted a retrospective cohort study (approved by UCI institutional review board) of sequential COVID-19 adult patients who were evaluated and discharged from the ED between December 4, 2020, and January 29, 2021, and received bamlanivimab treatment (cases) compared with a nontreatment group (control) of ED patients.

Using the UCI electronic medical record (EMR) system, we identified 1278 ED patients with COVID-19 not treated with bamlanivimab and 73 patients with COVID-19 treated with bamlanivimab during the months of December 2020 and January 2021. All patients included in this study met the EUA criteria for mAb therapy. According to the Centers for Disease Control and Prevention (CDC), during the period of this study, patients met EUA criteria if they had mild to moderate COVID-19, a positive direct SARS-CoV-2 viral testing, and a high risk for progressing to severe COVID-19 or hospitalization.¹³ High risk for progressing to severe COVID-19 and/or hospitalization is defined as meeting at least 1 of the following criteria: a body mass index of 35 or higher, chronic kidney disease (CKD), diabetes, immunosuppressive disease, currently receiving immunosuppressive treatment, aged 65 years or older, aged 55 years or older and have cardiovascular disease or hypertension, or chronic obstructive pulmonary disease (COPD)/other chronic respiratory diseases.¹³ All patients in the ED who met EUA criteria were offered mAb treatment; those who accepted the treatment were included in the treatment group, and those who refused were included in the control group.

All patients included in this study had positive results on nucleic acid amplification detection from nasopharyngeal or throat swabs and presented with 1 or more mild or moderate symptom, defined as: fever, cough, sore throat, malaise, headache, muscle pain, gastrointestinal symptoms, or shortness of breath. We excluded patients admitted to the hospital on that ED visit and those discharged to hospice. In addition, we excluded patients who presented 2 weeks after symptom onset and those who did not meet EUA criteria. Demographic data (age and gender) and comorbid conditions were obtained by EMR review. Comorbid conditions obtained included diabetes, hypertension, cardiovascular disease, coronary artery disease, CKD/end-stage renal disease (ESRD), COPD, obesity, and immunocompromised status.

Bamlanivimab infusion therapy in the ED followed CDC guidelines. Each patient received 700 mg of bamlanivimab diluted in 0.9% sodium chloride and administered as a single IV infusion. We established protocols to give patients IV immunoglobulin (IVIG) infusions directly in the ED.

The primary outcome analyzed among this cohort of ED patients was overall improvement, which included subsequent ED/hospital visits, inpatient hospitalization, and death related to COVID-19 within 90 days of initial ED visit. Each patient was only counted once. Data analysis and statistical tests were conducted using SPSS statistical software (SPSS Inc). Treatment effects were compared using χ² test with an α level of 0.05. A t test was used for continuous variables, including age. A P value of less than .05 was considered significant.

Results

We screened a total of 1351 patients with COVID-19. Of these, 1278 patients did not receive treatment with bamlanivimab. Two hundred thirty-nine patients met inclusion criteria and were included in the control group. Seventy-three patients were treated with bamlanivimab in the ED; 63 of these patients met EUA criteria and comprised the treatment group (Figure 1).

Demographic details of the trial groups are provided in Table 1. The median age of the treatment group was 61 years (interquartile range [IQR], 55-73), while the median age of the control group was 57 years (IQR, 48-68). The difference in median age between the treatment and control individuals was significantly different (P = .03). There was no significant difference found in terms of gender between the control and treatment groups (P = .07). In addition, no significant difference was seen among racial and ethnic groups in the control and treatment groups. Comorbidities and demographics of all patients in the treatment and control groups are provided in Table 1. The only comorbidity that was found to be significantly different between the treatment and control groups was CKD/ESRD. Among those treated with bamlanivimab, 20.6% (13/63) had CKD/ESRD compared with 10.5% (25/239) in the control group (P = .02).

Overall, 7.9% (5/63) of patients receiving bamlanivimab had a subsequent ED/hospital visit, hospitalization, or death compared with 19.2% (46/239) in the control group (P = .03) (Table 2).

While the primary outcome of overall improvement was significantly different between the 2 groups, comparison of the individual components, including subsequent ED visits, hospitalizations, or death, were not significant. No treatment patients were hospitalized, compared with 5.4% (13/239) in the control group (P = .05). In the treatment group, 6.3% (4/63) returned to the ED compared with 12.6% (30/239) of the control group (P = .17). Finally, 1.6% (1/63) of the treatment group had a subsequent death that was due to COVID-19 compared with 1.3% (3/239) in the control group (P = .84) (Figure 2).

Discussion

In this retrospective cohort study, we observed a significant difference in rates of COVID-19 patients requiring repeat ED visits, hospitalizations, and deaths among those who received bamlanivimab compared with those who did not. Our study focused on high-risk patients with mild or moderate COVID-19, a unique subset of individuals who would normally be followed and treated via outpatient monitoring. We propose that treating high-risk patients earlier in their disease process with mAb therapy can have a major impact on overall outcomes, as defined by decreased subsequent hospitalizations, ED visits, and death.

Compared to clinical trials such as BLAZE-1 or REGN-COV2, every patient in this trial had at least 1 high-risk characteristic.^9,11 This may explain why a greater proportion of our patients in both the control and treatment groups had subsequent hospitalization, ED visits, and deaths. COVID-19 patients seen in the ED may be a uniquely self-selected population of individuals likely to benefit from mAb therapy since they may be more likely to be sicker, have more comorbidities, or have less readily available primary care access for testing and treatment.¹⁴

Despite conducting a thorough literature review, we were unable to find any similar studies describing the ED as an appropriate setting for mAb treatment in patients with COVID-19. Multiple studies have used outpatient clinics as a setting for mAb treatment, and 1 retrospective analysis found that neutralizing mAb treatment in COVID-19 patients in an outpatient setting reduced hospital utilization.¹⁵ However, many Americans do not have access to primary care, with 1 study finding that only 75% of Americans had an identified source of primary care in 2015.¹⁶ Obstacles to primary care access include disabilities, lack of health insurance, language-related barriers, race/ethnicity, and homelessness.¹⁷ Barriers to access for primary care services and timely care make these populations more likely to frequent the ED.¹⁷ This makes the ED a unique location for early and targeted treatment of COVID-19 patients with a high risk for progression to severe COVID-19.

During surge periods in the COVID-19 pandemic, many hospitals met capacity or superseded their capacity for patients, with 4423 hospitals reporting more than 90% of hospital beds occupied and 2591 reporting more than 90% of ICU beds occupied during the peak surge week of January 1, 2021, to January 7, 2021.¹⁸ The main goals of lockdowns and masking have been to decrease the transmission of COVID-19 and hopefully flatten the curve to alleviate the burden on hospitals and decrease patient mortality. However, in surge situations when hospitals have already been pushed to their limits, we need to find ways to circumvent these shortages. This was particularly true at our academic medical center during the surge period of December 2020 through January 2021, necessitating the need for an innovative approach to improve patient outcomes and reduce the strain on resources. Utilizing the ED and implementing early treatment strategies with mAbs, especially during a surge crisis, can decrease severity of illness, hospitalizations, and deaths, as demonstrated in our article.

This study had several limitations. First, it is plausible that some ED patients may have gone to a different hospital after discharge from the UCI ED rather than returning to our institution. Given the constraints of using the EMR, we were only able to assess hospitalizations and subsequent ED visits at UCI. Second, there were 2 confounding variables identified when analyzing the demographic differences between the control and treatment group among those who met EUA criteria. The median age among those in the treatment group was greater than those in the control group (P = .03), and the proportion of individuals with CKD/ESRD was also greater in those in the treatment group (P = .02). It is well known that older patients and those with renal disease have higher incidences of morbidity and mortality. Achieving statistically significant differences overall between control and treatment groups despite greater numbers of older individuals and patients with renal disease in the treatment group supports our strategy and the usage of mAb.^19,20

Finally, as of April 16, 2021, the FDA revoked EUA for bamlanivimab when administered alone. However, alternative mAb therapies remain available under the EUA, including REGEN-COV (casirivimab and imdevimab), sotrovimab, and the combination therapy of bamlanivimab and etesevimab.²¹ This decision was made in light of the increased frequency of resistant variants of SARS-CoV-2 with bamlanivimab treatment alone.²¹ Our study was conducted prior to this announcement. However, as treatment with other mAbs is still permissible, we believe our findings can translate to treatment with mAbs in general. In fact, combination therapy with bamlanivimab and etesevimab has been found to be more effective than monotherapy alone, suggesting that our results may be even more robust with combination mAb therapy.¹¹ Overall, while additional studies are needed with larger sample sizes and combination mAb treatment to fully elucidate the impact of administering mAb treatment in the ED, our results suggest that targeting ED patients for mAb treatment may be an effective strategy to prevent the composite end point of repeat ED visits, hospitalizations, or deaths.

Conclusion

Targeting ED patients for mAb treatment may be an effective strategy to prevent progression to severe COVID-19 illness and substantially reduce the composite end point of repeat ED visits, hospitalizations, and deaths, especially for individuals of underserved populations who may not have access to ambulatory care.

Corresponding author: Alpesh Amin, MD, MBA, Department of Medicine and Hospital Medicine Program, University of California, Irvine, 333 City Tower West, Ste 500, Orange, CA 92868; [email protected].

Financial disclosures: This manuscript was generously supported by multiple donors, including the Mehra Family, the Yang Family, and the Chao Family. Dr. Amin reported serving as Principal Investigator or Co-Investigator of clinical trials sponsored by NIH/NIAID, NeuroRX Pharma, Pulmotect, Blade Therapeutics, Novartis, Takeda, Humanigen, Eli Lilly, PTC Therapeutics, OctaPharma, Fulcrum Therapeutics, and Alexion, unrelated to the present study. He has served as speaker and/or consultant for BMS, Pfizer, BI, Portola, Sunovion, Mylan, Salix, Alexion, AstraZeneca, Novartis, Nabriva, Paratek, Bayer, Tetraphase, Achaogen La Jolla, Ferring, Seres, Millennium, PeraHealth, HeartRite, Aseptiscope, and Sprightly, unrelated to the present study.

Inside the emergency department at Sparrow Hospital in Lansing, Mich., staff members are struggling to care for patients showing up much sicker than they’ve ever seen.

Tiffani Dusang, the ER’s nursing director, practically vibrates with pent-up anxiety, looking at patients lying on a long line of stretchers pushed up against the beige walls of the hospital hallways. “It’s hard to watch,” she said in a warm Texas twang.

But there’s nothing she can do. The ER’s 72 rooms are already filled.

“I always feel very, very bad when I walk down the hallway and see that people are in pain, or needing to sleep, or needing quiet. But they have to be in the hallway with, as you can see, 10 or 15 people walking by every minute,” Ms. Dusang said.

The scene is a stark contrast to where this emergency department — and thousands of others — were at the start of the pandemic. Except for initial hot spots like New York City, in spring 2020 many ERs across the country were often eerily empty. Terrified of contracting COVID-19, people who were sick with other things did their best to stay away from hospitals. Visits to emergency rooms dropped to half their typical levels, according to the Epic Health Research Network, and didn’t fully rebound until this summer.

But now, they’re too full. Even in parts of the country where covid isn’t overwhelming the health system, patients are showing up to the ER sicker than before the pandemic, their diseases more advanced and in need of more complicated care.

Months of treatment delays have exacerbated chronic conditions and worsened symptoms. Doctors and nurses say the severity of illness ranges widely and includes abdominal pain, respiratory problems, blood clots, heart conditions and suicide attempts, among other conditions.

But they can hardly be accommodated. Emergency departments, ideally, are meant to be brief ports in a storm, with patients staying just long enough to be sent home with instructions to follow up with primary care physicians, or sufficiently stabilized to be transferred “upstairs” to inpatient or intensive care units.

Except now those long-term care floors are full too, with a mix of covid and non-covid patients. People coming to the ER get warehoused for hours, even days, forcing ER staffers to perform long-term care roles they weren’t trained to do.

At Sparrow, space is a valuable commodity in the ER: A separate section of the hospital was turned into an overflow unit. Stretchers stack up in halls. A row of brown reclining chairs lines a wall, intended for patients who aren’t sick enough for a stretcher but are too sick to stay in the main waiting room.

Forget privacy, Alejos Perrientoz learned when he arrived. He came to the ER because his arm had been tingling and painful for over a week. He couldn’t hold a cup of coffee. A nurse gave him a full physical exam in a brown recliner, which made him self-conscious about having his shirt lifted in front of strangers. “I felt a little uncomfortable,” he whispered. “But I have no choice, you know? I’m in the hallway. There’s no rooms.

“We could have done the physical in the parking lot,” he added, managing a laugh.

Even patients who arrive by ambulance are not guaranteed a room: One nurse runs triage, screening those who absolutely need a bed, and those who can be put in the waiting area.

“I hate that we even have to make that determination,” MS. Dusang said. Lately, staff members have been pulling out some patients already in the ER’s rooms when others arrive who are more critically ill. “No one likes to take someone out of the privacy of their room and say, ‘We’re going to put you in a hallway because we need to get care to someone else.’”

ER patients have grown sicker

“We are hearing from members in every part of the country,” said Dr. Lisa Moreno, president of the American Academy of Emergency Medicine. “The Midwest, the South, the Northeast, the West … they are seeing this exact same phenomenon.”

Although the number of ER visits returned to pre-COVID levels this summer, admission rates, from the ER to the hospital’s inpatient floors, are still almost 20% higher. That’s according to the most recent analysis by the Epic Health Research Network, which pulls data from more than 120 million patients across the country.

“It’s an early indicator that what’s happening in the ED is that we’re seeing more acute cases than we were pre-pandemic,” said Caleb Cox, a data scientist at Epic.

Less acute cases, such as people with health issues like rashes or conjunctivitis, still aren’t going to the ER as much as they used to. Instead, they may be opting for an urgent care center or their primary care doctor, Mr. Cox explained. Meanwhile, there has been an increase in people coming to the ER with more serious conditions, like strokes and heart attacks.

So, even though the total number of patients coming to ERs is about the same as before the pandemic, “that’s absolutely going to feel like [if I’m an ER doctor or nurse] I’m seeing more patients and I’m seeing more acute patients,” Mr. Cox said.

Dr. Moreno, the AAEM president, works at an emergency department in New Orleans. She said the level of illness, and the inability to admit patients quickly and move them to beds upstairs, has created a level of chaos she described as “not even humane.”

At the beginning of a recent shift, she heard a patient crying nearby and went to investigate. It was a paraplegic man who’d recently had surgery for colon cancer. His large post-operative wound was sealed with a device called a wound vac, which pulls fluid from the wound into a drainage tube attached to a portable vacuum pump.

But the wound vac had malfunctioned, which is why he had come to the ER. Staffers were so busy, however, that by the time Dr. Moreno came in, the fluid from his wound was leaking everywhere.

“When I went in, the bed was covered,” she recalled. “I mean, he was lying in a puddle of secretions from this wound. And he was crying, because he said to me, ‘I’m paralyzed. I can’t move to get away from all these secretions, and I know I’m going to end up getting an infection. I know I’m going to end up getting an ulcer. I’ve been laying in this for, like, eight or nine hours.’”

The nurse in charge of his care told Dr. Moreno she simply hadn’t had time to help this patient yet. “She said, ‘I’ve had so many patients to take care of, and so many critical patients. I started [an IV] drip on this person. This person is on a cardiac monitor. I just didn’t have time to get in there.’”

“This is not humane care,” Dr. Moreno said. “This is horrible care.”

But it’s what can happen when emergency department staffers don’t have the resources they need to deal with the onslaught of competing demands.

“All the nurses and doctors had the highest level of intent to do the right thing for the person,” Dr. Moreno said. “But because of the high acuity of … a large number of patients, the staffing ratio of nurse to patient, even the staffing ratio of doctor to patient, this guy did not get the care that he deserved to get, just as a human being.”

The instance of unintended neglect that Dr. Moreno saw is extreme, and not the experience of most patients who arrive at ERs these days. But the problem is not new: Even before the pandemic, ER overcrowding had been a “widespread problem and a source of patient harm, according to a recent commentary in NEJM Catalyst Innovations in Care Delivery.

“ED crowding is not an issue of inconvenience,” the authors wrote. “There is incontrovertible evidence that ED crowding leads to significant patient harm, including morbidity and mortality related to consequential delays of treatment for both high- and low-acuity patients.”

And already-overwhelmed staffers are burning out.

Burnout feeds staffing shortages, and vice versa

Every morning, Tiffani Dusang wakes up and checks her Sparrow email with one singular hope: that she will not see yet another nurse resignation letter in her inbox.

“I cannot tell you how many of them [the nurses] tell me they went home crying” after their shifts, she said.

Despite Ms. Dusang’s best efforts to support her staffers, they’re leaving too fast to be replaced, either to take higher-paying gigs as a travel nurse, to try a less-stressful type of nursing, or simply walking away from the profession entirely.

Kelly Spitz has been an emergency department nurse at Sparrow for 10 years. But, lately, she has also fantasized about leaving. “It has crossed my mind several times,” she said, and yet she continues to come back. “Because I have a team here. And I love what I do.” But then she started to cry. The issue is not the hard work, or even the stress. She struggles with not being able to give her patients the kind of care and attention she wants to give them, and that they need and deserve, she said.

She often thinks about a patient whose test results revealed terminal cancer, she said. Ms. Spitz spent all day working the phones, hustling case managers, trying to get hospice care set up in the man’s home. He was going to die, and she just didn’t want him to have to die in the hospital, where only one visitor was allowed. She wanted to get him home, and back with his family.

Finally, after many hours, they found an ambulance to take him home.

Three days later, the man’s family members called Ms. Spitz: He had died surrounded by family. They were calling to thank her.

“I felt like I did my job there, because I got him home,” she said. But that’s a rare feeling these days. “I just hope it gets better. I hope it gets better soon.”

Around 4 p.m. at Sparrow Hospital as one shift approached its end, Ms. Dusang faced a new crisis: The overnight shift was more short-staffed than usual.

“Can we get two inpatient nurses?” she asked, hoping to borrow two nurses from one of the hospital floors upstairs.

“Already tried,” replied nurse Troy Latunski.

Without more staff, it’s going to be hard to care for new patients who come in overnight — from car crashes to seizures or other emergencies.

But Mr. Latunski had a plan: He would go home, snatch a few hours of sleep and return at 11 p.m. to work the overnight shift in the ER’s overflow unit. That meant he would be largely caring for eight patients, alone. On just a few short hours of sleep. But lately that seemed to be their only, and best, option.

Ms. Dusang considered for a moment, took a deep breath and nodded. “OK,” she said.

“Go home. Get some sleep. Thank you,” she added, shooting Mr. Latunski a grateful smile. And then she pivoted, because another nurse was approaching with an urgent question. On to the next crisis.

KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation. This story is part of a partnership that includes Michigan Radio, NPR and KHN.

Inside the emergency department at Sparrow Hospital in Lansing, Mich., staff members are struggling to care for patients showing up much sicker than they’ve ever seen.

Tiffani Dusang, the ER’s nursing director, practically vibrates with pent-up anxiety, looking at patients lying on a long line of stretchers pushed up against the beige walls of the hospital hallways. “It’s hard to watch,” she said in a warm Texas twang.

But there’s nothing she can do. The ER’s 72 rooms are already filled.

“I always feel very, very bad when I walk down the hallway and see that people are in pain, or needing to sleep, or needing quiet. But they have to be in the hallway with, as you can see, 10 or 15 people walking by every minute,” Ms. Dusang said.

The scene is a stark contrast to where this emergency department — and thousands of others — were at the start of the pandemic. Except for initial hot spots like New York City, in spring 2020 many ERs across the country were often eerily empty. Terrified of contracting COVID-19, people who were sick with other things did their best to stay away from hospitals. Visits to emergency rooms dropped to half their typical levels, according to the Epic Health Research Network, and didn’t fully rebound until this summer.

But now, they’re too full. Even in parts of the country where covid isn’t overwhelming the health system, patients are showing up to the ER sicker than before the pandemic, their diseases more advanced and in need of more complicated care.

Months of treatment delays have exacerbated chronic conditions and worsened symptoms. Doctors and nurses say the severity of illness ranges widely and includes abdominal pain, respiratory problems, blood clots, heart conditions and suicide attempts, among other conditions.

But they can hardly be accommodated. Emergency departments, ideally, are meant to be brief ports in a storm, with patients staying just long enough to be sent home with instructions to follow up with primary care physicians, or sufficiently stabilized to be transferred “upstairs” to inpatient or intensive care units.

Except now those long-term care floors are full too, with a mix of covid and non-covid patients. People coming to the ER get warehoused for hours, even days, forcing ER staffers to perform long-term care roles they weren’t trained to do.

At Sparrow, space is a valuable commodity in the ER: A separate section of the hospital was turned into an overflow unit. Stretchers stack up in halls. A row of brown reclining chairs lines a wall, intended for patients who aren’t sick enough for a stretcher but are too sick to stay in the main waiting room.

Forget privacy, Alejos Perrientoz learned when he arrived. He came to the ER because his arm had been tingling and painful for over a week. He couldn’t hold a cup of coffee. A nurse gave him a full physical exam in a brown recliner, which made him self-conscious about having his shirt lifted in front of strangers. “I felt a little uncomfortable,” he whispered. “But I have no choice, you know? I’m in the hallway. There’s no rooms.

“We could have done the physical in the parking lot,” he added, managing a laugh.

Even patients who arrive by ambulance are not guaranteed a room: One nurse runs triage, screening those who absolutely need a bed, and those who can be put in the waiting area.

“I hate that we even have to make that determination,” MS. Dusang said. Lately, staff members have been pulling out some patients already in the ER’s rooms when others arrive who are more critically ill. “No one likes to take someone out of the privacy of their room and say, ‘We’re going to put you in a hallway because we need to get care to someone else.’”

ER patients have grown sicker

“We are hearing from members in every part of the country,” said Dr. Lisa Moreno, president of the American Academy of Emergency Medicine. “The Midwest, the South, the Northeast, the West … they are seeing this exact same phenomenon.”

Although the number of ER visits returned to pre-COVID levels this summer, admission rates, from the ER to the hospital’s inpatient floors, are still almost 20% higher. That’s according to the most recent analysis by the Epic Health Research Network, which pulls data from more than 120 million patients across the country.

“It’s an early indicator that what’s happening in the ED is that we’re seeing more acute cases than we were pre-pandemic,” said Caleb Cox, a data scientist at Epic.

Less acute cases, such as people with health issues like rashes or conjunctivitis, still aren’t going to the ER as much as they used to. Instead, they may be opting for an urgent care center or their primary care doctor, Mr. Cox explained. Meanwhile, there has been an increase in people coming to the ER with more serious conditions, like strokes and heart attacks.

So, even though the total number of patients coming to ERs is about the same as before the pandemic, “that’s absolutely going to feel like [if I’m an ER doctor or nurse] I’m seeing more patients and I’m seeing more acute patients,” Mr. Cox said.

Dr. Moreno, the AAEM president, works at an emergency department in New Orleans. She said the level of illness, and the inability to admit patients quickly and move them to beds upstairs, has created a level of chaos she described as “not even humane.”

At the beginning of a recent shift, she heard a patient crying nearby and went to investigate. It was a paraplegic man who’d recently had surgery for colon cancer. His large post-operative wound was sealed with a device called a wound vac, which pulls fluid from the wound into a drainage tube attached to a portable vacuum pump.

But the wound vac had malfunctioned, which is why he had come to the ER. Staffers were so busy, however, that by the time Dr. Moreno came in, the fluid from his wound was leaking everywhere.

“When I went in, the bed was covered,” she recalled. “I mean, he was lying in a puddle of secretions from this wound. And he was crying, because he said to me, ‘I’m paralyzed. I can’t move to get away from all these secretions, and I know I’m going to end up getting an infection. I know I’m going to end up getting an ulcer. I’ve been laying in this for, like, eight or nine hours.’”

The nurse in charge of his care told Dr. Moreno she simply hadn’t had time to help this patient yet. “She said, ‘I’ve had so many patients to take care of, and so many critical patients. I started [an IV] drip on this person. This person is on a cardiac monitor. I just didn’t have time to get in there.’”

“This is not humane care,” Dr. Moreno said. “This is horrible care.”

But it’s what can happen when emergency department staffers don’t have the resources they need to deal with the onslaught of competing demands.

“All the nurses and doctors had the highest level of intent to do the right thing for the person,” Dr. Moreno said. “But because of the high acuity of … a large number of patients, the staffing ratio of nurse to patient, even the staffing ratio of doctor to patient, this guy did not get the care that he deserved to get, just as a human being.”

The instance of unintended neglect that Dr. Moreno saw is extreme, and not the experience of most patients who arrive at ERs these days. But the problem is not new: Even before the pandemic, ER overcrowding had been a “widespread problem and a source of patient harm, according to a recent commentary in NEJM Catalyst Innovations in Care Delivery.

“ED crowding is not an issue of inconvenience,” the authors wrote. “There is incontrovertible evidence that ED crowding leads to significant patient harm, including morbidity and mortality related to consequential delays of treatment for both high- and low-acuity patients.”

And already-overwhelmed staffers are burning out.

Burnout feeds staffing shortages, and vice versa

Every morning, Tiffani Dusang wakes up and checks her Sparrow email with one singular hope: that she will not see yet another nurse resignation letter in her inbox.

“I cannot tell you how many of them [the nurses] tell me they went home crying” after their shifts, she said.

Despite Ms. Dusang’s best efforts to support her staffers, they’re leaving too fast to be replaced, either to take higher-paying gigs as a travel nurse, to try a less-stressful type of nursing, or simply walking away from the profession entirely.

Kelly Spitz has been an emergency department nurse at Sparrow for 10 years. But, lately, she has also fantasized about leaving. “It has crossed my mind several times,” she said, and yet she continues to come back. “Because I have a team here. And I love what I do.” But then she started to cry. The issue is not the hard work, or even the stress. She struggles with not being able to give her patients the kind of care and attention she wants to give them, and that they need and deserve, she said.

She often thinks about a patient whose test results revealed terminal cancer, she said. Ms. Spitz spent all day working the phones, hustling case managers, trying to get hospice care set up in the man’s home. He was going to die, and she just didn’t want him to have to die in the hospital, where only one visitor was allowed. She wanted to get him home, and back with his family.

Finally, after many hours, they found an ambulance to take him home.

Three days later, the man’s family members called Ms. Spitz: He had died surrounded by family. They were calling to thank her.

“I felt like I did my job there, because I got him home,” she said. But that’s a rare feeling these days. “I just hope it gets better. I hope it gets better soon.”

Around 4 p.m. at Sparrow Hospital as one shift approached its end, Ms. Dusang faced a new crisis: The overnight shift was more short-staffed than usual.

“Can we get two inpatient nurses?” she asked, hoping to borrow two nurses from one of the hospital floors upstairs.

“Already tried,” replied nurse Troy Latunski.

Without more staff, it’s going to be hard to care for new patients who come in overnight — from car crashes to seizures or other emergencies.

But Mr. Latunski had a plan: He would go home, snatch a few hours of sleep and return at 11 p.m. to work the overnight shift in the ER’s overflow unit. That meant he would be largely caring for eight patients, alone. On just a few short hours of sleep. But lately that seemed to be their only, and best, option.

Ms. Dusang considered for a moment, took a deep breath and nodded. “OK,” she said.

“Go home. Get some sleep. Thank you,” she added, shooting Mr. Latunski a grateful smile. And then she pivoted, because another nurse was approaching with an urgent question. On to the next crisis.

KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation. This story is part of a partnership that includes Michigan Radio, NPR and KHN.

Inside the emergency department at Sparrow Hospital in Lansing, Mich., staff members are struggling to care for patients showing up much sicker than they’ve ever seen.

Tiffani Dusang, the ER’s nursing director, practically vibrates with pent-up anxiety, looking at patients lying on a long line of stretchers pushed up against the beige walls of the hospital hallways. “It’s hard to watch,” she said in a warm Texas twang.

But there’s nothing she can do. The ER’s 72 rooms are already filled.

“I always feel very, very bad when I walk down the hallway and see that people are in pain, or needing to sleep, or needing quiet. But they have to be in the hallway with, as you can see, 10 or 15 people walking by every minute,” Ms. Dusang said.

The scene is a stark contrast to where this emergency department — and thousands of others — were at the start of the pandemic. Except for initial hot spots like New York City, in spring 2020 many ERs across the country were often eerily empty. Terrified of contracting COVID-19, people who were sick with other things did their best to stay away from hospitals. Visits to emergency rooms dropped to half their typical levels, according to the Epic Health Research Network, and didn’t fully rebound until this summer.

But now, they’re too full. Even in parts of the country where covid isn’t overwhelming the health system, patients are showing up to the ER sicker than before the pandemic, their diseases more advanced and in need of more complicated care.

Months of treatment delays have exacerbated chronic conditions and worsened symptoms. Doctors and nurses say the severity of illness ranges widely and includes abdominal pain, respiratory problems, blood clots, heart conditions and suicide attempts, among other conditions.

But they can hardly be accommodated. Emergency departments, ideally, are meant to be brief ports in a storm, with patients staying just long enough to be sent home with instructions to follow up with primary care physicians, or sufficiently stabilized to be transferred “upstairs” to inpatient or intensive care units.

Except now those long-term care floors are full too, with a mix of covid and non-covid patients. People coming to the ER get warehoused for hours, even days, forcing ER staffers to perform long-term care roles they weren’t trained to do.

At Sparrow, space is a valuable commodity in the ER: A separate section of the hospital was turned into an overflow unit. Stretchers stack up in halls. A row of brown reclining chairs lines a wall, intended for patients who aren’t sick enough for a stretcher but are too sick to stay in the main waiting room.

Forget privacy, Alejos Perrientoz learned when he arrived. He came to the ER because his arm had been tingling and painful for over a week. He couldn’t hold a cup of coffee. A nurse gave him a full physical exam in a brown recliner, which made him self-conscious about having his shirt lifted in front of strangers. “I felt a little uncomfortable,” he whispered. “But I have no choice, you know? I’m in the hallway. There’s no rooms.

“We could have done the physical in the parking lot,” he added, managing a laugh.

Even patients who arrive by ambulance are not guaranteed a room: One nurse runs triage, screening those who absolutely need a bed, and those who can be put in the waiting area.

“I hate that we even have to make that determination,” MS. Dusang said. Lately, staff members have been pulling out some patients already in the ER’s rooms when others arrive who are more critically ill. “No one likes to take someone out of the privacy of their room and say, ‘We’re going to put you in a hallway because we need to get care to someone else.’”

ER patients have grown sicker

“We are hearing from members in every part of the country,” said Dr. Lisa Moreno, president of the American Academy of Emergency Medicine. “The Midwest, the South, the Northeast, the West … they are seeing this exact same phenomenon.”

Although the number of ER visits returned to pre-COVID levels this summer, admission rates, from the ER to the hospital’s inpatient floors, are still almost 20% higher. That’s according to the most recent analysis by the Epic Health Research Network, which pulls data from more than 120 million patients across the country.

“It’s an early indicator that what’s happening in the ED is that we’re seeing more acute cases than we were pre-pandemic,” said Caleb Cox, a data scientist at Epic.

Less acute cases, such as people with health issues like rashes or conjunctivitis, still aren’t going to the ER as much as they used to. Instead, they may be opting for an urgent care center or their primary care doctor, Mr. Cox explained. Meanwhile, there has been an increase in people coming to the ER with more serious conditions, like strokes and heart attacks.

So, even though the total number of patients coming to ERs is about the same as before the pandemic, “that’s absolutely going to feel like [if I’m an ER doctor or nurse] I’m seeing more patients and I’m seeing more acute patients,” Mr. Cox said.

Dr. Moreno, the AAEM president, works at an emergency department in New Orleans. She said the level of illness, and the inability to admit patients quickly and move them to beds upstairs, has created a level of chaos she described as “not even humane.”

At the beginning of a recent shift, she heard a patient crying nearby and went to investigate. It was a paraplegic man who’d recently had surgery for colon cancer. His large post-operative wound was sealed with a device called a wound vac, which pulls fluid from the wound into a drainage tube attached to a portable vacuum pump.

But the wound vac had malfunctioned, which is why he had come to the ER. Staffers were so busy, however, that by the time Dr. Moreno came in, the fluid from his wound was leaking everywhere.

“When I went in, the bed was covered,” she recalled. “I mean, he was lying in a puddle of secretions from this wound. And he was crying, because he said to me, ‘I’m paralyzed. I can’t move to get away from all these secretions, and I know I’m going to end up getting an infection. I know I’m going to end up getting an ulcer. I’ve been laying in this for, like, eight or nine hours.’”

The nurse in charge of his care told Dr. Moreno she simply hadn’t had time to help this patient yet. “She said, ‘I’ve had so many patients to take care of, and so many critical patients. I started [an IV] drip on this person. This person is on a cardiac monitor. I just didn’t have time to get in there.’”

“This is not humane care,” Dr. Moreno said. “This is horrible care.”

But it’s what can happen when emergency department staffers don’t have the resources they need to deal with the onslaught of competing demands.

“All the nurses and doctors had the highest level of intent to do the right thing for the person,” Dr. Moreno said. “But because of the high acuity of … a large number of patients, the staffing ratio of nurse to patient, even the staffing ratio of doctor to patient, this guy did not get the care that he deserved to get, just as a human being.”

The instance of unintended neglect that Dr. Moreno saw is extreme, and not the experience of most patients who arrive at ERs these days. But the problem is not new: Even before the pandemic, ER overcrowding had been a “widespread problem and a source of patient harm, according to a recent commentary in NEJM Catalyst Innovations in Care Delivery.

“ED crowding is not an issue of inconvenience,” the authors wrote. “There is incontrovertible evidence that ED crowding leads to significant patient harm, including morbidity and mortality related to consequential delays of treatment for both high- and low-acuity patients.”

And already-overwhelmed staffers are burning out.

Burnout feeds staffing shortages, and vice versa

Every morning, Tiffani Dusang wakes up and checks her Sparrow email with one singular hope: that she will not see yet another nurse resignation letter in her inbox.

“I cannot tell you how many of them [the nurses] tell me they went home crying” after their shifts, she said.

Despite Ms. Dusang’s best efforts to support her staffers, they’re leaving too fast to be replaced, either to take higher-paying gigs as a travel nurse, to try a less-stressful type of nursing, or simply walking away from the profession entirely.

Kelly Spitz has been an emergency department nurse at Sparrow for 10 years. But, lately, she has also fantasized about leaving. “It has crossed my mind several times,” she said, and yet she continues to come back. “Because I have a team here. And I love what I do.” But then she started to cry. The issue is not the hard work, or even the stress. She struggles with not being able to give her patients the kind of care and attention she wants to give them, and that they need and deserve, she said.

She often thinks about a patient whose test results revealed terminal cancer, she said. Ms. Spitz spent all day working the phones, hustling case managers, trying to get hospice care set up in the man’s home. He was going to die, and she just didn’t want him to have to die in the hospital, where only one visitor was allowed. She wanted to get him home, and back with his family.

Finally, after many hours, they found an ambulance to take him home.

Three days later, the man’s family members called Ms. Spitz: He had died surrounded by family. They were calling to thank her.

“I felt like I did my job there, because I got him home,” she said. But that’s a rare feeling these days. “I just hope it gets better. I hope it gets better soon.”

Around 4 p.m. at Sparrow Hospital as one shift approached its end, Ms. Dusang faced a new crisis: The overnight shift was more short-staffed than usual.

“Can we get two inpatient nurses?” she asked, hoping to borrow two nurses from one of the hospital floors upstairs.

“Already tried,” replied nurse Troy Latunski.

Without more staff, it’s going to be hard to care for new patients who come in overnight — from car crashes to seizures or other emergencies.

But Mr. Latunski had a plan: He would go home, snatch a few hours of sleep and return at 11 p.m. to work the overnight shift in the ER’s overflow unit. That meant he would be largely caring for eight patients, alone. On just a few short hours of sleep. But lately that seemed to be their only, and best, option.

Ms. Dusang considered for a moment, took a deep breath and nodded. “OK,” she said.

“Go home. Get some sleep. Thank you,” she added, shooting Mr. Latunski a grateful smile. And then she pivoted, because another nurse was approaching with an urgent question. On to the next crisis.

KHN (Kaiser Health News) is a national newsroom that produces in-depth journalism about health issues. Together with Policy Analysis and Polling, KHN is one of the three major operating programs at KFF (Kaiser Family Foundation). KFF is an endowed nonprofit organization providing information on health issues to the nation. This story is part of a partnership that includes Michigan Radio, NPR and KHN.

While sick at home with a 26-day symptomatic course of COVID-19 in March 2020, I watched the surge unfold in my state and the hospital where I work as an inpatient adult medicine physician. Although the preponderance of my professional life is dedicated to leading teams in implementing delivery system transformation, the hat I wore in that moment involved living through and keeping up with the changes around me. Once I recovered and returned to the arena as a COVID doctor, I adapted to and made changes during constant shifts in how we provided care.

Looking back on those months during the worst of the COVID-19 hospital surge in my region, I reflect on the factors that helped me, as a frontline and shift-work clinician, adapt to and make those changes. In reflecting on the elements that were meaningful to me during the crisis, I recognize a set of change-enabling factors that have broad relevance for those of us who work to improve outcomes for patients and populations.

Confidence engendered by liberating data

In the early days of the surge, there was much uncertainty, and unfortunately, some seriously imperfect messaging. Trust was broken or badly bruised for many frontline clinicians. I share this painful phase not to criticize, but rather reflect on what mattered to me during that crisis of confidence. It was data. Raw, unadjusted, best-available data. Produced and pushed out. Available, trended over time, telling the story of where we are, now. Counts of tests, beds, and ventilators. The consistent, transparent availability of relevant and straightforward data provided an active antidote to a sense of uncertainty during a crisis of confidence.

Personal practice change stimulated by relevance and urgency

For half a decade, I have been encouraging interdisciplinary inpatient teams to identify and actively engage the family and/or care partner as a member of the care team. Despite even the American Association of Retired Persons mobilizing an impressive regulatory approach in 32 states to require that family and/or care partners are included as such, the practice change efforts continued on a slow and steady path. Why? We just didn’t believe it was of urgent, relevant, mission-critical importance to our daily practice to do so. That all changed in March 2020.

Without needing to be told, educated, or incentivized, my first night as a COVID doctor found me calling every single patient’s family upon admission, regardless of what time it was. It was critical to review the diagnosis, transparently discuss the uncertainty regarding the upcoming hours and days, review the potential contingencies, and ask, right there and then, whether intubation is consistent with goals of care. It was that urgent and relevant. Without exception, families were grateful for the effort and candor.

The significance of this practice—undoubtedly adopted by every inpatient provider who has worked a COVID surge—is rooted in decades of academic deliberation on which is the “right” doctor to have these discussions. None of that mattered. Historical opinions changed due to what was urgent and relevant given the situation at hand and the job we had to do. Imagine, for example, what we could do and how we could change if we now consider it urgent and relevant to identify and mobilize enhanced services and supports to patients who experience inequities because we believe it to be mission-critical to the job we show up to do every day.

Change fostered by a creative problem-solving ecosystem

Embracing personal practice change was made easier and implicitly affirmed by the creative problem solving that occurred everywhere. Tents, drive-throughs, and even college field houses were now settings of care. Primary care physicians, cardiologists, and gastrointestinal (GI) and postanesthesia care nurses staffed the COVID floors. Rolling stands held iPads so staff could communicate with patients without entering the room. This creative ecosystem fostered individual practice change. No debates were needed to recognize that standard processes were inadequate. No single role or service of any discipline was singularly asked to change to meet the needs of the moment. Because of this ecosystem of creative, active change, there was a much greater flexibility among individuals, role types, departments, and disciplines to change. This is particularly poignant to me in light of the work I lead to improve care for patients who experience systemic inequities in our health care system. When we ask a single role type or discipline to change, it can be met with resistance; far more success is achieved when we engage an interdisciplinary and interdepartmental approach to change. When surrounded by others making change, it makes us more willing to change, too.

It is so often invoked that health care is a team sport. In practicality, while we may aspire to work as a team, health care delivery is still all too often comprised of a set of individual actors with individualized responsibilities trying to communicate the best they can with each other.

What I experienced during the surge at my hospital was the very best version of teamwork I have ever been a part of in health care: empathetic, mutually interdependent strangers coming together during daily changes in staffing, processes, and resources. I will never forget nights walking into the pediatric floor or day surgery recovery area—now repurposed as a COVID unit—to entirely new faces comprised of GI suite nurses, outpatient doctors, and moonlighting intensivists.

We were all new to each other, all new to working in this setting, and all new to whatever the newest changes of the day brought. I will never forget how we greeted each other and introduced ourselves. We asked each other where we were “from,” and held a genuine appreciation to each other for being there. Imagine how this impacted how we worked together. Looking back on those night shifts, I remember us as a truly interdependent team. I will endeavor to bring that sense of mutual regard and interdependency into my work to foster effective interdisciplinary and cross-continuum teamwork.

Takeaways

As a student and practitioner of delivery system transformation, I am often in conversations about imperfect data, incomplete evidence, and role-specific and organizational resistance to change. As an acute care provider during the COVID-19 hospital surge in my region, the experiences I had as a participant in the COVID-related delivery system change will stay with me as I lead value-based delivery system change. What worked in an infectious disease crisis holds great relevance to our pressing, urgent, relevant work to create a more person-centered, equitable, and value-based delivery system.

I am confident that if those of us seeking to improve outcomes use visible and accessible data to engender confidence, clearly link practice change to the relevant and urgent issue at hand, promote broadly visible creative problem solving to foster an ecosystem of change, and cultivate empathy and mutual interdependence to promote the teamwork we aspire to have, that we will foster meaningful progress in our efforts to improve care for patients and populations.

Corresponding author: Amy Boutwell, MD, MPP, President, Collaborative Healthcare Strategies, Lexington, MA; [email protected].

Financial disclosures: None.

While sick at home with a 26-day symptomatic course of COVID-19 in March 2020, I watched the surge unfold in my state and the hospital where I work as an inpatient adult medicine physician. Although the preponderance of my professional life is dedicated to leading teams in implementing delivery system transformation, the hat I wore in that moment involved living through and keeping up with the changes around me. Once I recovered and returned to the arena as a COVID doctor, I adapted to and made changes during constant shifts in how we provided care.

Looking back on those months during the worst of the COVID-19 hospital surge in my region, I reflect on the factors that helped me, as a frontline and shift-work clinician, adapt to and make those changes. In reflecting on the elements that were meaningful to me during the crisis, I recognize a set of change-enabling factors that have broad relevance for those of us who work to improve outcomes for patients and populations.

Confidence engendered by liberating data

In the early days of the surge, there was much uncertainty, and unfortunately, some seriously imperfect messaging. Trust was broken or badly bruised for many frontline clinicians. I share this painful phase not to criticize, but rather reflect on what mattered to me during that crisis of confidence. It was data. Raw, unadjusted, best-available data. Produced and pushed out. Available, trended over time, telling the story of where we are, now. Counts of tests, beds, and ventilators. The consistent, transparent availability of relevant and straightforward data provided an active antidote to a sense of uncertainty during a crisis of confidence.

Personal practice change stimulated by relevance and urgency

For half a decade, I have been encouraging interdisciplinary inpatient teams to identify and actively engage the family and/or care partner as a member of the care team. Despite even the American Association of Retired Persons mobilizing an impressive regulatory approach in 32 states to require that family and/or care partners are included as such, the practice change efforts continued on a slow and steady path. Why? We just didn’t believe it was of urgent, relevant, mission-critical importance to our daily practice to do so. That all changed in March 2020.

Without needing to be told, educated, or incentivized, my first night as a COVID doctor found me calling every single patient’s family upon admission, regardless of what time it was. It was critical to review the diagnosis, transparently discuss the uncertainty regarding the upcoming hours and days, review the potential contingencies, and ask, right there and then, whether intubation is consistent with goals of care. It was that urgent and relevant. Without exception, families were grateful for the effort and candor.

The significance of this practice—undoubtedly adopted by every inpatient provider who has worked a COVID surge—is rooted in decades of academic deliberation on which is the “right” doctor to have these discussions. None of that mattered. Historical opinions changed due to what was urgent and relevant given the situation at hand and the job we had to do. Imagine, for example, what we could do and how we could change if we now consider it urgent and relevant to identify and mobilize enhanced services and supports to patients who experience inequities because we believe it to be mission-critical to the job we show up to do every day.

Change fostered by a creative problem-solving ecosystem

Embracing personal practice change was made easier and implicitly affirmed by the creative problem solving that occurred everywhere. Tents, drive-throughs, and even college field houses were now settings of care. Primary care physicians, cardiologists, and gastrointestinal (GI) and postanesthesia care nurses staffed the COVID floors. Rolling stands held iPads so staff could communicate with patients without entering the room. This creative ecosystem fostered individual practice change. No debates were needed to recognize that standard processes were inadequate. No single role or service of any discipline was singularly asked to change to meet the needs of the moment. Because of this ecosystem of creative, active change, there was a much greater flexibility among individuals, role types, departments, and disciplines to change. This is particularly poignant to me in light of the work I lead to improve care for patients who experience systemic inequities in our health care system. When we ask a single role type or discipline to change, it can be met with resistance; far more success is achieved when we engage an interdisciplinary and interdepartmental approach to change. When surrounded by others making change, it makes us more willing to change, too.

It is so often invoked that health care is a team sport. In practicality, while we may aspire to work as a team, health care delivery is still all too often comprised of a set of individual actors with individualized responsibilities trying to communicate the best they can with each other.

What I experienced during the surge at my hospital was the very best version of teamwork I have ever been a part of in health care: empathetic, mutually interdependent strangers coming together during daily changes in staffing, processes, and resources. I will never forget nights walking into the pediatric floor or day surgery recovery area—now repurposed as a COVID unit—to entirely new faces comprised of GI suite nurses, outpatient doctors, and moonlighting intensivists.

We were all new to each other, all new to working in this setting, and all new to whatever the newest changes of the day brought. I will never forget how we greeted each other and introduced ourselves. We asked each other where we were “from,” and held a genuine appreciation to each other for being there. Imagine how this impacted how we worked together. Looking back on those night shifts, I remember us as a truly interdependent team. I will endeavor to bring that sense of mutual regard and interdependency into my work to foster effective interdisciplinary and cross-continuum teamwork.

Takeaways

As a student and practitioner of delivery system transformation, I am often in conversations about imperfect data, incomplete evidence, and role-specific and organizational resistance to change. As an acute care provider during the COVID-19 hospital surge in my region, the experiences I had as a participant in the COVID-related delivery system change will stay with me as I lead value-based delivery system change. What worked in an infectious disease crisis holds great relevance to our pressing, urgent, relevant work to create a more person-centered, equitable, and value-based delivery system.

I am confident that if those of us seeking to improve outcomes use visible and accessible data to engender confidence, clearly link practice change to the relevant and urgent issue at hand, promote broadly visible creative problem solving to foster an ecosystem of change, and cultivate empathy and mutual interdependence to promote the teamwork we aspire to have, that we will foster meaningful progress in our efforts to improve care for patients and populations.

Corresponding author: Amy Boutwell, MD, MPP, President, Collaborative Healthcare Strategies, Lexington, MA; [email protected].

Financial disclosures: None.

While sick at home with a 26-day symptomatic course of COVID-19 in March 2020, I watched the surge unfold in my state and the hospital where I work as an inpatient adult medicine physician. Although the preponderance of my professional life is dedicated to leading teams in implementing delivery system transformation, the hat I wore in that moment involved living through and keeping up with the changes around me. Once I recovered and returned to the arena as a COVID doctor, I adapted to and made changes during constant shifts in how we provided care.

Looking back on those months during the worst of the COVID-19 hospital surge in my region, I reflect on the factors that helped me, as a frontline and shift-work clinician, adapt to and make those changes. In reflecting on the elements that were meaningful to me during the crisis, I recognize a set of change-enabling factors that have broad relevance for those of us who work to improve outcomes for patients and populations.

Confidence engendered by liberating data

In the early days of the surge, there was much uncertainty, and unfortunately, some seriously imperfect messaging. Trust was broken or badly bruised for many frontline clinicians. I share this painful phase not to criticize, but rather reflect on what mattered to me during that crisis of confidence. It was data. Raw, unadjusted, best-available data. Produced and pushed out. Available, trended over time, telling the story of where we are, now. Counts of tests, beds, and ventilators. The consistent, transparent availability of relevant and straightforward data provided an active antidote to a sense of uncertainty during a crisis of confidence.

Personal practice change stimulated by relevance and urgency

For half a decade, I have been encouraging interdisciplinary inpatient teams to identify and actively engage the family and/or care partner as a member of the care team. Despite even the American Association of Retired Persons mobilizing an impressive regulatory approach in 32 states to require that family and/or care partners are included as such, the practice change efforts continued on a slow and steady path. Why? We just didn’t believe it was of urgent, relevant, mission-critical importance to our daily practice to do so. That all changed in March 2020.

Without needing to be told, educated, or incentivized, my first night as a COVID doctor found me calling every single patient’s family upon admission, regardless of what time it was. It was critical to review the diagnosis, transparently discuss the uncertainty regarding the upcoming hours and days, review the potential contingencies, and ask, right there and then, whether intubation is consistent with goals of care. It was that urgent and relevant. Without exception, families were grateful for the effort and candor.

The significance of this practice—undoubtedly adopted by every inpatient provider who has worked a COVID surge—is rooted in decades of academic deliberation on which is the “right” doctor to have these discussions. None of that mattered. Historical opinions changed due to what was urgent and relevant given the situation at hand and the job we had to do. Imagine, for example, what we could do and how we could change if we now consider it urgent and relevant to identify and mobilize enhanced services and supports to patients who experience inequities because we believe it to be mission-critical to the job we show up to do every day.

Change fostered by a creative problem-solving ecosystem

Embracing personal practice change was made easier and implicitly affirmed by the creative problem solving that occurred everywhere. Tents, drive-throughs, and even college field houses were now settings of care. Primary care physicians, cardiologists, and gastrointestinal (GI) and postanesthesia care nurses staffed the COVID floors. Rolling stands held iPads so staff could communicate with patients without entering the room. This creative ecosystem fostered individual practice change. No debates were needed to recognize that standard processes were inadequate. No single role or service of any discipline was singularly asked to change to meet the needs of the moment. Because of this ecosystem of creative, active change, there was a much greater flexibility among individuals, role types, departments, and disciplines to change. This is particularly poignant to me in light of the work I lead to improve care for patients who experience systemic inequities in our health care system. When we ask a single role type or discipline to change, it can be met with resistance; far more success is achieved when we engage an interdisciplinary and interdepartmental approach to change. When surrounded by others making change, it makes us more willing to change, too.

It is so often invoked that health care is a team sport. In practicality, while we may aspire to work as a team, health care delivery is still all too often comprised of a set of individual actors with individualized responsibilities trying to communicate the best they can with each other.

What I experienced during the surge at my hospital was the very best version of teamwork I have ever been a part of in health care: empathetic, mutually interdependent strangers coming together during daily changes in staffing, processes, and resources. I will never forget nights walking into the pediatric floor or day surgery recovery area—now repurposed as a COVID unit—to entirely new faces comprised of GI suite nurses, outpatient doctors, and moonlighting intensivists.

We were all new to each other, all new to working in this setting, and all new to whatever the newest changes of the day brought. I will never forget how we greeted each other and introduced ourselves. We asked each other where we were “from,” and held a genuine appreciation to each other for being there. Imagine how this impacted how we worked together. Looking back on those night shifts, I remember us as a truly interdependent team. I will endeavor to bring that sense of mutual regard and interdependency into my work to foster effective interdisciplinary and cross-continuum teamwork.

Takeaways

As a student and practitioner of delivery system transformation, I am often in conversations about imperfect data, incomplete evidence, and role-specific and organizational resistance to change. As an acute care provider during the COVID-19 hospital surge in my region, the experiences I had as a participant in the COVID-related delivery system change will stay with me as I lead value-based delivery system change. What worked in an infectious disease crisis holds great relevance to our pressing, urgent, relevant work to create a more person-centered, equitable, and value-based delivery system.

I am confident that if those of us seeking to improve outcomes use visible and accessible data to engender confidence, clearly link practice change to the relevant and urgent issue at hand, promote broadly visible creative problem solving to foster an ecosystem of change, and cultivate empathy and mutual interdependence to promote the teamwork we aspire to have, that we will foster meaningful progress in our efforts to improve care for patients and populations.

Corresponding author: Amy Boutwell, MD, MPP, President, Collaborative Healthcare Strategies, Lexington, MA; [email protected].

Financial disclosures: None.

From the Department of Internal Medicine, Mount Sinai Health System, Icahn School of Medicine at Mount Sinai, New York, NY (Dr. Gandhi), and the School of Medicine, Seth Gordhandas Sunderdas Medical College, and King Edward Memorial Hospital, Mumbai, India (Drs. Gandhi and Tiwari).

Objective: Calculation of HEART score to (1) stratify patients as low-risk, intermediate-risk, high-risk, and to predict the short-term incidence of major adverse cardiovascular events (MACE), and (2) demonstrate feasibility of HEART score in our local settings.

Design: A prospective cohort study of patients with a chief complaint of chest pain concerning for acute coronary syndrome.

Setting: Participants were recruited from the emergency department (ED) of King Edward Memorial Hospital, a tertiary care academic medical center and a resource-limited setting in Mumbai, India.

Participants: We evaluated 141 patients aged 18 years and older presenting to the ED and stratified them using the HEART score. To assess patients’ progress, a follow-up phone call was made within 6 weeks after presentation to the ED.

Measurements: The primary outcomes were a risk stratification, 6-week occurrence of MACE, and performance of unscheduled revascularization or stress testing. The secondary outcomes were discharge or death.

Results: The 141 participants were stratified into low-risk, intermediate-risk, and high-risk groups: 67 (47.52%), 44 (31.21%), and 30 (21.28%), respectively. The 6-week incidence of MACE in each category was 1.49%, 18.18%, and 90%, respectively. An acute myocardial infarction was diagnosed in 24 patients (17.02%), 15 patients (10.64%) underwent percutaneous coronary intervention (PCI), and 4 patients (2.84%) underwent coronary artery bypass graft (CABG). Overall, 98.5% of low-risk patients and 93.33% of high-risk patients had an uneventful recovery following discharge; therefore, extrapolation based on results demonstrated reduced health care utilization. All the survey respondents found the HEART score to be feasible. The patient characteristics and risk profile of the patients with and without MACE demonstrated that: patients with MACE were older and had a higher proportion of males, hypertension, type 2 diabetes mellitus, smoking, hypercholesterolemia, prior history of PCI/CABG, and history of stroke.

Conclusion: The HEART score seems to be a useful tool for risk stratification and a reliable predictor of outcomes in chest pain patients and can therefore be used for triage.

Keywords: chest pain; emergency department; HEART score; acute coronary syndrome; major adverse cardiac events; myocardial infarction; revascularization.

Cardiovascular diseases (CVDs), especially coronary heart disease (CHD), have epidemic proportions worldwide. Globally, in 2012, CVD led to 17.5 million deaths,^1,2 with more than 75% of them occurring in developing countries. In contrast to developed countries, where mortality from CHD is rapidly declining, it is increasing in developing countries.^1,3 Current estimates from epidemiologic studies from various parts of India indicate the prevalence of CHD in India to be between 7% and 13% in urban populations and 2% and 7% in rural populations.⁴

Premature mortality in terms of years of life lost because of CVD in India increased by 59% over a 20-year span, from 23.2 million in 1990 to 37 million in 2010.⁵ Studies conducted in Mumbai (Mumbai Cohort Study) reported very high CVD mortality rates, approaching 500 per 100 000 for men and 250 per 100 000 for women.^6,7 However, to the best of our knowledge, in the Indian population, there are minimal data on utilization of a triage score, such as the HEART score, in chest pain patients in the emergency department (ED) in a resource-limited setting.

The most common reason for admitting patients to the ED is chest pain.⁸ There are various cardiac and noncardiac etiologies of chest pain presentation. Acute coronary syndrome (ACS) needs to be ruled out first in every patient presenting with chest pain. However, 80% of patients with ACS have no clear diagnostic features on presentation.⁹ The timely diagnosis and treatment of patients with ACS improves their prognosis. Therefore, clinicians tend to start each patient on ACS treatment to reduce the risk, which often leads to increased costs due to unnecessary, time-consuming diagnostic procedures that may place burdens on both the health care system and the patient.¹⁰

Several risk-stratifying tools have been developed in the last few years. Both the GRACE and TIMI risk scores have been designed for risk stratification of patients with proven ACS and not for the chest pain population at the ED.¹¹ Some of these tools are applicable to patients with all types of chest pain presenting to the ED, such as the Manchester Triage System. Other, more selective systems are devoted to the risk stratification of suspected ACS in the ED. One is the HEART score.¹²

The first study on the HEART score—an acronym that stands for History, Electrocardiogram, Age, Risk factors, and Troponin—was done by Backus et al, who proved that the HEART score is an easy, quick, and reliable predictor of outcomes in chest pain patients.¹⁰ The HEART score predicts the short-term incidence of major adverse cardiac events (MACE), which allows clinicians to stratify patients as low-risk, intermediate-risk, and high-risk and to guide their clinical decision-making accordingly. It was developed to provide clinicians with a simple, reliable predictor of cardiac risk on the basis of the lowest score of 0 (very low-risk) up to a score of 10 (very high-risk).

We studied the clinical performance of the HEART score in patients with chest pain, focusing on the efficacy and safety of rapidly identifying patients at risk of MACE. We aimed to determine (1) whether the HEART score is a reliable predictor of outcomes of chest pain patients presenting to the ED; (2) whether the score is feasible in our local settings; and (3) whether it describes the risk profile of patients with and without MACE.

Methods

Setting

Participants were recruited from the ED of King Edward Memorial Hospital, a municipal teaching hospital in Mumbai. The study institute is a tertiary care academic medical center located in Parel, Mumbai, Maharashtra, and is a resource-limited setting serving urban, suburban, and rural populations. Participants requiring urgent attention are first seen by a casualty officer and then referred to the emergency ward. Here, the physician on duty evaluates them and decides on admission to the various wards, like the general ward, medical intensive care unit (ICU), coronary care unit (CCU), etc. The specialist’s opinion may also be obtained before admission. Critically ill patients are initially admitted to the emergency ward and stabilized before being shifted to other areas of the hospital.

Participants

Patients aged 18 years and older presenting with symptoms of acute chest pain or suspected ACS were stratified by priority using the chest pain scoring system—the HEART score. Only patients presenting to the ED were eligible for the study. Informed consent from the patient or next of kin was mandatory for participation in the study.

Patients were determined ineligible for the following reasons: a clear cause for chest pain other than ACS (eg, trauma, diagnosed aortic dissection), persisting or recurrent chest pain caused by rheumatic diseases or cancer (a terminal illness), pregnancy, unable or unwilling to provide informed consent, or incomplete data.

Study design

We conducted a prospective observational study of patients arriving at the tertiary care hospital with a chief complaint of “chest pain” concerning for ACS. All participants provided witnessed written informed consent. Patients were screened over approximately a 3-month period, from July 2019 to October 2019, after acquiring approval from the Institutional Ethics Committee. Any patient who was admitted to the ED due to chest pain, prehospital referrals based on a physician’s suspicions of a heart condition, and previous medical treatment due to ischemic heart disease (IHD) was eligible. All patients were stratified by priority in our ED using the chest pain scoring system—the HEART score—and were followed up by phone within 6 weeks after presenting to the ED, to assess their progress.

We conducted our study to determine the importance of calculating the HEART score in each patient, which will help to correctly place them into low-, intermediate-, and high-risk groups for clinically important, irreversible adverse cardiac events and guide the clinical decision-making. Patients with low risk will avoid costly tests and hospital admissions, thus decreasing the cost of treatment and ensuring timely discharge from the ED. Patients with high risk will be treated immediately, to possibly prevent a life-threatening, ACS-related incident. Thus, the HEART score will serve as a quick and reliable predictor of outcomes in chest pain patients and help clinicians to make accurate diagnostic and therapeutic choices in uncertain situations.

HEART score

The total number of points for History, Electrocardiogram (ECG), Age, Risk factors, and Troponin was noted as the HEART score (Table 1).

For this study, the patient’s history and ECGs were interpreted by internal medicine attending physicians in the ED. The ECG taken in the emergency room was reviewed and classified, and a copy of the admission ECG was added to the file. The recommendation for patients with a HEART score in a particular range was evaluated. Notably, those with a score of 3 or lower led to a recommendation of reassurance and early discharge. Those with a HEART score in the intermediate range (4-6) were admitted to the hospital for further clinical observation and testing, whereas a high HEART score (7-10) led to admission for intensive monitoring and early intervention. In the analysis of HEART score data, we only used those patients having records for all 5 parameters, excluding patients without an ECG or troponin test.

Results

Myocardial infarction (MI) was defined based on Universal Definition of Myocardial Infarction.¹³ Coronary revascularization was defined as angioplasty with or without stent placement or coronary artery bypass surgery.¹⁴ Percutaneous coronary intervention (PCI) was defined as any therapeutic catheter intervention in the coronary arteries. Coronary artery bypass graft (CABG) surgery was defined as any cardiac surgery in which coronary arteries were operated on.

The primary outcomes in this study were the (1) risk stratification of chest pain patients into low-risk, intermediate-risk, and high-risk categories; (2) incidence of a MACE within 6 weeks of initial presentation. MACE consists of acute myocardial infarction (AMI), PCI, CABG, coronary angiography revealing procedurally correctable stenosis managed conservatively, and death due to any cause.

Our secondary outcomes were discharge or death due to any cause within 6 weeks after presentation.

Follow-up

Within 6 weeks after presentation to the ED, a follow-up phone call was placed to assess the patient’s progress. The follow-up focused on the endpoint of MACE, comprising all-cause death, MI, and revascularization. No patient was lost to follow-up.

Statistical analysis

We aimed to find a difference in the 6-week MACE between low-, intermediate-, and high-risk categories of the HEART score. The prevalence of CHD in India is 10%,⁴ and assuming an α of 0.05, we needed a sample of 141 patients from the ED patient population. Continuous variables were presented by mean (SD), and categorical variables as percentages. We used t test and the Mann-Whitney U test for comparison of means for continuous variables, χ² for categorical variables, and Fisher’s exact test for comparison of the categorical variables. Results with P < .05 were considered statistically significant.

We evaluated 141 patients presenting to the ED with chest pain concerning for ACS during the study period, from July 2019 to October 2019. Patients were 57.54 (13.13) years of age. The male to female distribution was 85 to 56. Other patient characteristics are shown in Table 2.

Primary outcomes

The risk stratification of the HEART score in chest pain patients and the incidence of 6-week MACE are outlined in Table 3 and Table 4, respectively.

The distribution of the HEART score’s 5 elements in the groups with or without MACE endpoints is shown in Table 5. Notice the significant differences between the groups. A follow-up phone call was made within 6 weeks after the presentation to the ED to assess the patient’s progress. The 6-week follow-up call data are included in Table 6.

Of 141 patients, 36 patients (25.53%) were diagnosed with MACE within 6 weeks of presentation. An AMI was diagnosed in 24 patients (17.02%). Coronary angiography was performed in 31 of 141 patients (21.99%), 15 patients (10.64%) underwent PCI, and 4 patients (2.84%) underwent CABG. The rest of the patients were treated with medications only.

Myocardial infarction—An AMI was diagnosed in 24 of the 141 patients (17.02%). Twenty-one of those already had positive markers on admission (apparently, these AMI had started before their arrival to the emergency room). One AMI occurred 2 days after admission in a 66-year-old male, and another occurred 10 days after discharge. A further AMI occurred 2 weeks after discharge. All 3 patients belonged to the intermediate-risk group.

Revascularization—Coronary angiography was performed in 31 of 141 patients (21.99%). Revascularization was performed in 19 patients (13.48%), of which 15 were PCIs (10.64%) and 4 were CABGs (2.84%).

Mortality—One patient died from the study population. He was a 72-year-old male who died 14 days after admission. He had a HEART score of 8.

Among the 67 low-risk patients:

• MACE: Coronary angiography was performed in 1 patient (1.49%). Among the 67 patients in the low-risk category, there was no cases of AMI or deaths. The remaining 66 patients (98.51%) had an uneventful recovery following discharge.
• General practitioner (GP) visits/readmissions following discharge: Two of 67 patients (2.99%) had GP visits following discharge, of which 1 was uneventful. The other patient, a 64-year-old male, was readmitted due to a recurrent history of chest pain and underwent coronary angiography.

Among the 44 intermediate-risk patients:

• MACE: Of the 7 of 44 patients (15.91%) who had coronary angiography, 3 patients (6.82%) had AMI, of which 1 occurred 2 days after admission in a 66-year-old male. Two patients had AMI following discharge. There were no deaths. Overall, 42 of 44 patients (95.55%) had an uneventful recovery following discharge.
• GP visits/readmissions following discharge: Three of 44 patients (6.82%) had repeated visits following discharge. One was a GP visit that was uneventful. The remaining 2 patients were diagnosed with AMI and readmitted following discharge. One AMI occurred 10 days after discharge in a patient with a HEART score of 6; another occurred 2 weeks after discharge in a patient with a HEART score of 5.

Among the 30 high-risk patients:

• MACE: Twenty-three of 30 patients (76.67%) underwent coronary angiography. One patient died 5 days after discharge. The patient had a HEART score of 8. Most patients however, had an uneventful recovery following discharge (28, 93.33%).
• GP visits/readmissions following discharge: Five of 30 patients (16.67%) had repeated visits following discharge. Two were uneventful. Two patients had a history of recurrent chest pain that resolved on Sorbitrate. One patient was readmitted 2 weeks following discharge due to a complication: a left ventricular clot was found. The patient had a HEART score of 10.

Secondary outcome—Overall, 140 of 141 patients were discharged. One patient died: a 72-year-old male with a HEART score of 8.

Feasibility—To determine the ease and feasibility of performing a HEART score in chest pain patients presenting to the ED, a survey was distributed to the internal medicine physicians in the ED. In the survey, the Likert scale was used to rate the ease of utilizing the HEART score and whether the physicians found it feasible to use it for risk stratification of their chest pain patients. A total of 12 of 15 respondents (80%) found it “easy” to use. Of the remaining 3 respondents, 2 (13.33%) rated the HEART score “very easy” to use, while 1 (6.66%) considered it “difficult” to work with. None of the respondents said that it was not feasible to perform a HEART score in the ED.

Risk factors for reaching an endpoint:

We compared risk profiles between the patient groups with and without an endpoint. The group of patients with MACE were older and had a higher proportion of males than the group of patients without MACE. Moreover, they also had a higher prevalence of hypertension, type 2 diabetes mellitus, smoking, hypercholesterolemia, prior history of PCI/CABG, and history of stroke. These also showed a significant association with MACE. Obesity was not included in our risk factors as we did not have data collected to measure body mass index. Results are represented in Table 7.

Discussion

Our study described a patient population presenting to an ED with chest pain as their primary complaint. The results of this prospective study confirm that the HEART score is an excellent system to triage chest pain patients. It provides the clinician with a reliable predictor of the outcome (MACE) after the patient’s arrival, based on available clinical data and in a resource-limited setting like ours.

Cardiovascular epidemiology studies indicate that this has become a significant public health problem in India.¹ Several risk scores for ACS have been published in European and American guidelines. However, in the Indian population, minimal data are available on utilization of such a triage score (HEART score) in chest pain patients in the ED in a resource-limited setting, to the best of our knowledge. In India, only 1 such study is reported,¹⁵ at the Sundaram Medical Foundation, a 170-bed community hospital in Chennai. In this study, 13 of 14 patients (92.86%) with a high HEART score had MACE, indicating a sensitivity of 92.86%; in the 44 patients with a low HEART score, 1 patient (2.22%) had MACE, indicating a specificity of 97.78%; and in the 28 patients with a moderate HEART score, 12 patients (42.86%) had MACE.  

In looking for the optimal risk-stratifying system for chest pain patients, we analyzed the HEART score. The first study on the HEART score was done Backus et al, proving that the HEART score is an easy, quick, and reliable predictor of outcomes in chest pain patients.¹⁰ The HEART score had good discriminatory power, too. The C statistic for the HEART score for ACS occurrence shows a value of 0.83. This signifies a good-to-excellent ability to stratify all-cause chest pain patients in the ED for their risk of MACE. The application of the HEART score to our patient population demonstrated that the majority of the patients belonged to the low-risk category, as reported in the first cohort study that applied the HEART score.⁸ The relationship between the HEART score category and occurrence of MACE within 6 weeks showed a curve with 3 different patterns, corresponding to the 3 risk categories defined in the literature.^11,12 The risk stratification of chest pain patients using the 3 categories (0-3, 4-6, 7-10) identified MACE with an incidence similar to the multicenter study of Backus et al,^10,11 but with a greater risk of MACE in the high-risk category (Figure).

Thus, our study confirmed the utility of the HEART score categories to predict the 6-week incidence of MACE. The sensitivity, specificity, and positive and negative predictive values for the established cut-off scores of 4 and 7 are shown in Table 8. The patients in the low-risk category, corresponding to a score < 4, had a very high negative predictive value, thus identifying a small-risk population. The patients in the high-risk category (score ≥ 7) showed a high positive predictive value, allowing the identification of a high-risk population, even in patients with more atypical presentations. Therefore, the HEART score may help clinicians to make accurate management choices by being a strong predictor of both event-free survival and potentially life-threatening cardiac events.^11,12

Our study tested the efficacy of the HEART score pathway in helping clinicians make smart diagnostic and therapeutic choices. It confirmed that the HEART score was accurate in predicting the short-term incidence of MACE, thus stratifying patients according to their risk severity. In our study, 67 of 141 patients (47.52%) had low-risk HEART scores, and we found the 6-week incidence of MACE to be 1.49%. We omitted the diagnostic and treatment evaluation for patients in the low-risk category and moved onto discharge. Overall, 66 of 67 patients (98.51%) in the low-risk category had an uneventful recovery following discharge. Only 2 of 67 these patients (2.99%) of patients had health care utilization following discharge. Therefore, extrapolation based on results demonstrates reduced health care utilization. Previous studies have shown similar results.^9,12,14,16 For instance, in a prospective study conducted in the Netherlands, low-risk patients representing 36.4% of the total were found to have a low MACE rate (1.7%).⁹ These low-risk patients were categorized as appropriate and safe for ED discharge without additional cardiac evaluation or inpatient admission.⁹ Another retrospective study in Portugal,¹² and one in Chennai, India,¹⁵ found the 6-week incidence of MACE to be 2.00% and 2.22%, respectively. The results of the first HEART Pathway Randomized Control Trial¹⁴ showed that the HEART score pathway reduces health care utilization (cardiac testing, hospitalization, and hospital length of stay). The study also showed that these gains occurred without any of the patients that were identified for early discharge, suffering from MACE at 30 days, or secondary increase in cardiac-related hospitalizations. Similar results were obtained by a randomized trial conducted in North Carolina¹⁷ that also demonstrated a reduction in objective cardiac testing, a doubling of the rate of early discharge from the ED, and a reduced length of stay by half a day. Another study using a modified HEART score also demonstrated that when low-risk patients are evaluated with cardiac testing, the likelihood for false positives is high.¹⁶ Hoffman et al also reported that patients randomized to coronary computed tomographic angiography (CCTA) received > 2.5 times more radiation exposure.¹⁶ Thus, low-risk patients may be safely discharged without the need for stress testing or CCTA.

In our study, 30 out of 141 patients (21.28%) had high-risk HEART scores (7-10), and we found the 6-week incidence of MACE to be 90%. Based on the pathway leading to inpatient admission and intensive treatment, 23 of 30 patients (76.67%) patients in our study underwent coronary angiography and further therapeutic treatment. In the high-risk category, 28 of 30 patients (93.33%) patients had an uneventful recovery following discharge. Previous studies have shown similar results. A retrospective study in Portugal showed that 76.9% of the high-risk patients had a 6-week incidence of MACE.¹² In a study in the Netherlands,⁹ 72.7% of high-risk patients had a 6-week incidence of MACE. Therefore, a HEART score of ≥ 7 in patients implies early aggressive treatment, including invasive strategies, when necessary, without noninvasive treatment preceding it.⁸

In terms of intermediate risk, in our study 44 of 141 patients (31.21%) patients had an intermediate-risk HEART score (4-6), and we found the 6-week incidence of MACE to be 18.18%. Based on the pathway, they were kept in the observation ward on admission. In our study, 7 of 44 patients (15.91%) underwent coronary angiography and further treatment; 42 of 44 patients (95.55%) had an uneventful recovery following discharge. In a prospective study in the Netherlands, 46.1% of patients with an intermediate score had a 6-week MACE incidence of 16.6%.¹⁰ Similarly, in another retrospective study in Portugal, the incidence of 6-week MACE in intermediate-risk patients (36.7%) was found to be 15.6%.¹² Therefore, in patients with a HEART score of 4-6 points, immediate discharge is not an option, as this figure indicates a risk of 18.18% for an adverse outcome. These patients should be admitted for clinical observation, treated as an ACS awaiting final diagnosis, and subjected to noninvasive investigations, such as repeated troponin. Using the HEART score as guidance in the treatment of chest pain patients will benefit patients on both sides of the spectrum.^11,12

Our sample presented a male predominance, a wide range of age, and a mean age similar to that of previous studies.^12.16 Some risk factors, we found, can increase significantly the odds of chest pain being of cardiovascular origin, such as male gender, smoking, hypertension, type 2 diabetes mellitus, and hypercholesterolemia. Other studies also reported similar findings.^8,12,16 Risk factors for premature CHD have been quantified in the case-control INTERHEART study.¹ In the INTERHEART study, 8 common risk factors explained > 90% of AMIs in South Asian and Indian patients. The risk factors include dyslipidemia, smoking or tobacco use, known hypertension, known diabetes, abdominal obesity, physical inactivity, low fruit and vegetable intake, and psychosocial stress.¹ Regarding the feasibility of treating physicians using the HEART score in the ED, we observed that, based on the Likert scale, 80% of survey respondents found it easy to use, and 100% found it feasible in the ED.

However, there were certain limitations to our study. It involved a single academic medical center and a small sample size, which limit generalizability of the findings. In addition, troponin levels are not calculated at our institution, as it is a resource-limited setting; therefore, we used a positive and negative as +2 and 0, respectively.

Conclusion

The HEART score provides the clinician with a quick and reliable predictor of outcome of patients with chest pain after arrival to the ED and can be used for triage. For patients with low HEART scores (0-3), short-term MACE can be excluded with greater than 98% certainty. In these patients, one may consider reserved treatment and discharge policies that may also reduce health care utilization. In patients with high HEART scores (7-10), the high risk of MACE (90%) may indicate early aggressive treatment, including invasive strategies, when necessary. Therefore, the HEART score may help clinicians make accurate management choices by being a strong predictor of both event-free survival and potentially life-threatening cardiac events. Age, gender, and cardiovascular risk factors may also be considered in the assessment of patients. This study confirmed the utility of the HEART score categories to predict the 6-week incidence of MACE.

Corresponding author: Smrati Bajpai Tiwari, MD, DNB, FAIMER, Department of Medicine, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Acharya Donde Marg, Parel, Mumbai 400 012, Maharashtra, India; [email protected].

Financial disclosures: None.

From the Department of Internal Medicine, Mount Sinai Health System, Icahn School of Medicine at Mount Sinai, New York, NY (Dr. Gandhi), and the School of Medicine, Seth Gordhandas Sunderdas Medical College, and King Edward Memorial Hospital, Mumbai, India (Drs. Gandhi and Tiwari).

Objective: Calculation of HEART score to (1) stratify patients as low-risk, intermediate-risk, high-risk, and to predict the short-term incidence of major adverse cardiovascular events (MACE), and (2) demonstrate feasibility of HEART score in our local settings.

Design: A prospective cohort study of patients with a chief complaint of chest pain concerning for acute coronary syndrome.

Setting: Participants were recruited from the emergency department (ED) of King Edward Memorial Hospital, a tertiary care academic medical center and a resource-limited setting in Mumbai, India.

Participants: We evaluated 141 patients aged 18 years and older presenting to the ED and stratified them using the HEART score. To assess patients’ progress, a follow-up phone call was made within 6 weeks after presentation to the ED.

Measurements: The primary outcomes were a risk stratification, 6-week occurrence of MACE, and performance of unscheduled revascularization or stress testing. The secondary outcomes were discharge or death.

Results: The 141 participants were stratified into low-risk, intermediate-risk, and high-risk groups: 67 (47.52%), 44 (31.21%), and 30 (21.28%), respectively. The 6-week incidence of MACE in each category was 1.49%, 18.18%, and 90%, respectively. An acute myocardial infarction was diagnosed in 24 patients (17.02%), 15 patients (10.64%) underwent percutaneous coronary intervention (PCI), and 4 patients (2.84%) underwent coronary artery bypass graft (CABG). Overall, 98.5% of low-risk patients and 93.33% of high-risk patients had an uneventful recovery following discharge; therefore, extrapolation based on results demonstrated reduced health care utilization. All the survey respondents found the HEART score to be feasible. The patient characteristics and risk profile of the patients with and without MACE demonstrated that: patients with MACE were older and had a higher proportion of males, hypertension, type 2 diabetes mellitus, smoking, hypercholesterolemia, prior history of PCI/CABG, and history of stroke.

Conclusion: The HEART score seems to be a useful tool for risk stratification and a reliable predictor of outcomes in chest pain patients and can therefore be used for triage.

Keywords: chest pain; emergency department; HEART score; acute coronary syndrome; major adverse cardiac events; myocardial infarction; revascularization.

Cardiovascular diseases (CVDs), especially coronary heart disease (CHD), have epidemic proportions worldwide. Globally, in 2012, CVD led to 17.5 million deaths,^1,2 with more than 75% of them occurring in developing countries. In contrast to developed countries, where mortality from CHD is rapidly declining, it is increasing in developing countries.^1,3 Current estimates from epidemiologic studies from various parts of India indicate the prevalence of CHD in India to be between 7% and 13% in urban populations and 2% and 7% in rural populations.⁴

Premature mortality in terms of years of life lost because of CVD in India increased by 59% over a 20-year span, from 23.2 million in 1990 to 37 million in 2010.⁵ Studies conducted in Mumbai (Mumbai Cohort Study) reported very high CVD mortality rates, approaching 500 per 100 000 for men and 250 per 100 000 for women.^6,7 However, to the best of our knowledge, in the Indian population, there are minimal data on utilization of a triage score, such as the HEART score, in chest pain patients in the emergency department (ED) in a resource-limited setting.

The most common reason for admitting patients to the ED is chest pain.⁸ There are various cardiac and noncardiac etiologies of chest pain presentation. Acute coronary syndrome (ACS) needs to be ruled out first in every patient presenting with chest pain. However, 80% of patients with ACS have no clear diagnostic features on presentation.⁹ The timely diagnosis and treatment of patients with ACS improves their prognosis. Therefore, clinicians tend to start each patient on ACS treatment to reduce the risk, which often leads to increased costs due to unnecessary, time-consuming diagnostic procedures that may place burdens on both the health care system and the patient.¹⁰

Several risk-stratifying tools have been developed in the last few years. Both the GRACE and TIMI risk scores have been designed for risk stratification of patients with proven ACS and not for the chest pain population at the ED.¹¹ Some of these tools are applicable to patients with all types of chest pain presenting to the ED, such as the Manchester Triage System. Other, more selective systems are devoted to the risk stratification of suspected ACS in the ED. One is the HEART score.¹²

The first study on the HEART score—an acronym that stands for History, Electrocardiogram, Age, Risk factors, and Troponin—was done by Backus et al, who proved that the HEART score is an easy, quick, and reliable predictor of outcomes in chest pain patients.¹⁰ The HEART score predicts the short-term incidence of major adverse cardiac events (MACE), which allows clinicians to stratify patients as low-risk, intermediate-risk, and high-risk and to guide their clinical decision-making accordingly. It was developed to provide clinicians with a simple, reliable predictor of cardiac risk on the basis of the lowest score of 0 (very low-risk) up to a score of 10 (very high-risk).

We studied the clinical performance of the HEART score in patients with chest pain, focusing on the efficacy and safety of rapidly identifying patients at risk of MACE. We aimed to determine (1) whether the HEART score is a reliable predictor of outcomes of chest pain patients presenting to the ED; (2) whether the score is feasible in our local settings; and (3) whether it describes the risk profile of patients with and without MACE.

Methods

Setting

Participants were recruited from the ED of King Edward Memorial Hospital, a municipal teaching hospital in Mumbai. The study institute is a tertiary care academic medical center located in Parel, Mumbai, Maharashtra, and is a resource-limited setting serving urban, suburban, and rural populations. Participants requiring urgent attention are first seen by a casualty officer and then referred to the emergency ward. Here, the physician on duty evaluates them and decides on admission to the various wards, like the general ward, medical intensive care unit (ICU), coronary care unit (CCU), etc. The specialist’s opinion may also be obtained before admission. Critically ill patients are initially admitted to the emergency ward and stabilized before being shifted to other areas of the hospital.

Participants

Patients aged 18 years and older presenting with symptoms of acute chest pain or suspected ACS were stratified by priority using the chest pain scoring system—the HEART score. Only patients presenting to the ED were eligible for the study. Informed consent from the patient or next of kin was mandatory for participation in the study.

Patients were determined ineligible for the following reasons: a clear cause for chest pain other than ACS (eg, trauma, diagnosed aortic dissection), persisting or recurrent chest pain caused by rheumatic diseases or cancer (a terminal illness), pregnancy, unable or unwilling to provide informed consent, or incomplete data.

Study design

We conducted a prospective observational study of patients arriving at the tertiary care hospital with a chief complaint of “chest pain” concerning for ACS. All participants provided witnessed written informed consent. Patients were screened over approximately a 3-month period, from July 2019 to October 2019, after acquiring approval from the Institutional Ethics Committee. Any patient who was admitted to the ED due to chest pain, prehospital referrals based on a physician’s suspicions of a heart condition, and previous medical treatment due to ischemic heart disease (IHD) was eligible. All patients were stratified by priority in our ED using the chest pain scoring system—the HEART score—and were followed up by phone within 6 weeks after presenting to the ED, to assess their progress.

We conducted our study to determine the importance of calculating the HEART score in each patient, which will help to correctly place them into low-, intermediate-, and high-risk groups for clinically important, irreversible adverse cardiac events and guide the clinical decision-making. Patients with low risk will avoid costly tests and hospital admissions, thus decreasing the cost of treatment and ensuring timely discharge from the ED. Patients with high risk will be treated immediately, to possibly prevent a life-threatening, ACS-related incident. Thus, the HEART score will serve as a quick and reliable predictor of outcomes in chest pain patients and help clinicians to make accurate diagnostic and therapeutic choices in uncertain situations.

HEART score

The total number of points for History, Electrocardiogram (ECG), Age, Risk factors, and Troponin was noted as the HEART score (Table 1).

For this study, the patient’s history and ECGs were interpreted by internal medicine attending physicians in the ED. The ECG taken in the emergency room was reviewed and classified, and a copy of the admission ECG was added to the file. The recommendation for patients with a HEART score in a particular range was evaluated. Notably, those with a score of 3 or lower led to a recommendation of reassurance and early discharge. Those with a HEART score in the intermediate range (4-6) were admitted to the hospital for further clinical observation and testing, whereas a high HEART score (7-10) led to admission for intensive monitoring and early intervention. In the analysis of HEART score data, we only used those patients having records for all 5 parameters, excluding patients without an ECG or troponin test.

Results

Myocardial infarction (MI) was defined based on Universal Definition of Myocardial Infarction.¹³ Coronary revascularization was defined as angioplasty with or without stent placement or coronary artery bypass surgery.¹⁴ Percutaneous coronary intervention (PCI) was defined as any therapeutic catheter intervention in the coronary arteries. Coronary artery bypass graft (CABG) surgery was defined as any cardiac surgery in which coronary arteries were operated on.

The primary outcomes in this study were the (1) risk stratification of chest pain patients into low-risk, intermediate-risk, and high-risk categories; (2) incidence of a MACE within 6 weeks of initial presentation. MACE consists of acute myocardial infarction (AMI), PCI, CABG, coronary angiography revealing procedurally correctable stenosis managed conservatively, and death due to any cause.

Our secondary outcomes were discharge or death due to any cause within 6 weeks after presentation.

Follow-up

Within 6 weeks after presentation to the ED, a follow-up phone call was placed to assess the patient’s progress. The follow-up focused on the endpoint of MACE, comprising all-cause death, MI, and revascularization. No patient was lost to follow-up.

Statistical analysis

We aimed to find a difference in the 6-week MACE between low-, intermediate-, and high-risk categories of the HEART score. The prevalence of CHD in India is 10%,⁴ and assuming an α of 0.05, we needed a sample of 141 patients from the ED patient population. Continuous variables were presented by mean (SD), and categorical variables as percentages. We used t test and the Mann-Whitney U test for comparison of means for continuous variables, χ² for categorical variables, and Fisher’s exact test for comparison of the categorical variables. Results with P < .05 were considered statistically significant.

We evaluated 141 patients presenting to the ED with chest pain concerning for ACS during the study period, from July 2019 to October 2019. Patients were 57.54 (13.13) years of age. The male to female distribution was 85 to 56. Other patient characteristics are shown in Table 2.

Primary outcomes

The risk stratification of the HEART score in chest pain patients and the incidence of 6-week MACE are outlined in Table 3 and Table 4, respectively.

The distribution of the HEART score’s 5 elements in the groups with or without MACE endpoints is shown in Table 5. Notice the significant differences between the groups. A follow-up phone call was made within 6 weeks after the presentation to the ED to assess the patient’s progress. The 6-week follow-up call data are included in Table 6.

Of 141 patients, 36 patients (25.53%) were diagnosed with MACE within 6 weeks of presentation. An AMI was diagnosed in 24 patients (17.02%). Coronary angiography was performed in 31 of 141 patients (21.99%), 15 patients (10.64%) underwent PCI, and 4 patients (2.84%) underwent CABG. The rest of the patients were treated with medications only.

Myocardial infarction—An AMI was diagnosed in 24 of the 141 patients (17.02%). Twenty-one of those already had positive markers on admission (apparently, these AMI had started before their arrival to the emergency room). One AMI occurred 2 days after admission in a 66-year-old male, and another occurred 10 days after discharge. A further AMI occurred 2 weeks after discharge. All 3 patients belonged to the intermediate-risk group.

Revascularization—Coronary angiography was performed in 31 of 141 patients (21.99%). Revascularization was performed in 19 patients (13.48%), of which 15 were PCIs (10.64%) and 4 were CABGs (2.84%).

Mortality—One patient died from the study population. He was a 72-year-old male who died 14 days after admission. He had a HEART score of 8.

Among the 67 low-risk patients:

• MACE: Coronary angiography was performed in 1 patient (1.49%). Among the 67 patients in the low-risk category, there was no cases of AMI or deaths. The remaining 66 patients (98.51%) had an uneventful recovery following discharge.
• General practitioner (GP) visits/readmissions following discharge: Two of 67 patients (2.99%) had GP visits following discharge, of which 1 was uneventful. The other patient, a 64-year-old male, was readmitted due to a recurrent history of chest pain and underwent coronary angiography.

Among the 44 intermediate-risk patients:

• MACE: Of the 7 of 44 patients (15.91%) who had coronary angiography, 3 patients (6.82%) had AMI, of which 1 occurred 2 days after admission in a 66-year-old male. Two patients had AMI following discharge. There were no deaths. Overall, 42 of 44 patients (95.55%) had an uneventful recovery following discharge.
• GP visits/readmissions following discharge: Three of 44 patients (6.82%) had repeated visits following discharge. One was a GP visit that was uneventful. The remaining 2 patients were diagnosed with AMI and readmitted following discharge. One AMI occurred 10 days after discharge in a patient with a HEART score of 6; another occurred 2 weeks after discharge in a patient with a HEART score of 5.

Among the 30 high-risk patients:

• MACE: Twenty-three of 30 patients (76.67%) underwent coronary angiography. One patient died 5 days after discharge. The patient had a HEART score of 8. Most patients however, had an uneventful recovery following discharge (28, 93.33%).
• GP visits/readmissions following discharge: Five of 30 patients (16.67%) had repeated visits following discharge. Two were uneventful. Two patients had a history of recurrent chest pain that resolved on Sorbitrate. One patient was readmitted 2 weeks following discharge due to a complication: a left ventricular clot was found. The patient had a HEART score of 10.

Secondary outcome—Overall, 140 of 141 patients were discharged. One patient died: a 72-year-old male with a HEART score of 8.

Feasibility—To determine the ease and feasibility of performing a HEART score in chest pain patients presenting to the ED, a survey was distributed to the internal medicine physicians in the ED. In the survey, the Likert scale was used to rate the ease of utilizing the HEART score and whether the physicians found it feasible to use it for risk stratification of their chest pain patients. A total of 12 of 15 respondents (80%) found it “easy” to use. Of the remaining 3 respondents, 2 (13.33%) rated the HEART score “very easy” to use, while 1 (6.66%) considered it “difficult” to work with. None of the respondents said that it was not feasible to perform a HEART score in the ED.

Risk factors for reaching an endpoint:

We compared risk profiles between the patient groups with and without an endpoint. The group of patients with MACE were older and had a higher proportion of males than the group of patients without MACE. Moreover, they also had a higher prevalence of hypertension, type 2 diabetes mellitus, smoking, hypercholesterolemia, prior history of PCI/CABG, and history of stroke. These also showed a significant association with MACE. Obesity was not included in our risk factors as we did not have data collected to measure body mass index. Results are represented in Table 7.

Discussion

Our study described a patient population presenting to an ED with chest pain as their primary complaint. The results of this prospective study confirm that the HEART score is an excellent system to triage chest pain patients. It provides the clinician with a reliable predictor of the outcome (MACE) after the patient’s arrival, based on available clinical data and in a resource-limited setting like ours.

Cardiovascular epidemiology studies indicate that this has become a significant public health problem in India.¹ Several risk scores for ACS have been published in European and American guidelines. However, in the Indian population, minimal data are available on utilization of such a triage score (HEART score) in chest pain patients in the ED in a resource-limited setting, to the best of our knowledge. In India, only 1 such study is reported,¹⁵ at the Sundaram Medical Foundation, a 170-bed community hospital in Chennai. In this study, 13 of 14 patients (92.86%) with a high HEART score had MACE, indicating a sensitivity of 92.86%; in the 44 patients with a low HEART score, 1 patient (2.22%) had MACE, indicating a specificity of 97.78%; and in the 28 patients with a moderate HEART score, 12 patients (42.86%) had MACE.  

In looking for the optimal risk-stratifying system for chest pain patients, we analyzed the HEART score. The first study on the HEART score was done Backus et al, proving that the HEART score is an easy, quick, and reliable predictor of outcomes in chest pain patients.¹⁰ The HEART score had good discriminatory power, too. The C statistic for the HEART score for ACS occurrence shows a value of 0.83. This signifies a good-to-excellent ability to stratify all-cause chest pain patients in the ED for their risk of MACE. The application of the HEART score to our patient population demonstrated that the majority of the patients belonged to the low-risk category, as reported in the first cohort study that applied the HEART score.⁸ The relationship between the HEART score category and occurrence of MACE within 6 weeks showed a curve with 3 different patterns, corresponding to the 3 risk categories defined in the literature.^11,12 The risk stratification of chest pain patients using the 3 categories (0-3, 4-6, 7-10) identified MACE with an incidence similar to the multicenter study of Backus et al,^10,11 but with a greater risk of MACE in the high-risk category (Figure).

Thus, our study confirmed the utility of the HEART score categories to predict the 6-week incidence of MACE. The sensitivity, specificity, and positive and negative predictive values for the established cut-off scores of 4 and 7 are shown in Table 8. The patients in the low-risk category, corresponding to a score < 4, had a very high negative predictive value, thus identifying a small-risk population. The patients in the high-risk category (score ≥ 7) showed a high positive predictive value, allowing the identification of a high-risk population, even in patients with more atypical presentations. Therefore, the HEART score may help clinicians to make accurate management choices by being a strong predictor of both event-free survival and potentially life-threatening cardiac events.^11,12

Our study tested the efficacy of the HEART score pathway in helping clinicians make smart diagnostic and therapeutic choices. It confirmed that the HEART score was accurate in predicting the short-term incidence of MACE, thus stratifying patients according to their risk severity. In our study, 67 of 141 patients (47.52%) had low-risk HEART scores, and we found the 6-week incidence of MACE to be 1.49%. We omitted the diagnostic and treatment evaluation for patients in the low-risk category and moved onto discharge. Overall, 66 of 67 patients (98.51%) in the low-risk category had an uneventful recovery following discharge. Only 2 of 67 these patients (2.99%) of patients had health care utilization following discharge. Therefore, extrapolation based on results demonstrates reduced health care utilization. Previous studies have shown similar results.^9,12,14,16 For instance, in a prospective study conducted in the Netherlands, low-risk patients representing 36.4% of the total were found to have a low MACE rate (1.7%).⁹ These low-risk patients were categorized as appropriate and safe for ED discharge without additional cardiac evaluation or inpatient admission.⁹ Another retrospective study in Portugal,¹² and one in Chennai, India,¹⁵ found the 6-week incidence of MACE to be 2.00% and 2.22%, respectively. The results of the first HEART Pathway Randomized Control Trial¹⁴ showed that the HEART score pathway reduces health care utilization (cardiac testing, hospitalization, and hospital length of stay). The study also showed that these gains occurred without any of the patients that were identified for early discharge, suffering from MACE at 30 days, or secondary increase in cardiac-related hospitalizations. Similar results were obtained by a randomized trial conducted in North Carolina¹⁷ that also demonstrated a reduction in objective cardiac testing, a doubling of the rate of early discharge from the ED, and a reduced length of stay by half a day. Another study using a modified HEART score also demonstrated that when low-risk patients are evaluated with cardiac testing, the likelihood for false positives is high.¹⁶ Hoffman et al also reported that patients randomized to coronary computed tomographic angiography (CCTA) received > 2.5 times more radiation exposure.¹⁶ Thus, low-risk patients may be safely discharged without the need for stress testing or CCTA.

In our study, 30 out of 141 patients (21.28%) had high-risk HEART scores (7-10), and we found the 6-week incidence of MACE to be 90%. Based on the pathway leading to inpatient admission and intensive treatment, 23 of 30 patients (76.67%) patients in our study underwent coronary angiography and further therapeutic treatment. In the high-risk category, 28 of 30 patients (93.33%) patients had an uneventful recovery following discharge. Previous studies have shown similar results. A retrospective study in Portugal showed that 76.9% of the high-risk patients had a 6-week incidence of MACE.¹² In a study in the Netherlands,⁹ 72.7% of high-risk patients had a 6-week incidence of MACE. Therefore, a HEART score of ≥ 7 in patients implies early aggressive treatment, including invasive strategies, when necessary, without noninvasive treatment preceding it.⁸

In terms of intermediate risk, in our study 44 of 141 patients (31.21%) patients had an intermediate-risk HEART score (4-6), and we found the 6-week incidence of MACE to be 18.18%. Based on the pathway, they were kept in the observation ward on admission. In our study, 7 of 44 patients (15.91%) underwent coronary angiography and further treatment; 42 of 44 patients (95.55%) had an uneventful recovery following discharge. In a prospective study in the Netherlands, 46.1% of patients with an intermediate score had a 6-week MACE incidence of 16.6%.¹⁰ Similarly, in another retrospective study in Portugal, the incidence of 6-week MACE in intermediate-risk patients (36.7%) was found to be 15.6%.¹² Therefore, in patients with a HEART score of 4-6 points, immediate discharge is not an option, as this figure indicates a risk of 18.18% for an adverse outcome. These patients should be admitted for clinical observation, treated as an ACS awaiting final diagnosis, and subjected to noninvasive investigations, such as repeated troponin. Using the HEART score as guidance in the treatment of chest pain patients will benefit patients on both sides of the spectrum.^11,12

Our sample presented a male predominance, a wide range of age, and a mean age similar to that of previous studies.^12.16 Some risk factors, we found, can increase significantly the odds of chest pain being of cardiovascular origin, such as male gender, smoking, hypertension, type 2 diabetes mellitus, and hypercholesterolemia. Other studies also reported similar findings.^8,12,16 Risk factors for premature CHD have been quantified in the case-control INTERHEART study.¹ In the INTERHEART study, 8 common risk factors explained > 90% of AMIs in South Asian and Indian patients. The risk factors include dyslipidemia, smoking or tobacco use, known hypertension, known diabetes, abdominal obesity, physical inactivity, low fruit and vegetable intake, and psychosocial stress.¹ Regarding the feasibility of treating physicians using the HEART score in the ED, we observed that, based on the Likert scale, 80% of survey respondents found it easy to use, and 100% found it feasible in the ED.

However, there were certain limitations to our study. It involved a single academic medical center and a small sample size, which limit generalizability of the findings. In addition, troponin levels are not calculated at our institution, as it is a resource-limited setting; therefore, we used a positive and negative as +2 and 0, respectively.

Conclusion

The HEART score provides the clinician with a quick and reliable predictor of outcome of patients with chest pain after arrival to the ED and can be used for triage. For patients with low HEART scores (0-3), short-term MACE can be excluded with greater than 98% certainty. In these patients, one may consider reserved treatment and discharge policies that may also reduce health care utilization. In patients with high HEART scores (7-10), the high risk of MACE (90%) may indicate early aggressive treatment, including invasive strategies, when necessary. Therefore, the HEART score may help clinicians make accurate management choices by being a strong predictor of both event-free survival and potentially life-threatening cardiac events. Age, gender, and cardiovascular risk factors may also be considered in the assessment of patients. This study confirmed the utility of the HEART score categories to predict the 6-week incidence of MACE.

Corresponding author: Smrati Bajpai Tiwari, MD, DNB, FAIMER, Department of Medicine, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Acharya Donde Marg, Parel, Mumbai 400 012, Maharashtra, India; [email protected].

Financial disclosures: None.

From the Department of Internal Medicine, Mount Sinai Health System, Icahn School of Medicine at Mount Sinai, New York, NY (Dr. Gandhi), and the School of Medicine, Seth Gordhandas Sunderdas Medical College, and King Edward Memorial Hospital, Mumbai, India (Drs. Gandhi and Tiwari).

Objective: Calculation of HEART score to (1) stratify patients as low-risk, intermediate-risk, high-risk, and to predict the short-term incidence of major adverse cardiovascular events (MACE), and (2) demonstrate feasibility of HEART score in our local settings.

Design: A prospective cohort study of patients with a chief complaint of chest pain concerning for acute coronary syndrome.

Setting: Participants were recruited from the emergency department (ED) of King Edward Memorial Hospital, a tertiary care academic medical center and a resource-limited setting in Mumbai, India.

Participants: We evaluated 141 patients aged 18 years and older presenting to the ED and stratified them using the HEART score. To assess patients’ progress, a follow-up phone call was made within 6 weeks after presentation to the ED.

Measurements: The primary outcomes were a risk stratification, 6-week occurrence of MACE, and performance of unscheduled revascularization or stress testing. The secondary outcomes were discharge or death.

Results: The 141 participants were stratified into low-risk, intermediate-risk, and high-risk groups: 67 (47.52%), 44 (31.21%), and 30 (21.28%), respectively. The 6-week incidence of MACE in each category was 1.49%, 18.18%, and 90%, respectively. An acute myocardial infarction was diagnosed in 24 patients (17.02%), 15 patients (10.64%) underwent percutaneous coronary intervention (PCI), and 4 patients (2.84%) underwent coronary artery bypass graft (CABG). Overall, 98.5% of low-risk patients and 93.33% of high-risk patients had an uneventful recovery following discharge; therefore, extrapolation based on results demonstrated reduced health care utilization. All the survey respondents found the HEART score to be feasible. The patient characteristics and risk profile of the patients with and without MACE demonstrated that: patients with MACE were older and had a higher proportion of males, hypertension, type 2 diabetes mellitus, smoking, hypercholesterolemia, prior history of PCI/CABG, and history of stroke.

Conclusion: The HEART score seems to be a useful tool for risk stratification and a reliable predictor of outcomes in chest pain patients and can therefore be used for triage.

Keywords: chest pain; emergency department; HEART score; acute coronary syndrome; major adverse cardiac events; myocardial infarction; revascularization.

Cardiovascular diseases (CVDs), especially coronary heart disease (CHD), have epidemic proportions worldwide. Globally, in 2012, CVD led to 17.5 million deaths,^1,2 with more than 75% of them occurring in developing countries. In contrast to developed countries, where mortality from CHD is rapidly declining, it is increasing in developing countries.^1,3 Current estimates from epidemiologic studies from various parts of India indicate the prevalence of CHD in India to be between 7% and 13% in urban populations and 2% and 7% in rural populations.⁴

Premature mortality in terms of years of life lost because of CVD in India increased by 59% over a 20-year span, from 23.2 million in 1990 to 37 million in 2010.⁵ Studies conducted in Mumbai (Mumbai Cohort Study) reported very high CVD mortality rates, approaching 500 per 100 000 for men and 250 per 100 000 for women.^6,7 However, to the best of our knowledge, in the Indian population, there are minimal data on utilization of a triage score, such as the HEART score, in chest pain patients in the emergency department (ED) in a resource-limited setting.

The most common reason for admitting patients to the ED is chest pain.⁸ There are various cardiac and noncardiac etiologies of chest pain presentation. Acute coronary syndrome (ACS) needs to be ruled out first in every patient presenting with chest pain. However, 80% of patients with ACS have no clear diagnostic features on presentation.⁹ The timely diagnosis and treatment of patients with ACS improves their prognosis. Therefore, clinicians tend to start each patient on ACS treatment to reduce the risk, which often leads to increased costs due to unnecessary, time-consuming diagnostic procedures that may place burdens on both the health care system and the patient.¹⁰

Several risk-stratifying tools have been developed in the last few years. Both the GRACE and TIMI risk scores have been designed for risk stratification of patients with proven ACS and not for the chest pain population at the ED.¹¹ Some of these tools are applicable to patients with all types of chest pain presenting to the ED, such as the Manchester Triage System. Other, more selective systems are devoted to the risk stratification of suspected ACS in the ED. One is the HEART score.¹²

The first study on the HEART score—an acronym that stands for History, Electrocardiogram, Age, Risk factors, and Troponin—was done by Backus et al, who proved that the HEART score is an easy, quick, and reliable predictor of outcomes in chest pain patients.¹⁰ The HEART score predicts the short-term incidence of major adverse cardiac events (MACE), which allows clinicians to stratify patients as low-risk, intermediate-risk, and high-risk and to guide their clinical decision-making accordingly. It was developed to provide clinicians with a simple, reliable predictor of cardiac risk on the basis of the lowest score of 0 (very low-risk) up to a score of 10 (very high-risk).

We studied the clinical performance of the HEART score in patients with chest pain, focusing on the efficacy and safety of rapidly identifying patients at risk of MACE. We aimed to determine (1) whether the HEART score is a reliable predictor of outcomes of chest pain patients presenting to the ED; (2) whether the score is feasible in our local settings; and (3) whether it describes the risk profile of patients with and without MACE.

Methods

Setting

Participants were recruited from the ED of King Edward Memorial Hospital, a municipal teaching hospital in Mumbai. The study institute is a tertiary care academic medical center located in Parel, Mumbai, Maharashtra, and is a resource-limited setting serving urban, suburban, and rural populations. Participants requiring urgent attention are first seen by a casualty officer and then referred to the emergency ward. Here, the physician on duty evaluates them and decides on admission to the various wards, like the general ward, medical intensive care unit (ICU), coronary care unit (CCU), etc. The specialist’s opinion may also be obtained before admission. Critically ill patients are initially admitted to the emergency ward and stabilized before being shifted to other areas of the hospital.

Participants

Patients aged 18 years and older presenting with symptoms of acute chest pain or suspected ACS were stratified by priority using the chest pain scoring system—the HEART score. Only patients presenting to the ED were eligible for the study. Informed consent from the patient or next of kin was mandatory for participation in the study.

Patients were determined ineligible for the following reasons: a clear cause for chest pain other than ACS (eg, trauma, diagnosed aortic dissection), persisting or recurrent chest pain caused by rheumatic diseases or cancer (a terminal illness), pregnancy, unable or unwilling to provide informed consent, or incomplete data.

Study design

We conducted a prospective observational study of patients arriving at the tertiary care hospital with a chief complaint of “chest pain” concerning for ACS. All participants provided witnessed written informed consent. Patients were screened over approximately a 3-month period, from July 2019 to October 2019, after acquiring approval from the Institutional Ethics Committee. Any patient who was admitted to the ED due to chest pain, prehospital referrals based on a physician’s suspicions of a heart condition, and previous medical treatment due to ischemic heart disease (IHD) was eligible. All patients were stratified by priority in our ED using the chest pain scoring system—the HEART score—and were followed up by phone within 6 weeks after presenting to the ED, to assess their progress.

We conducted our study to determine the importance of calculating the HEART score in each patient, which will help to correctly place them into low-, intermediate-, and high-risk groups for clinically important, irreversible adverse cardiac events and guide the clinical decision-making. Patients with low risk will avoid costly tests and hospital admissions, thus decreasing the cost of treatment and ensuring timely discharge from the ED. Patients with high risk will be treated immediately, to possibly prevent a life-threatening, ACS-related incident. Thus, the HEART score will serve as a quick and reliable predictor of outcomes in chest pain patients and help clinicians to make accurate diagnostic and therapeutic choices in uncertain situations.

HEART score

The total number of points for History, Electrocardiogram (ECG), Age, Risk factors, and Troponin was noted as the HEART score (Table 1).

For this study, the patient’s history and ECGs were interpreted by internal medicine attending physicians in the ED. The ECG taken in the emergency room was reviewed and classified, and a copy of the admission ECG was added to the file. The recommendation for patients with a HEART score in a particular range was evaluated. Notably, those with a score of 3 or lower led to a recommendation of reassurance and early discharge. Those with a HEART score in the intermediate range (4-6) were admitted to the hospital for further clinical observation and testing, whereas a high HEART score (7-10) led to admission for intensive monitoring and early intervention. In the analysis of HEART score data, we only used those patients having records for all 5 parameters, excluding patients without an ECG or troponin test.

Results

Myocardial infarction (MI) was defined based on Universal Definition of Myocardial Infarction.¹³ Coronary revascularization was defined as angioplasty with or without stent placement or coronary artery bypass surgery.¹⁴ Percutaneous coronary intervention (PCI) was defined as any therapeutic catheter intervention in the coronary arteries. Coronary artery bypass graft (CABG) surgery was defined as any cardiac surgery in which coronary arteries were operated on.

The primary outcomes in this study were the (1) risk stratification of chest pain patients into low-risk, intermediate-risk, and high-risk categories; (2) incidence of a MACE within 6 weeks of initial presentation. MACE consists of acute myocardial infarction (AMI), PCI, CABG, coronary angiography revealing procedurally correctable stenosis managed conservatively, and death due to any cause.

Our secondary outcomes were discharge or death due to any cause within 6 weeks after presentation.

Follow-up

Within 6 weeks after presentation to the ED, a follow-up phone call was placed to assess the patient’s progress. The follow-up focused on the endpoint of MACE, comprising all-cause death, MI, and revascularization. No patient was lost to follow-up.

Statistical analysis

We aimed to find a difference in the 6-week MACE between low-, intermediate-, and high-risk categories of the HEART score. The prevalence of CHD in India is 10%,⁴ and assuming an α of 0.05, we needed a sample of 141 patients from the ED patient population. Continuous variables were presented by mean (SD), and categorical variables as percentages. We used t test and the Mann-Whitney U test for comparison of means for continuous variables, χ² for categorical variables, and Fisher’s exact test for comparison of the categorical variables. Results with P < .05 were considered statistically significant.

We evaluated 141 patients presenting to the ED with chest pain concerning for ACS during the study period, from July 2019 to October 2019. Patients were 57.54 (13.13) years of age. The male to female distribution was 85 to 56. Other patient characteristics are shown in Table 2.

Primary outcomes

The risk stratification of the HEART score in chest pain patients and the incidence of 6-week MACE are outlined in Table 3 and Table 4, respectively.

The distribution of the HEART score’s 5 elements in the groups with or without MACE endpoints is shown in Table 5. Notice the significant differences between the groups. A follow-up phone call was made within 6 weeks after the presentation to the ED to assess the patient’s progress. The 6-week follow-up call data are included in Table 6.

Of 141 patients, 36 patients (25.53%) were diagnosed with MACE within 6 weeks of presentation. An AMI was diagnosed in 24 patients (17.02%). Coronary angiography was performed in 31 of 141 patients (21.99%), 15 patients (10.64%) underwent PCI, and 4 patients (2.84%) underwent CABG. The rest of the patients were treated with medications only.

Myocardial infarction—An AMI was diagnosed in 24 of the 141 patients (17.02%). Twenty-one of those already had positive markers on admission (apparently, these AMI had started before their arrival to the emergency room). One AMI occurred 2 days after admission in a 66-year-old male, and another occurred 10 days after discharge. A further AMI occurred 2 weeks after discharge. All 3 patients belonged to the intermediate-risk group.

Revascularization—Coronary angiography was performed in 31 of 141 patients (21.99%). Revascularization was performed in 19 patients (13.48%), of which 15 were PCIs (10.64%) and 4 were CABGs (2.84%).

Mortality—One patient died from the study population. He was a 72-year-old male who died 14 days after admission. He had a HEART score of 8.

Among the 67 low-risk patients:

• MACE: Coronary angiography was performed in 1 patient (1.49%). Among the 67 patients in the low-risk category, there was no cases of AMI or deaths. The remaining 66 patients (98.51%) had an uneventful recovery following discharge.
• General practitioner (GP) visits/readmissions following discharge: Two of 67 patients (2.99%) had GP visits following discharge, of which 1 was uneventful. The other patient, a 64-year-old male, was readmitted due to a recurrent history of chest pain and underwent coronary angiography.

Among the 44 intermediate-risk patients:

• MACE: Of the 7 of 44 patients (15.91%) who had coronary angiography, 3 patients (6.82%) had AMI, of which 1 occurred 2 days after admission in a 66-year-old male. Two patients had AMI following discharge. There were no deaths. Overall, 42 of 44 patients (95.55%) had an uneventful recovery following discharge.
• GP visits/readmissions following discharge: Three of 44 patients (6.82%) had repeated visits following discharge. One was a GP visit that was uneventful. The remaining 2 patients were diagnosed with AMI and readmitted following discharge. One AMI occurred 10 days after discharge in a patient with a HEART score of 6; another occurred 2 weeks after discharge in a patient with a HEART score of 5.

Among the 30 high-risk patients:

• MACE: Twenty-three of 30 patients (76.67%) underwent coronary angiography. One patient died 5 days after discharge. The patient had a HEART score of 8. Most patients however, had an uneventful recovery following discharge (28, 93.33%).
• GP visits/readmissions following discharge: Five of 30 patients (16.67%) had repeated visits following discharge. Two were uneventful. Two patients had a history of recurrent chest pain that resolved on Sorbitrate. One patient was readmitted 2 weeks following discharge due to a complication: a left ventricular clot was found. The patient had a HEART score of 10.

Secondary outcome—Overall, 140 of 141 patients were discharged. One patient died: a 72-year-old male with a HEART score of 8.

Feasibility—To determine the ease and feasibility of performing a HEART score in chest pain patients presenting to the ED, a survey was distributed to the internal medicine physicians in the ED. In the survey, the Likert scale was used to rate the ease of utilizing the HEART score and whether the physicians found it feasible to use it for risk stratification of their chest pain patients. A total of 12 of 15 respondents (80%) found it “easy” to use. Of the remaining 3 respondents, 2 (13.33%) rated the HEART score “very easy” to use, while 1 (6.66%) considered it “difficult” to work with. None of the respondents said that it was not feasible to perform a HEART score in the ED.

Risk factors for reaching an endpoint:

We compared risk profiles between the patient groups with and without an endpoint. The group of patients with MACE were older and had a higher proportion of males than the group of patients without MACE. Moreover, they also had a higher prevalence of hypertension, type 2 diabetes mellitus, smoking, hypercholesterolemia, prior history of PCI/CABG, and history of stroke. These also showed a significant association with MACE. Obesity was not included in our risk factors as we did not have data collected to measure body mass index. Results are represented in Table 7.

Discussion

Our study described a patient population presenting to an ED with chest pain as their primary complaint. The results of this prospective study confirm that the HEART score is an excellent system to triage chest pain patients. It provides the clinician with a reliable predictor of the outcome (MACE) after the patient’s arrival, based on available clinical data and in a resource-limited setting like ours.

Cardiovascular epidemiology studies indicate that this has become a significant public health problem in India.¹ Several risk scores for ACS have been published in European and American guidelines. However, in the Indian population, minimal data are available on utilization of such a triage score (HEART score) in chest pain patients in the ED in a resource-limited setting, to the best of our knowledge. In India, only 1 such study is reported,¹⁵ at the Sundaram Medical Foundation, a 170-bed community hospital in Chennai. In this study, 13 of 14 patients (92.86%) with a high HEART score had MACE, indicating a sensitivity of 92.86%; in the 44 patients with a low HEART score, 1 patient (2.22%) had MACE, indicating a specificity of 97.78%; and in the 28 patients with a moderate HEART score, 12 patients (42.86%) had MACE.  

In looking for the optimal risk-stratifying system for chest pain patients, we analyzed the HEART score. The first study on the HEART score was done Backus et al, proving that the HEART score is an easy, quick, and reliable predictor of outcomes in chest pain patients.¹⁰ The HEART score had good discriminatory power, too. The C statistic for the HEART score for ACS occurrence shows a value of 0.83. This signifies a good-to-excellent ability to stratify all-cause chest pain patients in the ED for their risk of MACE. The application of the HEART score to our patient population demonstrated that the majority of the patients belonged to the low-risk category, as reported in the first cohort study that applied the HEART score.⁸ The relationship between the HEART score category and occurrence of MACE within 6 weeks showed a curve with 3 different patterns, corresponding to the 3 risk categories defined in the literature.^11,12 The risk stratification of chest pain patients using the 3 categories (0-3, 4-6, 7-10) identified MACE with an incidence similar to the multicenter study of Backus et al,^10,11 but with a greater risk of MACE in the high-risk category (Figure).

Thus, our study confirmed the utility of the HEART score categories to predict the 6-week incidence of MACE. The sensitivity, specificity, and positive and negative predictive values for the established cut-off scores of 4 and 7 are shown in Table 8. The patients in the low-risk category, corresponding to a score < 4, had a very high negative predictive value, thus identifying a small-risk population. The patients in the high-risk category (score ≥ 7) showed a high positive predictive value, allowing the identification of a high-risk population, even in patients with more atypical presentations. Therefore, the HEART score may help clinicians to make accurate management choices by being a strong predictor of both event-free survival and potentially life-threatening cardiac events.^11,12

Our study tested the efficacy of the HEART score pathway in helping clinicians make smart diagnostic and therapeutic choices. It confirmed that the HEART score was accurate in predicting the short-term incidence of MACE, thus stratifying patients according to their risk severity. In our study, 67 of 141 patients (47.52%) had low-risk HEART scores, and we found the 6-week incidence of MACE to be 1.49%. We omitted the diagnostic and treatment evaluation for patients in the low-risk category and moved onto discharge. Overall, 66 of 67 patients (98.51%) in the low-risk category had an uneventful recovery following discharge. Only 2 of 67 these patients (2.99%) of patients had health care utilization following discharge. Therefore, extrapolation based on results demonstrates reduced health care utilization. Previous studies have shown similar results.^9,12,14,16 For instance, in a prospective study conducted in the Netherlands, low-risk patients representing 36.4% of the total were found to have a low MACE rate (1.7%).⁹ These low-risk patients were categorized as appropriate and safe for ED discharge without additional cardiac evaluation or inpatient admission.⁹ Another retrospective study in Portugal,¹² and one in Chennai, India,¹⁵ found the 6-week incidence of MACE to be 2.00% and 2.22%, respectively. The results of the first HEART Pathway Randomized Control Trial¹⁴ showed that the HEART score pathway reduces health care utilization (cardiac testing, hospitalization, and hospital length of stay). The study also showed that these gains occurred without any of the patients that were identified for early discharge, suffering from MACE at 30 days, or secondary increase in cardiac-related hospitalizations. Similar results were obtained by a randomized trial conducted in North Carolina¹⁷ that also demonstrated a reduction in objective cardiac testing, a doubling of the rate of early discharge from the ED, and a reduced length of stay by half a day. Another study using a modified HEART score also demonstrated that when low-risk patients are evaluated with cardiac testing, the likelihood for false positives is high.¹⁶ Hoffman et al also reported that patients randomized to coronary computed tomographic angiography (CCTA) received > 2.5 times more radiation exposure.¹⁶ Thus, low-risk patients may be safely discharged without the need for stress testing or CCTA.

In our study, 30 out of 141 patients (21.28%) had high-risk HEART scores (7-10), and we found the 6-week incidence of MACE to be 90%. Based on the pathway leading to inpatient admission and intensive treatment, 23 of 30 patients (76.67%) patients in our study underwent coronary angiography and further therapeutic treatment. In the high-risk category, 28 of 30 patients (93.33%) patients had an uneventful recovery following discharge. Previous studies have shown similar results. A retrospective study in Portugal showed that 76.9% of the high-risk patients had a 6-week incidence of MACE.¹² In a study in the Netherlands,⁹ 72.7% of high-risk patients had a 6-week incidence of MACE. Therefore, a HEART score of ≥ 7 in patients implies early aggressive treatment, including invasive strategies, when necessary, without noninvasive treatment preceding it.⁸

In terms of intermediate risk, in our study 44 of 141 patients (31.21%) patients had an intermediate-risk HEART score (4-6), and we found the 6-week incidence of MACE to be 18.18%. Based on the pathway, they were kept in the observation ward on admission. In our study, 7 of 44 patients (15.91%) underwent coronary angiography and further treatment; 42 of 44 patients (95.55%) had an uneventful recovery following discharge. In a prospective study in the Netherlands, 46.1% of patients with an intermediate score had a 6-week MACE incidence of 16.6%.¹⁰ Similarly, in another retrospective study in Portugal, the incidence of 6-week MACE in intermediate-risk patients (36.7%) was found to be 15.6%.¹² Therefore, in patients with a HEART score of 4-6 points, immediate discharge is not an option, as this figure indicates a risk of 18.18% for an adverse outcome. These patients should be admitted for clinical observation, treated as an ACS awaiting final diagnosis, and subjected to noninvasive investigations, such as repeated troponin. Using the HEART score as guidance in the treatment of chest pain patients will benefit patients on both sides of the spectrum.^11,12

Our sample presented a male predominance, a wide range of age, and a mean age similar to that of previous studies.^12.16 Some risk factors, we found, can increase significantly the odds of chest pain being of cardiovascular origin, such as male gender, smoking, hypertension, type 2 diabetes mellitus, and hypercholesterolemia. Other studies also reported similar findings.^8,12,16 Risk factors for premature CHD have been quantified in the case-control INTERHEART study.¹ In the INTERHEART study, 8 common risk factors explained > 90% of AMIs in South Asian and Indian patients. The risk factors include dyslipidemia, smoking or tobacco use, known hypertension, known diabetes, abdominal obesity, physical inactivity, low fruit and vegetable intake, and psychosocial stress.¹ Regarding the feasibility of treating physicians using the HEART score in the ED, we observed that, based on the Likert scale, 80% of survey respondents found it easy to use, and 100% found it feasible in the ED.

However, there were certain limitations to our study. It involved a single academic medical center and a small sample size, which limit generalizability of the findings. In addition, troponin levels are not calculated at our institution, as it is a resource-limited setting; therefore, we used a positive and negative as +2 and 0, respectively.

Conclusion

The HEART score provides the clinician with a quick and reliable predictor of outcome of patients with chest pain after arrival to the ED and can be used for triage. For patients with low HEART scores (0-3), short-term MACE can be excluded with greater than 98% certainty. In these patients, one may consider reserved treatment and discharge policies that may also reduce health care utilization. In patients with high HEART scores (7-10), the high risk of MACE (90%) may indicate early aggressive treatment, including invasive strategies, when necessary. Therefore, the HEART score may help clinicians make accurate management choices by being a strong predictor of both event-free survival and potentially life-threatening cardiac events. Age, gender, and cardiovascular risk factors may also be considered in the assessment of patients. This study confirmed the utility of the HEART score categories to predict the 6-week incidence of MACE.

Corresponding author: Smrati Bajpai Tiwari, MD, DNB, FAIMER, Department of Medicine, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Acharya Donde Marg, Parel, Mumbai 400 012, Maharashtra, India; [email protected].

Financial disclosures: None.

I have been in the business of medicine for more than 15 years and I will never forget the initial surge of the COVID-19 pandemic in Massachusetts.

As a hospitalist, I admitted patients infected with COVID-19, followed them on the floor, and, since I had some experience working in an intensive care unit (ICU), was assigned to cover a “COVID ICU.” This wing of the hospital used to be a fancy orthopedic floor that our institution was lucky enough to have. So began the most life-changing experience in my career as a physician.

In this role, we witness death more than any of us would care to discuss. It comes with the territory, and we never expected this to change once COVID hit. However, so many patients succumbed to this disease, especially during the first surge, which made it difficult to handle emotionally. Patients that fell ill initially stayed isolated at home, optimistic they would turn the corner only to enter the hospital a week later after their conditioned worsened. After requiring a couple of liters of supplemental oxygen in the emergency room, they eventually ended up on a high flow nasal cannula in just a matter of hours.

Patients slowly got sicker and felt more helpless as the days passed, leading us to prescribe drugs that eventually proved to have no benefit. We checked countless inflammatory markers, most of which we were not even sure what to do with. Many times, we hosted a family meeting via FaceTime, holding a patient’s hand in one hand and an iPad in the other to discuss goals of care. Too often, a dark cloud hung over these discussions, a realization that there was not much else we could do.

I have always felt that helping someone have a decent and peaceful death is important, especially when the prognosis is grim, and that patient is suffering. But the sheer number of times this happened during the initial surge of the pandemic was difficult to handle. It felt like I had more of those discussions in 3 months than I did during my entire career as a hospitalist.

We helped plenty of people get better, with some heading home in a week. They thanked us, painted rocks and the sidewalks in front of the hospital displaying messages of gratitude, and sent lunches. Others, though, left the hospital 2 months later with a tube in their stomach so they could receive some form of nutrition and another in their neck to help them breathe.

These struggles were by no means special to me; other hospitalists around the world faced similar situations at one point or another during the pandemic. Working overtime, coming home late, exhausted, undressing in the garage, trying to be there for my 3 kids who were full of energy after a whole day of Zoom and doing the usual kid stuff. My house used to have strict rules about screen time. No more.

The summer months provided a bit of a COVID break, with only 1 or 2 infected patients entering my care. We went to outdoor restaurants and tried to get our lives back to “normal.” As the weather turned cold, however, things went south again. This time no more hydroxychloroquine, a drug used to fight malaria but also treat other autoimmune diseases, as it was proven eventually over many studies that it is not helpful and was potentially harmful. We instead shifted our focus to remdesivir—an antiviral drug that displayed some benefits—tocilizumab, and dexamethasone, anti-inflammatory drugs with the latter providing some positive outcomes on mortality.

Patient survival rates improved slightly, likely due to a combination of factors. We were more experienced at fighting the disease, which led to things in the hospital not being as chaotic and more time available to spend with the patients. Personal protective equipment (PPE) and tests were more readily available, and the population getting hit by the disease changed slightly with fewer elderly people from nursing homes falling ill because of social distancing, other safety measures, or having already fought the disease. Our attention turned instead to more young people that had returned to work and their social lives.

The arrival of the vaccines brought considerable relief. I remember a few decades ago debating and sometimes fighting with friends and family over who was better: Iron Man or Spider-Man. Now I found myself having the same conversation about the Pfizer and Moderna COVID vaccines.

Summer 2021 holds significantly more promise. Most of the adult population is getting vaccinated, and I am very hopeful that we are approaching the end of this nightmare. In June, our office received word that we could remove our masks if we were fully vaccinated. It felt weird, but represented another sign that things are improving. I took my kids to the mall and removed my mask. It felt odd considering how that little blue thing became part of me during the pandemic. It also felt strange to not prescribe a single dose of remdesivir for an entire month.

It feels good—and normal—to care for the patients that we neglected for a year. It has been a needed boost to see patients return to their health care providers for their colonoscopy screenings, mammograms, and managing chronic problems like coronary artery disease, congestive heart failure, or receiving chemotherapy.

I learned plenty from this pandemic and hope I am not alone. I learned to be humble. We started with a drug that was harmful, moved on to a drug that is probably neutral and eventually were able to come up with a drug that seems to decrease mortality at least in some COVID patients. I learned it is fine to try new therapies based on the best data in the hope they result in positive clinical outcomes. However, it is critical that we all keep an eye on the rapidly evolving literature and adjust our behavior accordingly.

I also learned, or relearned, that if people are desperate enough, they will drink bleach to see if it works. Others are convinced that the purpose of vaccination is to inject a microchip allowing ourselves to be tracked by some higher power. I learned that we must take the first step to prepare for the next pandemic by having a decent reserve of PPE.

It is clear synthetic messenger RNA (mRNA) technology is here to stay, and I believe it has a huge potential to change many areas of medicine. mRNA vaccines proved to be much faster to develop and probably much easier to change as the pathogen, in this case coronavirus, changes.

The technology could be used against a variety of infectious diseases to make vaccines against malaria, tuberculosis, HIV, or hepatitis. It can also be very useful for faster vaccine development needed in future possible pandemics such as influenza, Ebola, or severe acute respiratory syndrome. It may also be used for cancer treatment.

As John P. Cooke, MD, PhD, the medical director for the Center of RNA Therapeutics Program at the Houston Methodist Research Institute, said, “Most vaccines today are still viral vaccines – they are inactivated virus, so it’s potentially infectious and you have to have virus on hand. With mRNA, you’re just writing code which is going to tell the cell to make a viral protein – one part of a viral protein to stimulate an immune response. And, here’s the wonderful thing, you don’t even need the virus in hand, just its DNA code.”¹

Corresponding author: Dragos Vesbianu, MD, Attending Hospitalist, Newton-Wellesley Hospital, 2014 Washington St, Newton, MA 02462; [email protected].

Financial dislosures: None.

I have been in the business of medicine for more than 15 years and I will never forget the initial surge of the COVID-19 pandemic in Massachusetts.

As a hospitalist, I admitted patients infected with COVID-19, followed them on the floor, and, since I had some experience working in an intensive care unit (ICU), was assigned to cover a “COVID ICU.” This wing of the hospital used to be a fancy orthopedic floor that our institution was lucky enough to have. So began the most life-changing experience in my career as a physician.

In this role, we witness death more than any of us would care to discuss. It comes with the territory, and we never expected this to change once COVID hit. However, so many patients succumbed to this disease, especially during the first surge, which made it difficult to handle emotionally. Patients that fell ill initially stayed isolated at home, optimistic they would turn the corner only to enter the hospital a week later after their conditioned worsened. After requiring a couple of liters of supplemental oxygen in the emergency room, they eventually ended up on a high flow nasal cannula in just a matter of hours.

Patients slowly got sicker and felt more helpless as the days passed, leading us to prescribe drugs that eventually proved to have no benefit. We checked countless inflammatory markers, most of which we were not even sure what to do with. Many times, we hosted a family meeting via FaceTime, holding a patient’s hand in one hand and an iPad in the other to discuss goals of care. Too often, a dark cloud hung over these discussions, a realization that there was not much else we could do.

I have always felt that helping someone have a decent and peaceful death is important, especially when the prognosis is grim, and that patient is suffering. But the sheer number of times this happened during the initial surge of the pandemic was difficult to handle. It felt like I had more of those discussions in 3 months than I did during my entire career as a hospitalist.

We helped plenty of people get better, with some heading home in a week. They thanked us, painted rocks and the sidewalks in front of the hospital displaying messages of gratitude, and sent lunches. Others, though, left the hospital 2 months later with a tube in their stomach so they could receive some form of nutrition and another in their neck to help them breathe.

These struggles were by no means special to me; other hospitalists around the world faced similar situations at one point or another during the pandemic. Working overtime, coming home late, exhausted, undressing in the garage, trying to be there for my 3 kids who were full of energy after a whole day of Zoom and doing the usual kid stuff. My house used to have strict rules about screen time. No more.

The summer months provided a bit of a COVID break, with only 1 or 2 infected patients entering my care. We went to outdoor restaurants and tried to get our lives back to “normal.” As the weather turned cold, however, things went south again. This time no more hydroxychloroquine, a drug used to fight malaria but also treat other autoimmune diseases, as it was proven eventually over many studies that it is not helpful and was potentially harmful. We instead shifted our focus to remdesivir—an antiviral drug that displayed some benefits—tocilizumab, and dexamethasone, anti-inflammatory drugs with the latter providing some positive outcomes on mortality.

Patient survival rates improved slightly, likely due to a combination of factors. We were more experienced at fighting the disease, which led to things in the hospital not being as chaotic and more time available to spend with the patients. Personal protective equipment (PPE) and tests were more readily available, and the population getting hit by the disease changed slightly with fewer elderly people from nursing homes falling ill because of social distancing, other safety measures, or having already fought the disease. Our attention turned instead to more young people that had returned to work and their social lives.

The arrival of the vaccines brought considerable relief. I remember a few decades ago debating and sometimes fighting with friends and family over who was better: Iron Man or Spider-Man. Now I found myself having the same conversation about the Pfizer and Moderna COVID vaccines.

Summer 2021 holds significantly more promise. Most of the adult population is getting vaccinated, and I am very hopeful that we are approaching the end of this nightmare. In June, our office received word that we could remove our masks if we were fully vaccinated. It felt weird, but represented another sign that things are improving. I took my kids to the mall and removed my mask. It felt odd considering how that little blue thing became part of me during the pandemic. It also felt strange to not prescribe a single dose of remdesivir for an entire month.

It feels good—and normal—to care for the patients that we neglected for a year. It has been a needed boost to see patients return to their health care providers for their colonoscopy screenings, mammograms, and managing chronic problems like coronary artery disease, congestive heart failure, or receiving chemotherapy.

I learned plenty from this pandemic and hope I am not alone. I learned to be humble. We started with a drug that was harmful, moved on to a drug that is probably neutral and eventually were able to come up with a drug that seems to decrease mortality at least in some COVID patients. I learned it is fine to try new therapies based on the best data in the hope they result in positive clinical outcomes. However, it is critical that we all keep an eye on the rapidly evolving literature and adjust our behavior accordingly.

I also learned, or relearned, that if people are desperate enough, they will drink bleach to see if it works. Others are convinced that the purpose of vaccination is to inject a microchip allowing ourselves to be tracked by some higher power. I learned that we must take the first step to prepare for the next pandemic by having a decent reserve of PPE.

It is clear synthetic messenger RNA (mRNA) technology is here to stay, and I believe it has a huge potential to change many areas of medicine. mRNA vaccines proved to be much faster to develop and probably much easier to change as the pathogen, in this case coronavirus, changes.

The technology could be used against a variety of infectious diseases to make vaccines against malaria, tuberculosis, HIV, or hepatitis. It can also be very useful for faster vaccine development needed in future possible pandemics such as influenza, Ebola, or severe acute respiratory syndrome. It may also be used for cancer treatment.

As John P. Cooke, MD, PhD, the medical director for the Center of RNA Therapeutics Program at the Houston Methodist Research Institute, said, “Most vaccines today are still viral vaccines – they are inactivated virus, so it’s potentially infectious and you have to have virus on hand. With mRNA, you’re just writing code which is going to tell the cell to make a viral protein – one part of a viral protein to stimulate an immune response. And, here’s the wonderful thing, you don’t even need the virus in hand, just its DNA code.”¹

Corresponding author: Dragos Vesbianu, MD, Attending Hospitalist, Newton-Wellesley Hospital, 2014 Washington St, Newton, MA 02462; [email protected].

Financial dislosures: None.

I have been in the business of medicine for more than 15 years and I will never forget the initial surge of the COVID-19 pandemic in Massachusetts.

As a hospitalist, I admitted patients infected with COVID-19, followed them on the floor, and, since I had some experience working in an intensive care unit (ICU), was assigned to cover a “COVID ICU.” This wing of the hospital used to be a fancy orthopedic floor that our institution was lucky enough to have. So began the most life-changing experience in my career as a physician.

In this role, we witness death more than any of us would care to discuss. It comes with the territory, and we never expected this to change once COVID hit. However, so many patients succumbed to this disease, especially during the first surge, which made it difficult to handle emotionally. Patients that fell ill initially stayed isolated at home, optimistic they would turn the corner only to enter the hospital a week later after their conditioned worsened. After requiring a couple of liters of supplemental oxygen in the emergency room, they eventually ended up on a high flow nasal cannula in just a matter of hours.

Patients slowly got sicker and felt more helpless as the days passed, leading us to prescribe drugs that eventually proved to have no benefit. We checked countless inflammatory markers, most of which we were not even sure what to do with. Many times, we hosted a family meeting via FaceTime, holding a patient’s hand in one hand and an iPad in the other to discuss goals of care. Too often, a dark cloud hung over these discussions, a realization that there was not much else we could do.

I have always felt that helping someone have a decent and peaceful death is important, especially when the prognosis is grim, and that patient is suffering. But the sheer number of times this happened during the initial surge of the pandemic was difficult to handle. It felt like I had more of those discussions in 3 months than I did during my entire career as a hospitalist.

We helped plenty of people get better, with some heading home in a week. They thanked us, painted rocks and the sidewalks in front of the hospital displaying messages of gratitude, and sent lunches. Others, though, left the hospital 2 months later with a tube in their stomach so they could receive some form of nutrition and another in their neck to help them breathe.

These struggles were by no means special to me; other hospitalists around the world faced similar situations at one point or another during the pandemic. Working overtime, coming home late, exhausted, undressing in the garage, trying to be there for my 3 kids who were full of energy after a whole day of Zoom and doing the usual kid stuff. My house used to have strict rules about screen time. No more.

The summer months provided a bit of a COVID break, with only 1 or 2 infected patients entering my care. We went to outdoor restaurants and tried to get our lives back to “normal.” As the weather turned cold, however, things went south again. This time no more hydroxychloroquine, a drug used to fight malaria but also treat other autoimmune diseases, as it was proven eventually over many studies that it is not helpful and was potentially harmful. We instead shifted our focus to remdesivir—an antiviral drug that displayed some benefits—tocilizumab, and dexamethasone, anti-inflammatory drugs with the latter providing some positive outcomes on mortality.

Patient survival rates improved slightly, likely due to a combination of factors. We were more experienced at fighting the disease, which led to things in the hospital not being as chaotic and more time available to spend with the patients. Personal protective equipment (PPE) and tests were more readily available, and the population getting hit by the disease changed slightly with fewer elderly people from nursing homes falling ill because of social distancing, other safety measures, or having already fought the disease. Our attention turned instead to more young people that had returned to work and their social lives.

The arrival of the vaccines brought considerable relief. I remember a few decades ago debating and sometimes fighting with friends and family over who was better: Iron Man or Spider-Man. Now I found myself having the same conversation about the Pfizer and Moderna COVID vaccines.

Summer 2021 holds significantly more promise. Most of the adult population is getting vaccinated, and I am very hopeful that we are approaching the end of this nightmare. In June, our office received word that we could remove our masks if we were fully vaccinated. It felt weird, but represented another sign that things are improving. I took my kids to the mall and removed my mask. It felt odd considering how that little blue thing became part of me during the pandemic. It also felt strange to not prescribe a single dose of remdesivir for an entire month.

It feels good—and normal—to care for the patients that we neglected for a year. It has been a needed boost to see patients return to their health care providers for their colonoscopy screenings, mammograms, and managing chronic problems like coronary artery disease, congestive heart failure, or receiving chemotherapy.

I learned plenty from this pandemic and hope I am not alone. I learned to be humble. We started with a drug that was harmful, moved on to a drug that is probably neutral and eventually were able to come up with a drug that seems to decrease mortality at least in some COVID patients. I learned it is fine to try new therapies based on the best data in the hope they result in positive clinical outcomes. However, it is critical that we all keep an eye on the rapidly evolving literature and adjust our behavior accordingly.

I also learned, or relearned, that if people are desperate enough, they will drink bleach to see if it works. Others are convinced that the purpose of vaccination is to inject a microchip allowing ourselves to be tracked by some higher power. I learned that we must take the first step to prepare for the next pandemic by having a decent reserve of PPE.

It is clear synthetic messenger RNA (mRNA) technology is here to stay, and I believe it has a huge potential to change many areas of medicine. mRNA vaccines proved to be much faster to develop and probably much easier to change as the pathogen, in this case coronavirus, changes.

The technology could be used against a variety of infectious diseases to make vaccines against malaria, tuberculosis, HIV, or hepatitis. It can also be very useful for faster vaccine development needed in future possible pandemics such as influenza, Ebola, or severe acute respiratory syndrome. It may also be used for cancer treatment.

As John P. Cooke, MD, PhD, the medical director for the Center of RNA Therapeutics Program at the Houston Methodist Research Institute, said, “Most vaccines today are still viral vaccines – they are inactivated virus, so it’s potentially infectious and you have to have virus on hand. With mRNA, you’re just writing code which is going to tell the cell to make a viral protein – one part of a viral protein to stimulate an immune response. And, here’s the wonderful thing, you don’t even need the virus in hand, just its DNA code.”¹

Corresponding author: Dragos Vesbianu, MD, Attending Hospitalist, Newton-Wellesley Hospital, 2014 Washington St, Newton, MA 02462; [email protected].

Financial dislosures: None.