One of the most striking features of the early COVID-19 pandemic was the sudden and sharp reductions in emergency department (ED) visits and hospitalizations throughout the United States.^1-4 Several studies have documented lower rates of hospitalization for many emergent, time-sensitive conditions, such as acute myocardial infarction, stroke, and hyperglycemic crises, starting shortly after community transmission of COVID-19 was recognized and social distancing guidelines were implemented.^5-8 In most cases, hospital volumes rebounded after an initial drop, stabilizing at somewhat lower levels than those expected from historic trends.⁹

The observed shifts in hospital use largely have been attributed to patients’ forgoing or delaying necessary care,¹⁰ which underscores the indirect effects of the pandemic on patients without COVID-19.¹¹ To date, the extent to which outcomes for patients without COVID-19 have been adversely affected is less well understood. Evidence suggests patients with acute and chronic illnesses have experienced increased morbidity and mortality since the onset of the pandemic. For example, in northern California, abrupt declines in ED visits for cardiac symptoms were coupled with higher rates of out-of-hospital cardiac arrest.¹² Moreover, states with higher rates of COVID-19 also reported increased deaths attributed to heart disease, diabetes, and other conditions.¹³

To better understand these potential indirect effects, this study used data from a large, multistate health care system to examine changes in hospital volume and its relationship to in-hospital mortality for patients without COVID-19 during the first 10 months of the pandemic.

Setting and Participants

We examined unplanned hospitalizations from January 2019 to December 2020 at 51 community hospitals across 6 states (Alaska, Washington, Montana, Oregon, California, and Texas) in the Providence St. Joseph Health system. Hospitals within the Providence system share a common standard dataset for each encounter with a centralized cloud data warehouse from which we extracted clinical and demographic data. No hospitals entered or left the system during the study period. Hospitalizations were considered unplanned if they had an “urgent” or “emergency” service type in the record; most originated in the ED. Hospitalizations for children younger than 18 years and those with evidence of COVID-19 (International Classification of Disease, Tenth Revision, Clinical Modification U07.1, a positive COVID-19 polymerase chain reaction test during the encounter, or an infection control-assigned label of COVID-19) were excluded. The Providence St. Joseph Health Institutional Review Board approved this study.

Measures

Trends in daily hospitalizations and their relationship to adjusted in-hospital mortality (percentage of patients who died during their hospital admission) were examined over time. In preliminary models using segmented regression, we identified three distinct pandemic periods with different trends in daily hospitalizations: (1) a 10-week period corresponding to the spring COVID-19 surge (March 4 to May 13, 2020; Period 1), (2) an intervening period extending over the summer and early fall (May 14 to October 19, 2020; Period 2), and (3) a second 10-week period corresponding to the fall COVID-19 surge (October 20 to December 31, 2020; Period 3). In-hospital mortality for these periods was compared with a baseline period (pre-COVID-19) from January 1, 2019 to March 3, 2020. To further assess differences in mortality by clinical condition, hospitalizations were first grouped by primary diagnosis using Clinical Classifications Software Refined (CCSR) categories from the Agency for Healthcare Research and Quality¹⁴ and ranked by the number of observed deaths and the percentage of patients who died while hospitalized in 2020. We selected common conditions that had >35 total deaths and an in-hospital mortality rate ≥1% for condition-specific analyses, of which 30 met these criteria.

Analysis

Multivariate logistic regression was used to evaluate changes in mortality for each of the pandemic periods compared with baseline for the overall cohort and selected diagnosis groups. Our main model adjusted for age, sex, race/ethnicity (White, Black, Latinx, Asian or Pacific Islander, and other), primary payor (commercial, Medicaid, Medicare, other, and self-pay), the presence or absence of 31 chronic comorbidities in the medical record, primary admitting diagnosis grouped by CCSR category (456 total diagnostic groups), and hospital fixed-effects to account for clustering. Results are expressed as the average marginal effects of each pandemic period on in-hospital mortality (eg, adjusted percentage point change in mortality over baseline). The number of excess deaths in each period was calculated by multiplying the estimated percentage point change in mortality for each period by the total number of hospitalizations. These excess deaths were subtracted from the number of observed deaths to derive the number of deaths that would be expected if pre-pandemic mortality rates persisted.

To further assess whether changes in adjusted mortality could be attributed to a smaller, sicker population of patients presenting to the hospital during the pandemic (meaning that less acutely ill patients stayed home), we conducted two sensitivity analyses. First, we tested whether substituting indicators for Medicare Severity Diagnosis Groups (MS-DRG) in lieu of CCSR categories had any impact on our results. MS-DRGs are designed to account for a patient’s illness severity and expected costs, whereas CCSR categories do not.¹⁵ MS-DRGs also better distinguish between surgical versus medical conditions. We re-ran our main model using indicators for CCSR to control for diagnostic mix, but further adjusted for severity using the DRG weight for the primary diagnosis and Modified Early Warning Score (MEWS) as continuous covariates. MEWS is a physiologic scoring system that incorporates abnormal vital signs and data related to mental status during the first 24 hours of a patient’s hospitalization into a risk-based score that has been shown to predict hospital mortality and need for intensive care.^16,17 These sensitivity analyses were performed on a subset of inpatient admissions because DRG data are not available for hospitalizations billed as an observation stay, and only approximately 70% of hospitals in the sample contributed vital sign data to the Providence data warehouse. All statistical analyses were conducted with R, version 3.6.3 (R Foundation for Statistical Computing) and SAS Enterprise Guide 7.1 (SAS Institute Inc).

The characteristics of our sample are described in Table 1. A total of 61,300, 159,430, and 65,923 hospitalizations occurred in each of the three pandemic periods, respectively, compared with 503,190 hospitalizations in the pre-pandemic period. The mean (SD) age of patients in the study was 63.2 (19.4) years; most were women (52.4%), White (70.6%), and had Medicare as their primary payor (53.7%). Less than half (42.7%) of hospitalizations occurred in California, and just under one-quarter were observation stays (23.2%). Patient characteristics were similar in the pre-COVID-19 and COVID-19 pandemic periods.

Figure 1 shows trends in hospital volume and mortality. Overall daily hospitalizations declined abruptly from a mean of 1176 per day in the pre-pandemic period to 617 per day (47.5% relative decrease) during the first 3 weeks of Period 1. Mean daily hospitalizations began to rise over the next 2 months (Period 1), reaching steady state at <1000 hospitalizations per day (15% relative decrease from baseline) during Period 2. During Period 3, we observed a decline in mean daily hospitalizations, with a low point of 882 per day on December 31, 2020 (25% relative decrease from baseline), corresponding to the end of our study period. Although hospital volumes declined during both COVID-19 surge periods, the percentage of patients who died during their hospitalization increased. There was an initial spike in in-hospital mortality that peaked approximately 1 month into the pandemic (middle of Period 1), a return to levels at or slightly below that before the pandemic by the beginning of Period 2, and then a rise throughout the autumn COVID-19 surge in Period 3, not yet peaking by the end of the study.

Adjusted in-hospital mortality for the three COVID-19 periods compared with the pre-pandemic period is presented in Table 2. The percentage of patients who died during their hospitalization rose from 2.9% in the pre-pandemic period to 3.4% during Period 1 (absolute difference, 0.6 percentage points; 95% CI, 0.5-0.7), corresponding to a 19.3% relative increase during the spring COVID-19 surge. Among the subset of patients hospitalized with 1 of the 30 conditions selected for individual analysis, mortality increased from 5.0% to 5.9% during the same time period (absolute difference, 0.9 percentage points; 95% CI, 0.8-1.1), corresponding to an 18.9% relative increase. In Period 2, in-hospital mortality was similar to that noted pre-pandemic for the overall cohort and the 30 selected conditions. During Period 3, in-hospital mortality increased by a magnitude similar to that observed in Period 1 for all hospitalizations combined (absolute difference, 0.5 percentage points; 95% CI, 0.0-0.6; corresponding to a 16.5% relative increase) as well as the subgroup with 1 of the 30 selected conditions (0.9 percentage points; 95% CI, 0.8-1.0; corresponding to an 18% relative increase). Further adjustment for severity by swapping CCSR categories with MS-DRG indicators or inclusion of DRG weight and MEWS score as covariates in our sensitivity analyses did not change our results.

Table 3 and the Appendix Figure describe changes in volume and adjusted in-hospital mortality for the 30 conditions selected for analysis. There was a decrease in the mean daily admissions for all conditions studied. Among the 30 conditions, 26 showed increased mortality during Period 1, although the increase was only statistically significant for 16 of these conditions. Among the 10 most commonly admitted conditions (by number of daily hospital admissions during the baseline period), there was a statistically significant relative increase in mortality for patients with sepsis (20.1%), heart failure (17.6%), ischemic stroke (12.5%), device/graft/surgical complications (14.0%), cardiac dysrhythmias (14.4%), pneumonia (24.5%), respiratory failure (16.1%), and gastrointestinal hemorrhage (23.3%). In general, mortality returned to baseline or improved during Period 2. Thereafter, all 30 conditions showed increased mortality in Period 3. This increase was significant for only 16 conditions, which were not the same ones noted during Period 1. Of note, although there was higher mortality for some cardiovascular conditions (heart failure cardiac dysrhythmias), mortality for myocardial infarction remained unchanged from baseline across all 3 periods. In contrast, several solid cancer–related conditions showed progressively worsening mortality throughout the study, with 7.7% higher mortality in Period 1, 10.3% higher mortality in Period 2, and 16.5% higher mortality in Period 3, respectively, compared with baseline. Although a similar pattern was observed for acute renal failure and some neurologic conditions (traumatic brain injury, seizure, other nervous system disorders), mortality for drug poisonings and gastrointestinal bleeds improved over time.

In this study of unplanned hospitalizations from 51 community hospitals across 6 states in the US West, we found a significant increase in mortality—at a rate of approximately 5 to 6 excess deaths per 1000 hospitalizations—among patients admitted during the pandemic with a variety of non-COVID-19 illnesses and injuries. Higher in-hospital mortality was observed in the spring (March to May) and fall (October to December) of 2020 when COVID-19 case counts surged and shelter-in-place mandates were implemented. With the initial surge, higher mortality rates were largely transient, and, for most conditions evaluated, returned to baseline approximately 3 months after the pandemic onset. For the fall surge, mortality rates had not peaked by the end of the study period. Changes in mortality were closely and inversely correlated with hospital volume for non-COVID-19 illnesses during both surge periods.

Higher morbidity and mortality for patients without COVID-19 appears to be an unfortunate spillover effect that has been reported in several studies. Recent work examining national surveillance data suggest that up to one-third of excess deaths (deaths higher than those expected for season) early in the pandemic have occurred among patients without known COVID-19.^13,18-20 Specifically, these studies estimate that mortality rates in the United States increased by 15% to 19% in the spring of 2020; of the identified excess deaths, only 38% to 77% could be attributed to COVID-19, with the remainder attributed to cardiovascular disease, diabetes, and Alzheimer’s disease, among others. In addition, reports from several European countries and China examining population death data have found similar trends,^21-25 as well as a recent study examining excess deaths in nursing homes.²⁶ Our results are largely consistent with these earlier studies in that we describe higher mortality in a sample of patients hospitalized with a variety of common conditions that otherwise are routinely treated in US hospitals. Reporting these indirect casualties of COVID-19 is important to fully understand the pandemic’s toll on patients and healthcare systems.

Our work builds on the current body of literature, highlighting the consistent relationship between rising COVID-19 case counts, hospital volume, and excess mortality over more than one surge period. Although several studies have looked at trends in hospital admissions or population mortality rates, few have examined the two outcomes together. The close correlation between daily hospital admissions and in-hospital mortality in this study suggests that the pandemic changed how patients use healthcare resources in ways that were important for their health and outcomes. The higher mortality rate that we and others have observed likely is related to patients’ delaying care because of fear of contracting COVID-19. In one survey, more than 4 in 10 adults in the United States reported that they avoided medical care during the early pandemic.¹⁰ Importantly, even a few days delay for many conditions, such as heart failure or sepsis, can result in precipitous declines in clinical status and outcomes.

It also is possible that we found increased rates of in-hospital mortality simply because patients with more moderate illness chose to stay home, resulting in a patient population enriched with those more likely to die. We found mixed evidence in our data that the observed increases in mortality could be attributable to a smaller, sicker population. Some characteristics that might be protective, such as a slightly younger mean age and lower mean DRG weight, were more common among those hospitalized during the pandemic. However, other characteristics, such as a slightly higher MEWS score and a greater percentage of total hospitalizations in the higher mortality subgroup, also were noted during the pandemic (Table 1). We do note, however, that the differences in these severity-related characteristics were small across the study periods. Further adjusting for these characteristics in our sensitivity analyses did not appreciably change our main findings, suggesting that the mortality increase could not be explained by changes in case-mix alone.

Other factors not dependent on patient behavior, such as barriers to accessing timely ambulatory care and impacts in the quality of care delivered, might have contributed. Shelter-in-place orders, reduced in-person access to clinicians in the ambulatory setting, slow implementation of telehealth services (with uncertainty about their equivalence to in-person exams), as well as delays in diagnostic tests and outpatient procedures could have played a role, especially during early months of the pandemic.²⁷ Significant changes to ambulatory health care delivery might have left many patients with chronic illnesses or complex medical needs with limited care options. Importantly, these care interruptions might have had greater implications for some patients, such as those with cancer who rely on intensive, largely outpatient-based treatment.^28,29 This, in part, could explain why we found persistently increased mortality among patients hospitalized with cancer after the spring surge. Later into the pandemic, however, most health systems had developed processes that allowed clinicians to resume timely care of ambulatory patients. Because of this, increases in mortality observed during the fall surge likely stem from other factors, such as patient behavior.

It is possible that care delays or changes in the quality of care delivered during the index hospitalization or pre-hospital setting might have contributed to the observed increase in mortality. This is particularly true for acute, time-sensitive conditions such as sepsis and stroke. Extra time spent donning personal protective equipment and/or new protocols instituted during the pandemic likely impacted the speed of emergency medical services transport, timeliness of ED evaluation, and delivery of definitive therapy. Although most hospitals in this study were not overwhelmed by the pandemic, the complexities associated with caring for known and suspected COVID-19 patients alongside those without the disease might have altered ideal care practices and strained healthcare teams.³⁰ In addition, nearly all hospitalized patients during this period were deprived of in-person advocacy by family members, who were not permitted to visit.

Important limitations with this study exist. First, the data come only from hospitals in the western United States. Second, some data elements such as triage scores or vital signs were not available for the entire population, potentially limiting some risk-adjustment. Third, we were unable to determine the root cause of excess mortality based on our study design and the coded variables available. It is unknown to what extent undiagnosed COVID-19 played a role. Early in the pandemic, many community hospitals did not have access to timely COVID-19 testing, and some cases might have not been diagnosed.³¹ However, we do not expect this to be a significant concern in the later months of the pandemic, as testing became more widespread and hospitals implemented surveillance screening for COVID-19 for inpatients.

Our study indicates that the COVID-19 pandemic was associated with increased mortality among patients hospitalized for a range of clinical conditions. Although higher observed mortality rates were limited to periods of high COVID-19 activity, future studies will need to tease out the extent to which these findings relate to patient factors (ie, delayed presentation and more severe disease) or systemic factors (reduction in access or changes in quality of care). This is of key importance, and appropriate solutions will need to be developed to mitigate adverse impacts with this and future pandemics.

One of the most striking features of the early COVID-19 pandemic was the sudden and sharp reductions in emergency department (ED) visits and hospitalizations throughout the United States.^1-4 Several studies have documented lower rates of hospitalization for many emergent, time-sensitive conditions, such as acute myocardial infarction, stroke, and hyperglycemic crises, starting shortly after community transmission of COVID-19 was recognized and social distancing guidelines were implemented.^5-8 In most cases, hospital volumes rebounded after an initial drop, stabilizing at somewhat lower levels than those expected from historic trends.⁹

The observed shifts in hospital use largely have been attributed to patients’ forgoing or delaying necessary care,¹⁰ which underscores the indirect effects of the pandemic on patients without COVID-19.¹¹ To date, the extent to which outcomes for patients without COVID-19 have been adversely affected is less well understood. Evidence suggests patients with acute and chronic illnesses have experienced increased morbidity and mortality since the onset of the pandemic. For example, in northern California, abrupt declines in ED visits for cardiac symptoms were coupled with higher rates of out-of-hospital cardiac arrest.¹² Moreover, states with higher rates of COVID-19 also reported increased deaths attributed to heart disease, diabetes, and other conditions.¹³

To better understand these potential indirect effects, this study used data from a large, multistate health care system to examine changes in hospital volume and its relationship to in-hospital mortality for patients without COVID-19 during the first 10 months of the pandemic.

Setting and Participants

We examined unplanned hospitalizations from January 2019 to December 2020 at 51 community hospitals across 6 states (Alaska, Washington, Montana, Oregon, California, and Texas) in the Providence St. Joseph Health system. Hospitals within the Providence system share a common standard dataset for each encounter with a centralized cloud data warehouse from which we extracted clinical and demographic data. No hospitals entered or left the system during the study period. Hospitalizations were considered unplanned if they had an “urgent” or “emergency” service type in the record; most originated in the ED. Hospitalizations for children younger than 18 years and those with evidence of COVID-19 (International Classification of Disease, Tenth Revision, Clinical Modification U07.1, a positive COVID-19 polymerase chain reaction test during the encounter, or an infection control-assigned label of COVID-19) were excluded. The Providence St. Joseph Health Institutional Review Board approved this study.

Measures

Trends in daily hospitalizations and their relationship to adjusted in-hospital mortality (percentage of patients who died during their hospital admission) were examined over time. In preliminary models using segmented regression, we identified three distinct pandemic periods with different trends in daily hospitalizations: (1) a 10-week period corresponding to the spring COVID-19 surge (March 4 to May 13, 2020; Period 1), (2) an intervening period extending over the summer and early fall (May 14 to October 19, 2020; Period 2), and (3) a second 10-week period corresponding to the fall COVID-19 surge (October 20 to December 31, 2020; Period 3). In-hospital mortality for these periods was compared with a baseline period (pre-COVID-19) from January 1, 2019 to March 3, 2020. To further assess differences in mortality by clinical condition, hospitalizations were first grouped by primary diagnosis using Clinical Classifications Software Refined (CCSR) categories from the Agency for Healthcare Research and Quality¹⁴ and ranked by the number of observed deaths and the percentage of patients who died while hospitalized in 2020. We selected common conditions that had >35 total deaths and an in-hospital mortality rate ≥1% for condition-specific analyses, of which 30 met these criteria.

Analysis

Multivariate logistic regression was used to evaluate changes in mortality for each of the pandemic periods compared with baseline for the overall cohort and selected diagnosis groups. Our main model adjusted for age, sex, race/ethnicity (White, Black, Latinx, Asian or Pacific Islander, and other), primary payor (commercial, Medicaid, Medicare, other, and self-pay), the presence or absence of 31 chronic comorbidities in the medical record, primary admitting diagnosis grouped by CCSR category (456 total diagnostic groups), and hospital fixed-effects to account for clustering. Results are expressed as the average marginal effects of each pandemic period on in-hospital mortality (eg, adjusted percentage point change in mortality over baseline). The number of excess deaths in each period was calculated by multiplying the estimated percentage point change in mortality for each period by the total number of hospitalizations. These excess deaths were subtracted from the number of observed deaths to derive the number of deaths that would be expected if pre-pandemic mortality rates persisted.

To further assess whether changes in adjusted mortality could be attributed to a smaller, sicker population of patients presenting to the hospital during the pandemic (meaning that less acutely ill patients stayed home), we conducted two sensitivity analyses. First, we tested whether substituting indicators for Medicare Severity Diagnosis Groups (MS-DRG) in lieu of CCSR categories had any impact on our results. MS-DRGs are designed to account for a patient’s illness severity and expected costs, whereas CCSR categories do not.¹⁵ MS-DRGs also better distinguish between surgical versus medical conditions. We re-ran our main model using indicators for CCSR to control for diagnostic mix, but further adjusted for severity using the DRG weight for the primary diagnosis and Modified Early Warning Score (MEWS) as continuous covariates. MEWS is a physiologic scoring system that incorporates abnormal vital signs and data related to mental status during the first 24 hours of a patient’s hospitalization into a risk-based score that has been shown to predict hospital mortality and need for intensive care.^16,17 These sensitivity analyses were performed on a subset of inpatient admissions because DRG data are not available for hospitalizations billed as an observation stay, and only approximately 70% of hospitals in the sample contributed vital sign data to the Providence data warehouse. All statistical analyses were conducted with R, version 3.6.3 (R Foundation for Statistical Computing) and SAS Enterprise Guide 7.1 (SAS Institute Inc).

The characteristics of our sample are described in Table 1. A total of 61,300, 159,430, and 65,923 hospitalizations occurred in each of the three pandemic periods, respectively, compared with 503,190 hospitalizations in the pre-pandemic period. The mean (SD) age of patients in the study was 63.2 (19.4) years; most were women (52.4%), White (70.6%), and had Medicare as their primary payor (53.7%). Less than half (42.7%) of hospitalizations occurred in California, and just under one-quarter were observation stays (23.2%). Patient characteristics were similar in the pre-COVID-19 and COVID-19 pandemic periods.

Figure 1 shows trends in hospital volume and mortality. Overall daily hospitalizations declined abruptly from a mean of 1176 per day in the pre-pandemic period to 617 per day (47.5% relative decrease) during the first 3 weeks of Period 1. Mean daily hospitalizations began to rise over the next 2 months (Period 1), reaching steady state at <1000 hospitalizations per day (15% relative decrease from baseline) during Period 2. During Period 3, we observed a decline in mean daily hospitalizations, with a low point of 882 per day on December 31, 2020 (25% relative decrease from baseline), corresponding to the end of our study period. Although hospital volumes declined during both COVID-19 surge periods, the percentage of patients who died during their hospitalization increased. There was an initial spike in in-hospital mortality that peaked approximately 1 month into the pandemic (middle of Period 1), a return to levels at or slightly below that before the pandemic by the beginning of Period 2, and then a rise throughout the autumn COVID-19 surge in Period 3, not yet peaking by the end of the study.

Adjusted in-hospital mortality for the three COVID-19 periods compared with the pre-pandemic period is presented in Table 2. The percentage of patients who died during their hospitalization rose from 2.9% in the pre-pandemic period to 3.4% during Period 1 (absolute difference, 0.6 percentage points; 95% CI, 0.5-0.7), corresponding to a 19.3% relative increase during the spring COVID-19 surge. Among the subset of patients hospitalized with 1 of the 30 conditions selected for individual analysis, mortality increased from 5.0% to 5.9% during the same time period (absolute difference, 0.9 percentage points; 95% CI, 0.8-1.1), corresponding to an 18.9% relative increase. In Period 2, in-hospital mortality was similar to that noted pre-pandemic for the overall cohort and the 30 selected conditions. During Period 3, in-hospital mortality increased by a magnitude similar to that observed in Period 1 for all hospitalizations combined (absolute difference, 0.5 percentage points; 95% CI, 0.0-0.6; corresponding to a 16.5% relative increase) as well as the subgroup with 1 of the 30 selected conditions (0.9 percentage points; 95% CI, 0.8-1.0; corresponding to an 18% relative increase). Further adjustment for severity by swapping CCSR categories with MS-DRG indicators or inclusion of DRG weight and MEWS score as covariates in our sensitivity analyses did not change our results.

Table 3 and the Appendix Figure describe changes in volume and adjusted in-hospital mortality for the 30 conditions selected for analysis. There was a decrease in the mean daily admissions for all conditions studied. Among the 30 conditions, 26 showed increased mortality during Period 1, although the increase was only statistically significant for 16 of these conditions. Among the 10 most commonly admitted conditions (by number of daily hospital admissions during the baseline period), there was a statistically significant relative increase in mortality for patients with sepsis (20.1%), heart failure (17.6%), ischemic stroke (12.5%), device/graft/surgical complications (14.0%), cardiac dysrhythmias (14.4%), pneumonia (24.5%), respiratory failure (16.1%), and gastrointestinal hemorrhage (23.3%). In general, mortality returned to baseline or improved during Period 2. Thereafter, all 30 conditions showed increased mortality in Period 3. This increase was significant for only 16 conditions, which were not the same ones noted during Period 1. Of note, although there was higher mortality for some cardiovascular conditions (heart failure cardiac dysrhythmias), mortality for myocardial infarction remained unchanged from baseline across all 3 periods. In contrast, several solid cancer–related conditions showed progressively worsening mortality throughout the study, with 7.7% higher mortality in Period 1, 10.3% higher mortality in Period 2, and 16.5% higher mortality in Period 3, respectively, compared with baseline. Although a similar pattern was observed for acute renal failure and some neurologic conditions (traumatic brain injury, seizure, other nervous system disorders), mortality for drug poisonings and gastrointestinal bleeds improved over time.

In this study of unplanned hospitalizations from 51 community hospitals across 6 states in the US West, we found a significant increase in mortality—at a rate of approximately 5 to 6 excess deaths per 1000 hospitalizations—among patients admitted during the pandemic with a variety of non-COVID-19 illnesses and injuries. Higher in-hospital mortality was observed in the spring (March to May) and fall (October to December) of 2020 when COVID-19 case counts surged and shelter-in-place mandates were implemented. With the initial surge, higher mortality rates were largely transient, and, for most conditions evaluated, returned to baseline approximately 3 months after the pandemic onset. For the fall surge, mortality rates had not peaked by the end of the study period. Changes in mortality were closely and inversely correlated with hospital volume for non-COVID-19 illnesses during both surge periods.

Higher morbidity and mortality for patients without COVID-19 appears to be an unfortunate spillover effect that has been reported in several studies. Recent work examining national surveillance data suggest that up to one-third of excess deaths (deaths higher than those expected for season) early in the pandemic have occurred among patients without known COVID-19.^13,18-20 Specifically, these studies estimate that mortality rates in the United States increased by 15% to 19% in the spring of 2020; of the identified excess deaths, only 38% to 77% could be attributed to COVID-19, with the remainder attributed to cardiovascular disease, diabetes, and Alzheimer’s disease, among others. In addition, reports from several European countries and China examining population death data have found similar trends,^21-25 as well as a recent study examining excess deaths in nursing homes.²⁶ Our results are largely consistent with these earlier studies in that we describe higher mortality in a sample of patients hospitalized with a variety of common conditions that otherwise are routinely treated in US hospitals. Reporting these indirect casualties of COVID-19 is important to fully understand the pandemic’s toll on patients and healthcare systems.

Our work builds on the current body of literature, highlighting the consistent relationship between rising COVID-19 case counts, hospital volume, and excess mortality over more than one surge period. Although several studies have looked at trends in hospital admissions or population mortality rates, few have examined the two outcomes together. The close correlation between daily hospital admissions and in-hospital mortality in this study suggests that the pandemic changed how patients use healthcare resources in ways that were important for their health and outcomes. The higher mortality rate that we and others have observed likely is related to patients’ delaying care because of fear of contracting COVID-19. In one survey, more than 4 in 10 adults in the United States reported that they avoided medical care during the early pandemic.¹⁰ Importantly, even a few days delay for many conditions, such as heart failure or sepsis, can result in precipitous declines in clinical status and outcomes.

It also is possible that we found increased rates of in-hospital mortality simply because patients with more moderate illness chose to stay home, resulting in a patient population enriched with those more likely to die. We found mixed evidence in our data that the observed increases in mortality could be attributable to a smaller, sicker population. Some characteristics that might be protective, such as a slightly younger mean age and lower mean DRG weight, were more common among those hospitalized during the pandemic. However, other characteristics, such as a slightly higher MEWS score and a greater percentage of total hospitalizations in the higher mortality subgroup, also were noted during the pandemic (Table 1). We do note, however, that the differences in these severity-related characteristics were small across the study periods. Further adjusting for these characteristics in our sensitivity analyses did not appreciably change our main findings, suggesting that the mortality increase could not be explained by changes in case-mix alone.

Other factors not dependent on patient behavior, such as barriers to accessing timely ambulatory care and impacts in the quality of care delivered, might have contributed. Shelter-in-place orders, reduced in-person access to clinicians in the ambulatory setting, slow implementation of telehealth services (with uncertainty about their equivalence to in-person exams), as well as delays in diagnostic tests and outpatient procedures could have played a role, especially during early months of the pandemic.²⁷ Significant changes to ambulatory health care delivery might have left many patients with chronic illnesses or complex medical needs with limited care options. Importantly, these care interruptions might have had greater implications for some patients, such as those with cancer who rely on intensive, largely outpatient-based treatment.^28,29 This, in part, could explain why we found persistently increased mortality among patients hospitalized with cancer after the spring surge. Later into the pandemic, however, most health systems had developed processes that allowed clinicians to resume timely care of ambulatory patients. Because of this, increases in mortality observed during the fall surge likely stem from other factors, such as patient behavior.

It is possible that care delays or changes in the quality of care delivered during the index hospitalization or pre-hospital setting might have contributed to the observed increase in mortality. This is particularly true for acute, time-sensitive conditions such as sepsis and stroke. Extra time spent donning personal protective equipment and/or new protocols instituted during the pandemic likely impacted the speed of emergency medical services transport, timeliness of ED evaluation, and delivery of definitive therapy. Although most hospitals in this study were not overwhelmed by the pandemic, the complexities associated with caring for known and suspected COVID-19 patients alongside those without the disease might have altered ideal care practices and strained healthcare teams.³⁰ In addition, nearly all hospitalized patients during this period were deprived of in-person advocacy by family members, who were not permitted to visit.

Important limitations with this study exist. First, the data come only from hospitals in the western United States. Second, some data elements such as triage scores or vital signs were not available for the entire population, potentially limiting some risk-adjustment. Third, we were unable to determine the root cause of excess mortality based on our study design and the coded variables available. It is unknown to what extent undiagnosed COVID-19 played a role. Early in the pandemic, many community hospitals did not have access to timely COVID-19 testing, and some cases might have not been diagnosed.³¹ However, we do not expect this to be a significant concern in the later months of the pandemic, as testing became more widespread and hospitals implemented surveillance screening for COVID-19 for inpatients.

Our study indicates that the COVID-19 pandemic was associated with increased mortality among patients hospitalized for a range of clinical conditions. Although higher observed mortality rates were limited to periods of high COVID-19 activity, future studies will need to tease out the extent to which these findings relate to patient factors (ie, delayed presentation and more severe disease) or systemic factors (reduction in access or changes in quality of care). This is of key importance, and appropriate solutions will need to be developed to mitigate adverse impacts with this and future pandemics.

One of the most striking features of the early COVID-19 pandemic was the sudden and sharp reductions in emergency department (ED) visits and hospitalizations throughout the United States.^1-4 Several studies have documented lower rates of hospitalization for many emergent, time-sensitive conditions, such as acute myocardial infarction, stroke, and hyperglycemic crises, starting shortly after community transmission of COVID-19 was recognized and social distancing guidelines were implemented.^5-8 In most cases, hospital volumes rebounded after an initial drop, stabilizing at somewhat lower levels than those expected from historic trends.⁹

The observed shifts in hospital use largely have been attributed to patients’ forgoing or delaying necessary care,¹⁰ which underscores the indirect effects of the pandemic on patients without COVID-19.¹¹ To date, the extent to which outcomes for patients without COVID-19 have been adversely affected is less well understood. Evidence suggests patients with acute and chronic illnesses have experienced increased morbidity and mortality since the onset of the pandemic. For example, in northern California, abrupt declines in ED visits for cardiac symptoms were coupled with higher rates of out-of-hospital cardiac arrest.¹² Moreover, states with higher rates of COVID-19 also reported increased deaths attributed to heart disease, diabetes, and other conditions.¹³

To better understand these potential indirect effects, this study used data from a large, multistate health care system to examine changes in hospital volume and its relationship to in-hospital mortality for patients without COVID-19 during the first 10 months of the pandemic.

Setting and Participants

We examined unplanned hospitalizations from January 2019 to December 2020 at 51 community hospitals across 6 states (Alaska, Washington, Montana, Oregon, California, and Texas) in the Providence St. Joseph Health system. Hospitals within the Providence system share a common standard dataset for each encounter with a centralized cloud data warehouse from which we extracted clinical and demographic data. No hospitals entered or left the system during the study period. Hospitalizations were considered unplanned if they had an “urgent” or “emergency” service type in the record; most originated in the ED. Hospitalizations for children younger than 18 years and those with evidence of COVID-19 (International Classification of Disease, Tenth Revision, Clinical Modification U07.1, a positive COVID-19 polymerase chain reaction test during the encounter, or an infection control-assigned label of COVID-19) were excluded. The Providence St. Joseph Health Institutional Review Board approved this study.

Measures

Trends in daily hospitalizations and their relationship to adjusted in-hospital mortality (percentage of patients who died during their hospital admission) were examined over time. In preliminary models using segmented regression, we identified three distinct pandemic periods with different trends in daily hospitalizations: (1) a 10-week period corresponding to the spring COVID-19 surge (March 4 to May 13, 2020; Period 1), (2) an intervening period extending over the summer and early fall (May 14 to October 19, 2020; Period 2), and (3) a second 10-week period corresponding to the fall COVID-19 surge (October 20 to December 31, 2020; Period 3). In-hospital mortality for these periods was compared with a baseline period (pre-COVID-19) from January 1, 2019 to March 3, 2020. To further assess differences in mortality by clinical condition, hospitalizations were first grouped by primary diagnosis using Clinical Classifications Software Refined (CCSR) categories from the Agency for Healthcare Research and Quality¹⁴ and ranked by the number of observed deaths and the percentage of patients who died while hospitalized in 2020. We selected common conditions that had >35 total deaths and an in-hospital mortality rate ≥1% for condition-specific analyses, of which 30 met these criteria.

Analysis

Multivariate logistic regression was used to evaluate changes in mortality for each of the pandemic periods compared with baseline for the overall cohort and selected diagnosis groups. Our main model adjusted for age, sex, race/ethnicity (White, Black, Latinx, Asian or Pacific Islander, and other), primary payor (commercial, Medicaid, Medicare, other, and self-pay), the presence or absence of 31 chronic comorbidities in the medical record, primary admitting diagnosis grouped by CCSR category (456 total diagnostic groups), and hospital fixed-effects to account for clustering. Results are expressed as the average marginal effects of each pandemic period on in-hospital mortality (eg, adjusted percentage point change in mortality over baseline). The number of excess deaths in each period was calculated by multiplying the estimated percentage point change in mortality for each period by the total number of hospitalizations. These excess deaths were subtracted from the number of observed deaths to derive the number of deaths that would be expected if pre-pandemic mortality rates persisted.

To further assess whether changes in adjusted mortality could be attributed to a smaller, sicker population of patients presenting to the hospital during the pandemic (meaning that less acutely ill patients stayed home), we conducted two sensitivity analyses. First, we tested whether substituting indicators for Medicare Severity Diagnosis Groups (MS-DRG) in lieu of CCSR categories had any impact on our results. MS-DRGs are designed to account for a patient’s illness severity and expected costs, whereas CCSR categories do not.¹⁵ MS-DRGs also better distinguish between surgical versus medical conditions. We re-ran our main model using indicators for CCSR to control for diagnostic mix, but further adjusted for severity using the DRG weight for the primary diagnosis and Modified Early Warning Score (MEWS) as continuous covariates. MEWS is a physiologic scoring system that incorporates abnormal vital signs and data related to mental status during the first 24 hours of a patient’s hospitalization into a risk-based score that has been shown to predict hospital mortality and need for intensive care.^16,17 These sensitivity analyses were performed on a subset of inpatient admissions because DRG data are not available for hospitalizations billed as an observation stay, and only approximately 70% of hospitals in the sample contributed vital sign data to the Providence data warehouse. All statistical analyses were conducted with R, version 3.6.3 (R Foundation for Statistical Computing) and SAS Enterprise Guide 7.1 (SAS Institute Inc).

The characteristics of our sample are described in Table 1. A total of 61,300, 159,430, and 65,923 hospitalizations occurred in each of the three pandemic periods, respectively, compared with 503,190 hospitalizations in the pre-pandemic period. The mean (SD) age of patients in the study was 63.2 (19.4) years; most were women (52.4%), White (70.6%), and had Medicare as their primary payor (53.7%). Less than half (42.7%) of hospitalizations occurred in California, and just under one-quarter were observation stays (23.2%). Patient characteristics were similar in the pre-COVID-19 and COVID-19 pandemic periods.

Figure 1 shows trends in hospital volume and mortality. Overall daily hospitalizations declined abruptly from a mean of 1176 per day in the pre-pandemic period to 617 per day (47.5% relative decrease) during the first 3 weeks of Period 1. Mean daily hospitalizations began to rise over the next 2 months (Period 1), reaching steady state at <1000 hospitalizations per day (15% relative decrease from baseline) during Period 2. During Period 3, we observed a decline in mean daily hospitalizations, with a low point of 882 per day on December 31, 2020 (25% relative decrease from baseline), corresponding to the end of our study period. Although hospital volumes declined during both COVID-19 surge periods, the percentage of patients who died during their hospitalization increased. There was an initial spike in in-hospital mortality that peaked approximately 1 month into the pandemic (middle of Period 1), a return to levels at or slightly below that before the pandemic by the beginning of Period 2, and then a rise throughout the autumn COVID-19 surge in Period 3, not yet peaking by the end of the study.

Adjusted in-hospital mortality for the three COVID-19 periods compared with the pre-pandemic period is presented in Table 2. The percentage of patients who died during their hospitalization rose from 2.9% in the pre-pandemic period to 3.4% during Period 1 (absolute difference, 0.6 percentage points; 95% CI, 0.5-0.7), corresponding to a 19.3% relative increase during the spring COVID-19 surge. Among the subset of patients hospitalized with 1 of the 30 conditions selected for individual analysis, mortality increased from 5.0% to 5.9% during the same time period (absolute difference, 0.9 percentage points; 95% CI, 0.8-1.1), corresponding to an 18.9% relative increase. In Period 2, in-hospital mortality was similar to that noted pre-pandemic for the overall cohort and the 30 selected conditions. During Period 3, in-hospital mortality increased by a magnitude similar to that observed in Period 1 for all hospitalizations combined (absolute difference, 0.5 percentage points; 95% CI, 0.0-0.6; corresponding to a 16.5% relative increase) as well as the subgroup with 1 of the 30 selected conditions (0.9 percentage points; 95% CI, 0.8-1.0; corresponding to an 18% relative increase). Further adjustment for severity by swapping CCSR categories with MS-DRG indicators or inclusion of DRG weight and MEWS score as covariates in our sensitivity analyses did not change our results.

Table 3 and the Appendix Figure describe changes in volume and adjusted in-hospital mortality for the 30 conditions selected for analysis. There was a decrease in the mean daily admissions for all conditions studied. Among the 30 conditions, 26 showed increased mortality during Period 1, although the increase was only statistically significant for 16 of these conditions. Among the 10 most commonly admitted conditions (by number of daily hospital admissions during the baseline period), there was a statistically significant relative increase in mortality for patients with sepsis (20.1%), heart failure (17.6%), ischemic stroke (12.5%), device/graft/surgical complications (14.0%), cardiac dysrhythmias (14.4%), pneumonia (24.5%), respiratory failure (16.1%), and gastrointestinal hemorrhage (23.3%). In general, mortality returned to baseline or improved during Period 2. Thereafter, all 30 conditions showed increased mortality in Period 3. This increase was significant for only 16 conditions, which were not the same ones noted during Period 1. Of note, although there was higher mortality for some cardiovascular conditions (heart failure cardiac dysrhythmias), mortality for myocardial infarction remained unchanged from baseline across all 3 periods. In contrast, several solid cancer–related conditions showed progressively worsening mortality throughout the study, with 7.7% higher mortality in Period 1, 10.3% higher mortality in Period 2, and 16.5% higher mortality in Period 3, respectively, compared with baseline. Although a similar pattern was observed for acute renal failure and some neurologic conditions (traumatic brain injury, seizure, other nervous system disorders), mortality for drug poisonings and gastrointestinal bleeds improved over time.

In this study of unplanned hospitalizations from 51 community hospitals across 6 states in the US West, we found a significant increase in mortality—at a rate of approximately 5 to 6 excess deaths per 1000 hospitalizations—among patients admitted during the pandemic with a variety of non-COVID-19 illnesses and injuries. Higher in-hospital mortality was observed in the spring (March to May) and fall (October to December) of 2020 when COVID-19 case counts surged and shelter-in-place mandates were implemented. With the initial surge, higher mortality rates were largely transient, and, for most conditions evaluated, returned to baseline approximately 3 months after the pandemic onset. For the fall surge, mortality rates had not peaked by the end of the study period. Changes in mortality were closely and inversely correlated with hospital volume for non-COVID-19 illnesses during both surge periods.

Higher morbidity and mortality for patients without COVID-19 appears to be an unfortunate spillover effect that has been reported in several studies. Recent work examining national surveillance data suggest that up to one-third of excess deaths (deaths higher than those expected for season) early in the pandemic have occurred among patients without known COVID-19.^13,18-20 Specifically, these studies estimate that mortality rates in the United States increased by 15% to 19% in the spring of 2020; of the identified excess deaths, only 38% to 77% could be attributed to COVID-19, with the remainder attributed to cardiovascular disease, diabetes, and Alzheimer’s disease, among others. In addition, reports from several European countries and China examining population death data have found similar trends,^21-25 as well as a recent study examining excess deaths in nursing homes.²⁶ Our results are largely consistent with these earlier studies in that we describe higher mortality in a sample of patients hospitalized with a variety of common conditions that otherwise are routinely treated in US hospitals. Reporting these indirect casualties of COVID-19 is important to fully understand the pandemic’s toll on patients and healthcare systems.

Our work builds on the current body of literature, highlighting the consistent relationship between rising COVID-19 case counts, hospital volume, and excess mortality over more than one surge period. Although several studies have looked at trends in hospital admissions or population mortality rates, few have examined the two outcomes together. The close correlation between daily hospital admissions and in-hospital mortality in this study suggests that the pandemic changed how patients use healthcare resources in ways that were important for their health and outcomes. The higher mortality rate that we and others have observed likely is related to patients’ delaying care because of fear of contracting COVID-19. In one survey, more than 4 in 10 adults in the United States reported that they avoided medical care during the early pandemic.¹⁰ Importantly, even a few days delay for many conditions, such as heart failure or sepsis, can result in precipitous declines in clinical status and outcomes.

It also is possible that we found increased rates of in-hospital mortality simply because patients with more moderate illness chose to stay home, resulting in a patient population enriched with those more likely to die. We found mixed evidence in our data that the observed increases in mortality could be attributable to a smaller, sicker population. Some characteristics that might be protective, such as a slightly younger mean age and lower mean DRG weight, were more common among those hospitalized during the pandemic. However, other characteristics, such as a slightly higher MEWS score and a greater percentage of total hospitalizations in the higher mortality subgroup, also were noted during the pandemic (Table 1). We do note, however, that the differences in these severity-related characteristics were small across the study periods. Further adjusting for these characteristics in our sensitivity analyses did not appreciably change our main findings, suggesting that the mortality increase could not be explained by changes in case-mix alone.

Other factors not dependent on patient behavior, such as barriers to accessing timely ambulatory care and impacts in the quality of care delivered, might have contributed. Shelter-in-place orders, reduced in-person access to clinicians in the ambulatory setting, slow implementation of telehealth services (with uncertainty about their equivalence to in-person exams), as well as delays in diagnostic tests and outpatient procedures could have played a role, especially during early months of the pandemic.²⁷ Significant changes to ambulatory health care delivery might have left many patients with chronic illnesses or complex medical needs with limited care options. Importantly, these care interruptions might have had greater implications for some patients, such as those with cancer who rely on intensive, largely outpatient-based treatment.^28,29 This, in part, could explain why we found persistently increased mortality among patients hospitalized with cancer after the spring surge. Later into the pandemic, however, most health systems had developed processes that allowed clinicians to resume timely care of ambulatory patients. Because of this, increases in mortality observed during the fall surge likely stem from other factors, such as patient behavior.

It is possible that care delays or changes in the quality of care delivered during the index hospitalization or pre-hospital setting might have contributed to the observed increase in mortality. This is particularly true for acute, time-sensitive conditions such as sepsis and stroke. Extra time spent donning personal protective equipment and/or new protocols instituted during the pandemic likely impacted the speed of emergency medical services transport, timeliness of ED evaluation, and delivery of definitive therapy. Although most hospitals in this study were not overwhelmed by the pandemic, the complexities associated with caring for known and suspected COVID-19 patients alongside those without the disease might have altered ideal care practices and strained healthcare teams.³⁰ In addition, nearly all hospitalized patients during this period were deprived of in-person advocacy by family members, who were not permitted to visit.

Important limitations with this study exist. First, the data come only from hospitals in the western United States. Second, some data elements such as triage scores or vital signs were not available for the entire population, potentially limiting some risk-adjustment. Third, we were unable to determine the root cause of excess mortality based on our study design and the coded variables available. It is unknown to what extent undiagnosed COVID-19 played a role. Early in the pandemic, many community hospitals did not have access to timely COVID-19 testing, and some cases might have not been diagnosed.³¹ However, we do not expect this to be a significant concern in the later months of the pandemic, as testing became more widespread and hospitals implemented surveillance screening for COVID-19 for inpatients.

Our study indicates that the COVID-19 pandemic was associated with increased mortality among patients hospitalized for a range of clinical conditions. Although higher observed mortality rates were limited to periods of high COVID-19 activity, future studies will need to tease out the extent to which these findings relate to patient factors (ie, delayed presentation and more severe disease) or systemic factors (reduction in access or changes in quality of care). This is of key importance, and appropriate solutions will need to be developed to mitigate adverse impacts with this and future pandemics.

Payors have been refining reimbursement policies for observation and inpatient stays over the past decade, and the effects on the healthcare payment system are significant.^1-4 Advocates claim that observation status could improve efficiency in the use of healthcare resources by reducing emergency department (ED) crowding and lowering costs for inpatient care.^5,6 Critics consider observation status to be a cost-shifting strategy that could lead to financial burdens for patients and hospitals.^7,8

Although reimbursement policies for observation stays traditionally have been set by the Centers for Medicare and Medicaid Services (CMS) in a uniform manner,^4,8 state Medicaid programs and commercial health insurers have developed a variety of policies for using observation status in broader populations and hospitals.^9-15 Coverage criteria and implementation timelines of these policies vary by states and commercial insurers.^11-15 For example, the California Department of Health Care Services did not have a specific reimbursement rate for observation stays in 2020, while some state Medicaid programs have had reimbursement policies for observation services in place since 2010.^11-15 These inconsistencies likely result in greater variation in use of observation stays across children’s hospitals than general hospitals.

Previous studies have shown rising trends in use of observation stays among adult patient populations and related implications for patients and general hospitals,^16-19 but few studies have reported the trends for pediatric populations. In this study, we sought to (1) describe recent trends of observation stays for pediatric populations at children’s hospitals from 2010 through 2019 and (2) investigate features of this shifting pattern for pediatric populations and hospital-level use of observation stays.

Study Design, Data, and Populations

We performed a retrospective analysis of the Pediatric Health Information System (PHIS), an administrative database that contains inpatient, observation, ambulatory, and ED encounter-level data from 50 not-for-profit, tertiary care children’s hospitals affiliated with the Children’s Hospital Association (CHA).²⁰ PHIS has an indicator to classify patient types (inpatient, observation, ED visits, ambulatory surgery, clinic visit, and others). The data are de-identified at the time of submission and subjected to validity and reliability checks by CHA and Truven Health Analytics (Ann Arbor, MI) before being included in PHIS. Each encounter in PHIS has only one patient type; therefore, encounters that transition to a higher level of care are assigned to their highest level of care (eg, a patient transitions from observation to inpatient status is classified as an inpatient encounter) to avoid duplicate counting.

To ensure consistent evaluations over time, we included 29 children’s hospitals that consistently reported both inpatient and observation data to PHIS across all quarters from 2010 through 2019. We identified the 20 most common clinical conditions using the All Patients Refined Diagnosis Related Groups (APR-DRGs; 3M Corporation) based upon their total frequencies of observation and inpatient stays over the study period. Regression analyses were conducted using all encounters within the 20 most common APR-DRGs.

Because all data have been de-identified in the PHIS database, the institutional review board at Ann and Robert H. Lurie Children’s Hospital of Chicago granted this study institutional review board–exempt status.

Main Outcome and Measures

We first presented longitudinal trends of observation stays for children’s hospitals using annual percentage of observation stays defined as:

To determine whether different pediatric populations have different trends of observation stays, we measured the growth rates of observation stays for each APR-DRG. Specifically, we first calculated the percentage of observation stays by APR-DRGs and years as described, and then calculated the growth rate of observation stays for each APR-DRG:

Next, we employed prolonged length of stay (LOS) and hospitalization resource-intensity scores for kids (H-RISK) to further investigate the shifting pattern of observation stays. Because most state Medicaid and commercial policies dictate that observation stays should not last longer than 48 hours, we defined prolonged LOS as >2 days.^11-15 We defined the annual percentage of observation stays with prolonged LOS for each year as:

Numerators and denominators of the three measures were obtained by pooling all children’s hospitals included in this study. H-RISK is a continuous variable developed by CHA to measure use of intensive care for children, which is comparable across various APR-DRGs.²¹ Changes in the empirical distribution of H-RISK from observation stays were presented over years using percentiles.

Other measures included sex, age, race, payor, and LOS. To investigate the use of observation stays among payors, we categorized payors into five groups: private, in-state Medicaid (managed care), in-state Medicaid (Children’s Health Insurance Program [CHIP]/others), other government, and all others, according to the data availability. The “private” group consisted of commercial preferred provider organizations, commercial health maintenance organizations, and commercial others. We combined both CHIP and in-state Medicaid (others), including Medicaid fee-for-service or unspecified Medicaid together as “in-state Medicaid (CHIP/others).” Detailed categorization information is summarized in Appendix Table 1. LOS was classified into four groups: 1 day (24 hours), 2 days (48 hours), 3 to 4 days, and >4 days.

Statistical Analysis

Descriptive statistics were stratified by inpatient and observation status and were summarized using frequency, percent, median, and interquartile range (IQR). Chi-square or Wilcoxon rank-sum tests were performed to examine differences between observation and inpatient status. Trends in annual percentage of observation stays and annual percentage of observation stays with prolonged LOS were estimated using first-order autoregressive models, in which year was considered a continuous variable. A nonparametric measure of rank correlation (Spearman’s rank correlation coefficient) was employed to evaluate the correlation between year and H-RISK from observation stays.

The risk-adjusted probability of being admitted as an observation stay was estimated using generalized linear mixed models by adjusting for year, age, sex, race, payor, LOS, H-RISK, and a random intercept for each hospital to control for patient clustering within a hospital (Appendix Model). Hospital-level use of observation stays was measured by risk-adjusted percent use of observation stays for each hospital using the predicted values from generalized linear mixed models. All analyses were performed using SAS software, version 9.4 (SAS Institute) and R (R Core Team, 2019), and P < .05 was considered statistically significant.

Increasing Trend of Observation Stays

Over the study period, there were 5,611,001 encounters, including 3,901,873 (69.5%) inpatient and 1,709,128 (30.5%) observation stays (Appendix Table 1). The number of observation stays increased from 117,246 in 2010 to 207,842 in 2019, and the number of inpatient stays slightly increased from 378,433 to 397,994 over the 10 years (Appendix Table 1). Because of different growth rates between observation and inpatient status, the annual percentage of observation stays increased from 23.7% in 2010 to 34.3% in 2019, while the annual percentage of inpatient stays decreased from 76.3% in 2010 to 65.7% in 2019 (Appendix Table 1; Figure 1, P < .001).

Different Growth Rates of Observation Stays for Various Pediatric Populations

As shown in the Table, growth rates of observation stays increased for 19 of the 20 most common APR-DRGs. The four APR-DRGs having the highest growth rates in observation stays were appendectomy, diabetes mellitus, kidney and urinary tract infections, and cellulitis and other bacterial skin infections (Appendix Figure). In particular, the annual percentage of observation stays for appendectomy increased from 19.8% in 2010 to 54.7% in 2019, with the number of observation stays growing from 2,321 to 7,876, while the number of inpatient stays decreased from 9,384 to 6,535 (Appendix Figure). The annual percentage of observation stays for diabetes mellitus increased from 8.16% in 2010 to 22.74% in 2019. Tonsil and adenoid procedures consistently held the largest numbers of observation stays across the 10 years among all the APR-DRGs, with 115,207 and 31,125 total observation and inpatient stays, respectively (Table).

Characteristics of Observation and Inpatient Stays

Patient characteristics are summarized in Appendix Table 1. There were 542,344 (32.9%) observation stays among patients with in-state Medicaid (managed care), and 241,157 (27.4%) observation stays among in-state Medicaid (CHIP/others). The percentages of observation and inpatient stays were 29.8% and 70.2% for private payor, as well as 29.6% and 70.4% for other government payor. Overall, the median (IQR) of H-RISK among observation stays was 0.79 (0.57-1.19) vs 1.23 (0.72-2.43) for inpatient stays. There were 1,410,694 (82.5%) observation stays discharged within 1 day and 243,972 (14.3%) observation stays discharged within 2 days. However, there were 47,413 (2.8%) and 7,049 (0.4%) observation stays with LOS 3 to 4 days or >4 days, respectively.

Shifting Pattern in Observation Stays

The annual percentage of observation stays with prolonged LOS (>2 days) rose from 1.1% in 2010 to 4.6% in 2019 (P < .001; Figure 2). The empirical distribution of H-RISK from observation stays by years further suggests a slightly increasing trend in intensity of care under observation stays. As shown in Appendix Table 2, although the 1st, 5th, 10th, 25th, and 99th percentiles of H-RISK were relatively stable, the 50th, 75th, 90th, and 95th percentiles of H-RISK were increasing over time. The correlation between year and intensity of care used under observation stays (H-RISK from observation stays) was found to be weak but significantly positive (Spearman correlation coefficients = 0.04; P < .001).

Interaction coefficients from our regression model demonstrate that the existing inverse association between H-RISK and odds of admission as an observation stay became less negative over the years. In 2010, the adjusted odds ratio (OR) of H-RISK was 0.57 (95% CI, 0.55-0.59). By 2017, the adjusted OR had increased to 0.65 (95% CI, 0.64-0.66). Compared with 2010, the seven adjusted ORs of H-RISK at years 2012 through 2018 were observed to be higher and statistically significant (P < .001, Appendix Table 3).

Hospitals-Level Use of Observation Stays

After adjusting for all covariates and hospital random effects, hospital-level use of observation stays increased between 2010 and 2019 for 26 out of 29 children’s hospitals. Although observation status essentially was not used at two children’s hospitals over the study period, the median hospital-level use of observation stays was 26% in 2010 (IQR, 3%-36%) and increased to 46% (IQR: 39%; 55%) in 2019. As shown in Figure 3, the number of hospitals with a low percentage of observation stays (<26%) decreased from 15 in 2010 to 4 in 2019. The number of hospitals with a high percentage of observation stays (≥51%) increased from 5 in 2010 to 10 in 2019. Nevertheless, there remained significant variation in the use of observation stays, and the hospital-level use ranged from 0% to 67% in 2019.

By 2020, observation status has become a key component of healthcare for pediatric patients, and its relevance for children’s hospitals recently has been described.^22,23 However, trends in observation stays for pediatric populations are not known. This represents the first study showing temporal trends of observation stays at children’s hospitals after 2010. Our results confirm that the increase in observation stays for pediatric populations is not attributable to decreasing patient acuity at children’s hospitals. We found a weak but significantly positive correlation between year and intensity of care used under observation stays. Although this correlation might not be clinically important, it demonstrates that patient acuity in observation stays is not decreasing. Regression results suggest that observation stays now encompass patients who need relatively higher intensity of care compared with those admitted under observation status in 2010.

This study also identifies a unique pattern in the use of observation stays among pediatric populations. Earlier studies exclusively focused on observation stays that were admitted from EDs.²⁴ Our results indicate that observation status has been used beyond a bridge from ED care to inpatient admission. In particular, observation status has expanded to include pediatric populations with more diverse clinical conditions (eg, appendicitis and diabetes mellitus), and has become a substantial component of postprocedural admissions (Appendix Figure). Looking forward, it is likely that the use of observation stays might surpass inpatient admissions for more conditions that primarily involve short-term stays.

Observation status originally was designed as a reimbursement strategy for patients who needed short stays in dedicated ED units or hospitals, but did not qualify for inpatient services.^5,25 After several changes in reimbursement policies, CMS released the “two midnight rule” for Medicare beneficiaries in 2013, which replaced condition-based criteria with time-based criteria to determine an inpatient or observation stay.¹ Some Medicaid programs and commercial payors have developed similar policies. Unlike the universal policy for Medicare populations, the regulations for pediatric populations vary by states and health insurers.^11-15,26-28 This might partially explain the wide variation observed among children’s hospital-level use of observation stays. For example, the California Medicaid program did not have a reimbursement rate for observation services as of 2020, while the Texas Medicaid program has had a policy for observation stays since 2010.^12,13 We found that two children’s hospitals in California had the lowest use of observation stays (almost zero), whereas the hospital-level use of observation stays was more than 50% for three out of four children’s hospitals in Texas. In addition to reimbursement policies, individual hospitals also might have different strategies for observation status designation. An earlier survey showed that there was lack of consistency in billing and payor-based designations of observation status at children’s hospitals.²⁹ These findings suggest that children’s hospital-level use of observation stays likely is influenced by reimbursement policy and practical strategy for observation status determination.

Earlier studies reported that observation status could be a more efficient use of healthcare resources.^5,6 However, there are still at least two concerns relevant to children’s hospitals during the last decade. The first is whether the use of observation stays can promote cost-saving or if it is just a cost-shifting strategy. An earlier study demonstrated that observation stays with prolonged LOS might increase risk of cost-sharing among adult patients.²⁹ Our study reveals an increasing trend of observation stays with prolonged LOS for pediatric patients. Similar to adult patients, LOS exceeding 24 or 48 hours could lead to uncovered healthcare costs and financial burdens on families.^30-32 Meanwhile, children’s hospitals also might take on a higher financial liability by implementing observation status. Earlier studies have indicated that resource use between observation and inpatient stays at children’s hospitals is similar, and increasing use of observation stays might lead to financial risk rather than cost effectiveness.³³ Further, administrative costs of observation determination are considerably high.³⁴ Medicaid is the major payor for pediatric patients in children’s hospitals. In this study, more than 50% of encounters were paid through Medicaid programs. It is well known that Medicaid reimbursement rates are lower than Medicare and commercial plans.³⁵ Therefore, the cost-saving conclusion drawn from Medicare patients cannot be generalized to pediatric populations at children’s hospitals without cautious reevaluation.

A second concern with increasing use of observation stays is selection bias in public reporting and comparisons of hospital performance. Presently, four main categories of quality indicators established by the Agency for Healthcare Research and Quality rely heavily on inpatient encounters.³⁶ In this study, we found that the range of hospital-level use of observation stays was large. In 2019, the risk-adjusted percent use of observation stays was less than 5% at three hospitals, while the percent use was greater than 60% in another three hospitals. Therefore, comparisons made without uniform accounting of observation stays might have significant implications for national rankings of children’s hospitals across the United States. These consequences have been investigated in several published studies.^22,23,37-39

There are several limitations to our study. First, the study sample was limited to children’s hospitals that consistently reported inpatient and observation data over the entire study period. Eighteen hospitals (86%) excluded from this study did not consistently submit inpatient and observation data to PHIS from 2010 through 2019. The primary purpose of this study was to present temporal trends of observation stays for children’s hospitals, and it was important to build the hospital cohort based on valid and consistent data during the study period. Appendix Table 4 presents differences of hospital characteristics by included and excluded groups of hospitals. Excluded hospitals might have fewer resources (eg, fewer pediatric intensive care beds). Nonetheless, the selection of hospitals was optimized based on data availability. Second, this study was a retrospective review of an administrative database of children’s hospitals and units. The sample does not represent all children’s hospitals or pediatric patients in the United States, but there are no available data sources—that we know of—that can generate national estimates for both inpatient and observation stays. Third, we did not attempt to conclusively infer any causal effects, and several factors could explain the increasing trends, such as reimbursement policies, hospital-level implementation strategies, determination guidelines for observation status designation, as well as changes in clinical care. Further studies should investigate impact of these factors on the use of observation stays for pediatric patients and children’s hospitals.

Observation status has been increasingly used for pediatric patients with more diverse clinical conditions, and there is a rising trend of prolonged LOS among observation stays since 2010. Considerable variation exists in hospital-level use of observation stays across children’s hospitals. Observation status could be an opportunity to improve efficiency of healthcare resource use or could lead to a financial risk for patients with prolonged LOS. Future studies should explore appropriateness of observation care in clinical practice through leveraging efficient care and alleviating financial risk.

Payors have been refining reimbursement policies for observation and inpatient stays over the past decade, and the effects on the healthcare payment system are significant.^1-4 Advocates claim that observation status could improve efficiency in the use of healthcare resources by reducing emergency department (ED) crowding and lowering costs for inpatient care.^5,6 Critics consider observation status to be a cost-shifting strategy that could lead to financial burdens for patients and hospitals.^7,8

Although reimbursement policies for observation stays traditionally have been set by the Centers for Medicare and Medicaid Services (CMS) in a uniform manner,^4,8 state Medicaid programs and commercial health insurers have developed a variety of policies for using observation status in broader populations and hospitals.^9-15 Coverage criteria and implementation timelines of these policies vary by states and commercial insurers.^11-15 For example, the California Department of Health Care Services did not have a specific reimbursement rate for observation stays in 2020, while some state Medicaid programs have had reimbursement policies for observation services in place since 2010.^11-15 These inconsistencies likely result in greater variation in use of observation stays across children’s hospitals than general hospitals.

Previous studies have shown rising trends in use of observation stays among adult patient populations and related implications for patients and general hospitals,^16-19 but few studies have reported the trends for pediatric populations. In this study, we sought to (1) describe recent trends of observation stays for pediatric populations at children’s hospitals from 2010 through 2019 and (2) investigate features of this shifting pattern for pediatric populations and hospital-level use of observation stays.

Study Design, Data, and Populations

We performed a retrospective analysis of the Pediatric Health Information System (PHIS), an administrative database that contains inpatient, observation, ambulatory, and ED encounter-level data from 50 not-for-profit, tertiary care children’s hospitals affiliated with the Children’s Hospital Association (CHA).²⁰ PHIS has an indicator to classify patient types (inpatient, observation, ED visits, ambulatory surgery, clinic visit, and others). The data are de-identified at the time of submission and subjected to validity and reliability checks by CHA and Truven Health Analytics (Ann Arbor, MI) before being included in PHIS. Each encounter in PHIS has only one patient type; therefore, encounters that transition to a higher level of care are assigned to their highest level of care (eg, a patient transitions from observation to inpatient status is classified as an inpatient encounter) to avoid duplicate counting.

To ensure consistent evaluations over time, we included 29 children’s hospitals that consistently reported both inpatient and observation data to PHIS across all quarters from 2010 through 2019. We identified the 20 most common clinical conditions using the All Patients Refined Diagnosis Related Groups (APR-DRGs; 3M Corporation) based upon their total frequencies of observation and inpatient stays over the study period. Regression analyses were conducted using all encounters within the 20 most common APR-DRGs.

Because all data have been de-identified in the PHIS database, the institutional review board at Ann and Robert H. Lurie Children’s Hospital of Chicago granted this study institutional review board–exempt status.

Main Outcome and Measures

We first presented longitudinal trends of observation stays for children’s hospitals using annual percentage of observation stays defined as:

To determine whether different pediatric populations have different trends of observation stays, we measured the growth rates of observation stays for each APR-DRG. Specifically, we first calculated the percentage of observation stays by APR-DRGs and years as described, and then calculated the growth rate of observation stays for each APR-DRG:

Next, we employed prolonged length of stay (LOS) and hospitalization resource-intensity scores for kids (H-RISK) to further investigate the shifting pattern of observation stays. Because most state Medicaid and commercial policies dictate that observation stays should not last longer than 48 hours, we defined prolonged LOS as >2 days.^11-15 We defined the annual percentage of observation stays with prolonged LOS for each year as:

Numerators and denominators of the three measures were obtained by pooling all children’s hospitals included in this study. H-RISK is a continuous variable developed by CHA to measure use of intensive care for children, which is comparable across various APR-DRGs.²¹ Changes in the empirical distribution of H-RISK from observation stays were presented over years using percentiles.

Other measures included sex, age, race, payor, and LOS. To investigate the use of observation stays among payors, we categorized payors into five groups: private, in-state Medicaid (managed care), in-state Medicaid (Children’s Health Insurance Program [CHIP]/others), other government, and all others, according to the data availability. The “private” group consisted of commercial preferred provider organizations, commercial health maintenance organizations, and commercial others. We combined both CHIP and in-state Medicaid (others), including Medicaid fee-for-service or unspecified Medicaid together as “in-state Medicaid (CHIP/others).” Detailed categorization information is summarized in Appendix Table 1. LOS was classified into four groups: 1 day (24 hours), 2 days (48 hours), 3 to 4 days, and >4 days.

Statistical Analysis

Descriptive statistics were stratified by inpatient and observation status and were summarized using frequency, percent, median, and interquartile range (IQR). Chi-square or Wilcoxon rank-sum tests were performed to examine differences between observation and inpatient status. Trends in annual percentage of observation stays and annual percentage of observation stays with prolonged LOS were estimated using first-order autoregressive models, in which year was considered a continuous variable. A nonparametric measure of rank correlation (Spearman’s rank correlation coefficient) was employed to evaluate the correlation between year and H-RISK from observation stays.

The risk-adjusted probability of being admitted as an observation stay was estimated using generalized linear mixed models by adjusting for year, age, sex, race, payor, LOS, H-RISK, and a random intercept for each hospital to control for patient clustering within a hospital (Appendix Model). Hospital-level use of observation stays was measured by risk-adjusted percent use of observation stays for each hospital using the predicted values from generalized linear mixed models. All analyses were performed using SAS software, version 9.4 (SAS Institute) and R (R Core Team, 2019), and P < .05 was considered statistically significant.

Increasing Trend of Observation Stays

Over the study period, there were 5,611,001 encounters, including 3,901,873 (69.5%) inpatient and 1,709,128 (30.5%) observation stays (Appendix Table 1). The number of observation stays increased from 117,246 in 2010 to 207,842 in 2019, and the number of inpatient stays slightly increased from 378,433 to 397,994 over the 10 years (Appendix Table 1). Because of different growth rates between observation and inpatient status, the annual percentage of observation stays increased from 23.7% in 2010 to 34.3% in 2019, while the annual percentage of inpatient stays decreased from 76.3% in 2010 to 65.7% in 2019 (Appendix Table 1; Figure 1, P < .001).

Different Growth Rates of Observation Stays for Various Pediatric Populations

As shown in the Table, growth rates of observation stays increased for 19 of the 20 most common APR-DRGs. The four APR-DRGs having the highest growth rates in observation stays were appendectomy, diabetes mellitus, kidney and urinary tract infections, and cellulitis and other bacterial skin infections (Appendix Figure). In particular, the annual percentage of observation stays for appendectomy increased from 19.8% in 2010 to 54.7% in 2019, with the number of observation stays growing from 2,321 to 7,876, while the number of inpatient stays decreased from 9,384 to 6,535 (Appendix Figure). The annual percentage of observation stays for diabetes mellitus increased from 8.16% in 2010 to 22.74% in 2019. Tonsil and adenoid procedures consistently held the largest numbers of observation stays across the 10 years among all the APR-DRGs, with 115,207 and 31,125 total observation and inpatient stays, respectively (Table).

Characteristics of Observation and Inpatient Stays

Patient characteristics are summarized in Appendix Table 1. There were 542,344 (32.9%) observation stays among patients with in-state Medicaid (managed care), and 241,157 (27.4%) observation stays among in-state Medicaid (CHIP/others). The percentages of observation and inpatient stays were 29.8% and 70.2% for private payor, as well as 29.6% and 70.4% for other government payor. Overall, the median (IQR) of H-RISK among observation stays was 0.79 (0.57-1.19) vs 1.23 (0.72-2.43) for inpatient stays. There were 1,410,694 (82.5%) observation stays discharged within 1 day and 243,972 (14.3%) observation stays discharged within 2 days. However, there were 47,413 (2.8%) and 7,049 (0.4%) observation stays with LOS 3 to 4 days or >4 days, respectively.

Shifting Pattern in Observation Stays

The annual percentage of observation stays with prolonged LOS (>2 days) rose from 1.1% in 2010 to 4.6% in 2019 (P < .001; Figure 2). The empirical distribution of H-RISK from observation stays by years further suggests a slightly increasing trend in intensity of care under observation stays. As shown in Appendix Table 2, although the 1st, 5th, 10th, 25th, and 99th percentiles of H-RISK were relatively stable, the 50th, 75th, 90th, and 95th percentiles of H-RISK were increasing over time. The correlation between year and intensity of care used under observation stays (H-RISK from observation stays) was found to be weak but significantly positive (Spearman correlation coefficients = 0.04; P < .001).

Interaction coefficients from our regression model demonstrate that the existing inverse association between H-RISK and odds of admission as an observation stay became less negative over the years. In 2010, the adjusted odds ratio (OR) of H-RISK was 0.57 (95% CI, 0.55-0.59). By 2017, the adjusted OR had increased to 0.65 (95% CI, 0.64-0.66). Compared with 2010, the seven adjusted ORs of H-RISK at years 2012 through 2018 were observed to be higher and statistically significant (P < .001, Appendix Table 3).

Hospitals-Level Use of Observation Stays

After adjusting for all covariates and hospital random effects, hospital-level use of observation stays increased between 2010 and 2019 for 26 out of 29 children’s hospitals. Although observation status essentially was not used at two children’s hospitals over the study period, the median hospital-level use of observation stays was 26% in 2010 (IQR, 3%-36%) and increased to 46% (IQR: 39%; 55%) in 2019. As shown in Figure 3, the number of hospitals with a low percentage of observation stays (<26%) decreased from 15 in 2010 to 4 in 2019. The number of hospitals with a high percentage of observation stays (≥51%) increased from 5 in 2010 to 10 in 2019. Nevertheless, there remained significant variation in the use of observation stays, and the hospital-level use ranged from 0% to 67% in 2019.

By 2020, observation status has become a key component of healthcare for pediatric patients, and its relevance for children’s hospitals recently has been described.^22,23 However, trends in observation stays for pediatric populations are not known. This represents the first study showing temporal trends of observation stays at children’s hospitals after 2010. Our results confirm that the increase in observation stays for pediatric populations is not attributable to decreasing patient acuity at children’s hospitals. We found a weak but significantly positive correlation between year and intensity of care used under observation stays. Although this correlation might not be clinically important, it demonstrates that patient acuity in observation stays is not decreasing. Regression results suggest that observation stays now encompass patients who need relatively higher intensity of care compared with those admitted under observation status in 2010.

This study also identifies a unique pattern in the use of observation stays among pediatric populations. Earlier studies exclusively focused on observation stays that were admitted from EDs.²⁴ Our results indicate that observation status has been used beyond a bridge from ED care to inpatient admission. In particular, observation status has expanded to include pediatric populations with more diverse clinical conditions (eg, appendicitis and diabetes mellitus), and has become a substantial component of postprocedural admissions (Appendix Figure). Looking forward, it is likely that the use of observation stays might surpass inpatient admissions for more conditions that primarily involve short-term stays.

Observation status originally was designed as a reimbursement strategy for patients who needed short stays in dedicated ED units or hospitals, but did not qualify for inpatient services.^5,25 After several changes in reimbursement policies, CMS released the “two midnight rule” for Medicare beneficiaries in 2013, which replaced condition-based criteria with time-based criteria to determine an inpatient or observation stay.¹ Some Medicaid programs and commercial payors have developed similar policies. Unlike the universal policy for Medicare populations, the regulations for pediatric populations vary by states and health insurers.^11-15,26-28 This might partially explain the wide variation observed among children’s hospital-level use of observation stays. For example, the California Medicaid program did not have a reimbursement rate for observation services as of 2020, while the Texas Medicaid program has had a policy for observation stays since 2010.^12,13 We found that two children’s hospitals in California had the lowest use of observation stays (almost zero), whereas the hospital-level use of observation stays was more than 50% for three out of four children’s hospitals in Texas. In addition to reimbursement policies, individual hospitals also might have different strategies for observation status designation. An earlier survey showed that there was lack of consistency in billing and payor-based designations of observation status at children’s hospitals.²⁹ These findings suggest that children’s hospital-level use of observation stays likely is influenced by reimbursement policy and practical strategy for observation status determination.

Earlier studies reported that observation status could be a more efficient use of healthcare resources.^5,6 However, there are still at least two concerns relevant to children’s hospitals during the last decade. The first is whether the use of observation stays can promote cost-saving or if it is just a cost-shifting strategy. An earlier study demonstrated that observation stays with prolonged LOS might increase risk of cost-sharing among adult patients.²⁹ Our study reveals an increasing trend of observation stays with prolonged LOS for pediatric patients. Similar to adult patients, LOS exceeding 24 or 48 hours could lead to uncovered healthcare costs and financial burdens on families.^30-32 Meanwhile, children’s hospitals also might take on a higher financial liability by implementing observation status. Earlier studies have indicated that resource use between observation and inpatient stays at children’s hospitals is similar, and increasing use of observation stays might lead to financial risk rather than cost effectiveness.³³ Further, administrative costs of observation determination are considerably high.³⁴ Medicaid is the major payor for pediatric patients in children’s hospitals. In this study, more than 50% of encounters were paid through Medicaid programs. It is well known that Medicaid reimbursement rates are lower than Medicare and commercial plans.³⁵ Therefore, the cost-saving conclusion drawn from Medicare patients cannot be generalized to pediatric populations at children’s hospitals without cautious reevaluation.

A second concern with increasing use of observation stays is selection bias in public reporting and comparisons of hospital performance. Presently, four main categories of quality indicators established by the Agency for Healthcare Research and Quality rely heavily on inpatient encounters.³⁶ In this study, we found that the range of hospital-level use of observation stays was large. In 2019, the risk-adjusted percent use of observation stays was less than 5% at three hospitals, while the percent use was greater than 60% in another three hospitals. Therefore, comparisons made without uniform accounting of observation stays might have significant implications for national rankings of children’s hospitals across the United States. These consequences have been investigated in several published studies.^22,23,37-39

There are several limitations to our study. First, the study sample was limited to children’s hospitals that consistently reported inpatient and observation data over the entire study period. Eighteen hospitals (86%) excluded from this study did not consistently submit inpatient and observation data to PHIS from 2010 through 2019. The primary purpose of this study was to present temporal trends of observation stays for children’s hospitals, and it was important to build the hospital cohort based on valid and consistent data during the study period. Appendix Table 4 presents differences of hospital characteristics by included and excluded groups of hospitals. Excluded hospitals might have fewer resources (eg, fewer pediatric intensive care beds). Nonetheless, the selection of hospitals was optimized based on data availability. Second, this study was a retrospective review of an administrative database of children’s hospitals and units. The sample does not represent all children’s hospitals or pediatric patients in the United States, but there are no available data sources—that we know of—that can generate national estimates for both inpatient and observation stays. Third, we did not attempt to conclusively infer any causal effects, and several factors could explain the increasing trends, such as reimbursement policies, hospital-level implementation strategies, determination guidelines for observation status designation, as well as changes in clinical care. Further studies should investigate impact of these factors on the use of observation stays for pediatric patients and children’s hospitals.

Observation status has been increasingly used for pediatric patients with more diverse clinical conditions, and there is a rising trend of prolonged LOS among observation stays since 2010. Considerable variation exists in hospital-level use of observation stays across children’s hospitals. Observation status could be an opportunity to improve efficiency of healthcare resource use or could lead to a financial risk for patients with prolonged LOS. Future studies should explore appropriateness of observation care in clinical practice through leveraging efficient care and alleviating financial risk.

Payors have been refining reimbursement policies for observation and inpatient stays over the past decade, and the effects on the healthcare payment system are significant.^1-4 Advocates claim that observation status could improve efficiency in the use of healthcare resources by reducing emergency department (ED) crowding and lowering costs for inpatient care.^5,6 Critics consider observation status to be a cost-shifting strategy that could lead to financial burdens for patients and hospitals.^7,8

Although reimbursement policies for observation stays traditionally have been set by the Centers for Medicare and Medicaid Services (CMS) in a uniform manner,^4,8 state Medicaid programs and commercial health insurers have developed a variety of policies for using observation status in broader populations and hospitals.^9-15 Coverage criteria and implementation timelines of these policies vary by states and commercial insurers.^11-15 For example, the California Department of Health Care Services did not have a specific reimbursement rate for observation stays in 2020, while some state Medicaid programs have had reimbursement policies for observation services in place since 2010.^11-15 These inconsistencies likely result in greater variation in use of observation stays across children’s hospitals than general hospitals.

Previous studies have shown rising trends in use of observation stays among adult patient populations and related implications for patients and general hospitals,^16-19 but few studies have reported the trends for pediatric populations. In this study, we sought to (1) describe recent trends of observation stays for pediatric populations at children’s hospitals from 2010 through 2019 and (2) investigate features of this shifting pattern for pediatric populations and hospital-level use of observation stays.

Study Design, Data, and Populations

We performed a retrospective analysis of the Pediatric Health Information System (PHIS), an administrative database that contains inpatient, observation, ambulatory, and ED encounter-level data from 50 not-for-profit, tertiary care children’s hospitals affiliated with the Children’s Hospital Association (CHA).²⁰ PHIS has an indicator to classify patient types (inpatient, observation, ED visits, ambulatory surgery, clinic visit, and others). The data are de-identified at the time of submission and subjected to validity and reliability checks by CHA and Truven Health Analytics (Ann Arbor, MI) before being included in PHIS. Each encounter in PHIS has only one patient type; therefore, encounters that transition to a higher level of care are assigned to their highest level of care (eg, a patient transitions from observation to inpatient status is classified as an inpatient encounter) to avoid duplicate counting.

To ensure consistent evaluations over time, we included 29 children’s hospitals that consistently reported both inpatient and observation data to PHIS across all quarters from 2010 through 2019. We identified the 20 most common clinical conditions using the All Patients Refined Diagnosis Related Groups (APR-DRGs; 3M Corporation) based upon their total frequencies of observation and inpatient stays over the study period. Regression analyses were conducted using all encounters within the 20 most common APR-DRGs.

Because all data have been de-identified in the PHIS database, the institutional review board at Ann and Robert H. Lurie Children’s Hospital of Chicago granted this study institutional review board–exempt status.

Main Outcome and Measures

We first presented longitudinal trends of observation stays for children’s hospitals using annual percentage of observation stays defined as:

To determine whether different pediatric populations have different trends of observation stays, we measured the growth rates of observation stays for each APR-DRG. Specifically, we first calculated the percentage of observation stays by APR-DRGs and years as described, and then calculated the growth rate of observation stays for each APR-DRG:

Next, we employed prolonged length of stay (LOS) and hospitalization resource-intensity scores for kids (H-RISK) to further investigate the shifting pattern of observation stays. Because most state Medicaid and commercial policies dictate that observation stays should not last longer than 48 hours, we defined prolonged LOS as >2 days.^11-15 We defined the annual percentage of observation stays with prolonged LOS for each year as:

Numerators and denominators of the three measures were obtained by pooling all children’s hospitals included in this study. H-RISK is a continuous variable developed by CHA to measure use of intensive care for children, which is comparable across various APR-DRGs.²¹ Changes in the empirical distribution of H-RISK from observation stays were presented over years using percentiles.

Other measures included sex, age, race, payor, and LOS. To investigate the use of observation stays among payors, we categorized payors into five groups: private, in-state Medicaid (managed care), in-state Medicaid (Children’s Health Insurance Program [CHIP]/others), other government, and all others, according to the data availability. The “private” group consisted of commercial preferred provider organizations, commercial health maintenance organizations, and commercial others. We combined both CHIP and in-state Medicaid (others), including Medicaid fee-for-service or unspecified Medicaid together as “in-state Medicaid (CHIP/others).” Detailed categorization information is summarized in Appendix Table 1. LOS was classified into four groups: 1 day (24 hours), 2 days (48 hours), 3 to 4 days, and >4 days.

Statistical Analysis

Descriptive statistics were stratified by inpatient and observation status and were summarized using frequency, percent, median, and interquartile range (IQR). Chi-square or Wilcoxon rank-sum tests were performed to examine differences between observation and inpatient status. Trends in annual percentage of observation stays and annual percentage of observation stays with prolonged LOS were estimated using first-order autoregressive models, in which year was considered a continuous variable. A nonparametric measure of rank correlation (Spearman’s rank correlation coefficient) was employed to evaluate the correlation between year and H-RISK from observation stays.

The risk-adjusted probability of being admitted as an observation stay was estimated using generalized linear mixed models by adjusting for year, age, sex, race, payor, LOS, H-RISK, and a random intercept for each hospital to control for patient clustering within a hospital (Appendix Model). Hospital-level use of observation stays was measured by risk-adjusted percent use of observation stays for each hospital using the predicted values from generalized linear mixed models. All analyses were performed using SAS software, version 9.4 (SAS Institute) and R (R Core Team, 2019), and P < .05 was considered statistically significant.

Increasing Trend of Observation Stays

Over the study period, there were 5,611,001 encounters, including 3,901,873 (69.5%) inpatient and 1,709,128 (30.5%) observation stays (Appendix Table 1). The number of observation stays increased from 117,246 in 2010 to 207,842 in 2019, and the number of inpatient stays slightly increased from 378,433 to 397,994 over the 10 years (Appendix Table 1). Because of different growth rates between observation and inpatient status, the annual percentage of observation stays increased from 23.7% in 2010 to 34.3% in 2019, while the annual percentage of inpatient stays decreased from 76.3% in 2010 to 65.7% in 2019 (Appendix Table 1; Figure 1, P < .001).

Different Growth Rates of Observation Stays for Various Pediatric Populations

As shown in the Table, growth rates of observation stays increased for 19 of the 20 most common APR-DRGs. The four APR-DRGs having the highest growth rates in observation stays were appendectomy, diabetes mellitus, kidney and urinary tract infections, and cellulitis and other bacterial skin infections (Appendix Figure). In particular, the annual percentage of observation stays for appendectomy increased from 19.8% in 2010 to 54.7% in 2019, with the number of observation stays growing from 2,321 to 7,876, while the number of inpatient stays decreased from 9,384 to 6,535 (Appendix Figure). The annual percentage of observation stays for diabetes mellitus increased from 8.16% in 2010 to 22.74% in 2019. Tonsil and adenoid procedures consistently held the largest numbers of observation stays across the 10 years among all the APR-DRGs, with 115,207 and 31,125 total observation and inpatient stays, respectively (Table).

Characteristics of Observation and Inpatient Stays

Patient characteristics are summarized in Appendix Table 1. There were 542,344 (32.9%) observation stays among patients with in-state Medicaid (managed care), and 241,157 (27.4%) observation stays among in-state Medicaid (CHIP/others). The percentages of observation and inpatient stays were 29.8% and 70.2% for private payor, as well as 29.6% and 70.4% for other government payor. Overall, the median (IQR) of H-RISK among observation stays was 0.79 (0.57-1.19) vs 1.23 (0.72-2.43) for inpatient stays. There were 1,410,694 (82.5%) observation stays discharged within 1 day and 243,972 (14.3%) observation stays discharged within 2 days. However, there were 47,413 (2.8%) and 7,049 (0.4%) observation stays with LOS 3 to 4 days or >4 days, respectively.

Shifting Pattern in Observation Stays

The annual percentage of observation stays with prolonged LOS (>2 days) rose from 1.1% in 2010 to 4.6% in 2019 (P < .001; Figure 2). The empirical distribution of H-RISK from observation stays by years further suggests a slightly increasing trend in intensity of care under observation stays. As shown in Appendix Table 2, although the 1st, 5th, 10th, 25th, and 99th percentiles of H-RISK were relatively stable, the 50th, 75th, 90th, and 95th percentiles of H-RISK were increasing over time. The correlation between year and intensity of care used under observation stays (H-RISK from observation stays) was found to be weak but significantly positive (Spearman correlation coefficients = 0.04; P < .001).

Interaction coefficients from our regression model demonstrate that the existing inverse association between H-RISK and odds of admission as an observation stay became less negative over the years. In 2010, the adjusted odds ratio (OR) of H-RISK was 0.57 (95% CI, 0.55-0.59). By 2017, the adjusted OR had increased to 0.65 (95% CI, 0.64-0.66). Compared with 2010, the seven adjusted ORs of H-RISK at years 2012 through 2018 were observed to be higher and statistically significant (P < .001, Appendix Table 3).

Hospitals-Level Use of Observation Stays

After adjusting for all covariates and hospital random effects, hospital-level use of observation stays increased between 2010 and 2019 for 26 out of 29 children’s hospitals. Although observation status essentially was not used at two children’s hospitals over the study period, the median hospital-level use of observation stays was 26% in 2010 (IQR, 3%-36%) and increased to 46% (IQR: 39%; 55%) in 2019. As shown in Figure 3, the number of hospitals with a low percentage of observation stays (<26%) decreased from 15 in 2010 to 4 in 2019. The number of hospitals with a high percentage of observation stays (≥51%) increased from 5 in 2010 to 10 in 2019. Nevertheless, there remained significant variation in the use of observation stays, and the hospital-level use ranged from 0% to 67% in 2019.

By 2020, observation status has become a key component of healthcare for pediatric patients, and its relevance for children’s hospitals recently has been described.^22,23 However, trends in observation stays for pediatric populations are not known. This represents the first study showing temporal trends of observation stays at children’s hospitals after 2010. Our results confirm that the increase in observation stays for pediatric populations is not attributable to decreasing patient acuity at children’s hospitals. We found a weak but significantly positive correlation between year and intensity of care used under observation stays. Although this correlation might not be clinically important, it demonstrates that patient acuity in observation stays is not decreasing. Regression results suggest that observation stays now encompass patients who need relatively higher intensity of care compared with those admitted under observation status in 2010.

This study also identifies a unique pattern in the use of observation stays among pediatric populations. Earlier studies exclusively focused on observation stays that were admitted from EDs.²⁴ Our results indicate that observation status has been used beyond a bridge from ED care to inpatient admission. In particular, observation status has expanded to include pediatric populations with more diverse clinical conditions (eg, appendicitis and diabetes mellitus), and has become a substantial component of postprocedural admissions (Appendix Figure). Looking forward, it is likely that the use of observation stays might surpass inpatient admissions for more conditions that primarily involve short-term stays.

Observation status originally was designed as a reimbursement strategy for patients who needed short stays in dedicated ED units or hospitals, but did not qualify for inpatient services.^5,25 After several changes in reimbursement policies, CMS released the “two midnight rule” for Medicare beneficiaries in 2013, which replaced condition-based criteria with time-based criteria to determine an inpatient or observation stay.¹ Some Medicaid programs and commercial payors have developed similar policies. Unlike the universal policy for Medicare populations, the regulations for pediatric populations vary by states and health insurers.^11-15,26-28 This might partially explain the wide variation observed among children’s hospital-level use of observation stays. For example, the California Medicaid program did not have a reimbursement rate for observation services as of 2020, while the Texas Medicaid program has had a policy for observation stays since 2010.^12,13 We found that two children’s hospitals in California had the lowest use of observation stays (almost zero), whereas the hospital-level use of observation stays was more than 50% for three out of four children’s hospitals in Texas. In addition to reimbursement policies, individual hospitals also might have different strategies for observation status designation. An earlier survey showed that there was lack of consistency in billing and payor-based designations of observation status at children’s hospitals.²⁹ These findings suggest that children’s hospital-level use of observation stays likely is influenced by reimbursement policy and practical strategy for observation status determination.

Earlier studies reported that observation status could be a more efficient use of healthcare resources.^5,6 However, there are still at least two concerns relevant to children’s hospitals during the last decade. The first is whether the use of observation stays can promote cost-saving or if it is just a cost-shifting strategy. An earlier study demonstrated that observation stays with prolonged LOS might increase risk of cost-sharing among adult patients.²⁹ Our study reveals an increasing trend of observation stays with prolonged LOS for pediatric patients. Similar to adult patients, LOS exceeding 24 or 48 hours could lead to uncovered healthcare costs and financial burdens on families.^30-32 Meanwhile, children’s hospitals also might take on a higher financial liability by implementing observation status. Earlier studies have indicated that resource use between observation and inpatient stays at children’s hospitals is similar, and increasing use of observation stays might lead to financial risk rather than cost effectiveness.³³ Further, administrative costs of observation determination are considerably high.³⁴ Medicaid is the major payor for pediatric patients in children’s hospitals. In this study, more than 50% of encounters were paid through Medicaid programs. It is well known that Medicaid reimbursement rates are lower than Medicare and commercial plans.³⁵ Therefore, the cost-saving conclusion drawn from Medicare patients cannot be generalized to pediatric populations at children’s hospitals without cautious reevaluation.

A second concern with increasing use of observation stays is selection bias in public reporting and comparisons of hospital performance. Presently, four main categories of quality indicators established by the Agency for Healthcare Research and Quality rely heavily on inpatient encounters.³⁶ In this study, we found that the range of hospital-level use of observation stays was large. In 2019, the risk-adjusted percent use of observation stays was less than 5% at three hospitals, while the percent use was greater than 60% in another three hospitals. Therefore, comparisons made without uniform accounting of observation stays might have significant implications for national rankings of children’s hospitals across the United States. These consequences have been investigated in several published studies.^22,23,37-39

There are several limitations to our study. First, the study sample was limited to children’s hospitals that consistently reported inpatient and observation data over the entire study period. Eighteen hospitals (86%) excluded from this study did not consistently submit inpatient and observation data to PHIS from 2010 through 2019. The primary purpose of this study was to present temporal trends of observation stays for children’s hospitals, and it was important to build the hospital cohort based on valid and consistent data during the study period. Appendix Table 4 presents differences of hospital characteristics by included and excluded groups of hospitals. Excluded hospitals might have fewer resources (eg, fewer pediatric intensive care beds). Nonetheless, the selection of hospitals was optimized based on data availability. Second, this study was a retrospective review of an administrative database of children’s hospitals and units. The sample does not represent all children’s hospitals or pediatric patients in the United States, but there are no available data sources—that we know of—that can generate national estimates for both inpatient and observation stays. Third, we did not attempt to conclusively infer any causal effects, and several factors could explain the increasing trends, such as reimbursement policies, hospital-level implementation strategies, determination guidelines for observation status designation, as well as changes in clinical care. Further studies should investigate impact of these factors on the use of observation stays for pediatric patients and children’s hospitals.

Observation status has been increasingly used for pediatric patients with more diverse clinical conditions, and there is a rising trend of prolonged LOS among observation stays since 2010. Considerable variation exists in hospital-level use of observation stays across children’s hospitals. Observation status could be an opportunity to improve efficiency of healthcare resource use or could lead to a financial risk for patients with prolonged LOS. Future studies should explore appropriateness of observation care in clinical practice through leveraging efficient care and alleviating financial risk.

Sliding scale insulin (SSI) for inpatient glycemic control was first proposed by Elliott P Joslin in 1934 when he recommended titration of insulin based on urine glucose levels.¹ As bedside glucose meters became widely available, physicians transitioned to dosing SSI based on capillary blood glucose (BG) levels,^2,3 and SSI became widely used for the management of inpatient hyperglycemia.¹ However, during the past decade, there has been strong opposition to the use of SSI in hospitals. Many authors oppose its use, highlighting the retrospective rather than prospective nature of SSI therapy and concerns about inadequate glycemic control.^4-6 In 2004, the American College of Endocrinology first released a position statement discouraging the use of SSI alone and recommended basal-bolus insulin as the preferred method of glycemic control for inpatients with type 2 diabetes (T2D).⁷ The American Diabetes Association (ADA) inpatient guidelines in 2005⁸ and the Endocrine Society guidelines in 2012⁹ also opposed SSI monotherapy and reaffirmed that a basal-bolus insulin regimen should be used for most non–critically ill patients with diabetes. Those guidelines remain in place currently.

Several randomized controlled trials (RCTs) and meta-analyses have shown that basal-bolus insulin regimens provide superior glycemic control in non–critical inpatients when compared with SSI alone.^10-14 In addition, the RABBIT 2 (Randomized Study of Basal-Bolus Insulin Therapy in the Inpatient Management of Patients With Type 2 Diabetes) trial showed a significant reduction in perioperative complications¹⁰ among surgical patients when treated with basal-bolus insulin therapy. Despite these studies and strong recommendations against its use, SSI continues to be widely used in the United States. According to a 2007 survey of 44 US hospitals, 41% of noncritical patients with hyperglycemia were treated with SSI alone.¹⁵ In addition, SSI remains one of the most commonly prescribed insulin regimens in many countries around the world.^16-19 The persistence of SSI use raises questions as to why clinicians continue to use a therapy that has been strongly criticized. Some authors point to convenience and fear of hypoglycemia with a basal-bolus insulin regimen.^20,21 Alternatively, it is possible that SSI usage remains so pervasive because it is effective in a subset of patients. In fact, a 2018 Cochrane review concluded that existing evidence is not sufficiently robust to definitively recommend basal-bolus insulin over SSI for inpatient diabetes management of non–critically ill patients despite existing guidelines.²²

Owing to the ongoing controversy and widespread use of SSI, we designed an exploratory analysis to understand the rationale for such therapy by investigating whether a certain subpopulation of hospitalized patients with T2D may achieve target glycemic control with SSI alone. We hypothesized that noncritical patients with mild hyperglycemia and admission BG <180 mg/dL would do well with SSI alone and may not require intensive treatment with basal-bolus insulin regimens. To address this question, we used electronic health records with individual-level patient data to assess inpatient glycemic control of non–critically ill patients with T2D treated with SSI alone.

Participants

Data from 25,813 adult noncritical inpatients with T2D, with an index admission between June 1, 2010, and June 30, 2018, were obtained through the Emory Healthcare Clinical Data Warehouse infrastructure program. All patients were admitted to Emory Healthcare hospitals, including Emory University Hospital, Emory University Hospital Midtown, and Emory Saint Joseph’s Hospital, in Atlanta, Georgia. Data were extracted for each patient during the index hospitalization, including demographics, anthropometrics, and admission and inpatient laboratory values. Information was collected on daily point-of-care glucose values, hemoglobin A1c (HbA1c), hypoglycemic events, insulin doses, hospital complications, comorbidities, and hospital setting (medical vs surgical admission). International Classification of Diseases, 9th and 10th Revisions (ICD-9/10) codes were used to determine diagnosis of T2D, comorbidities, and complications.

From our initial dataset, we identified 16,366 patients who were treated with SSI during hospitalization. We excluded patients who were admitted to the intensive care unit (ICU) or placed on intravenous insulin, patients with missing admission BG values, and patients with a length of stay less than 1 day. To prevent inclusion of patients presenting in diabetic ketoacidosis or hyperosmolar hyperglycemic syndrome, we excluded patients with an admission BG >500 mg/dL. We then excluded 6,739 patients who received basal insulin within the first 2 days of hospitalization, as well as 943 patients who were treated with noninsulin (oral or injectable) antidiabetic agents. Our final dataset included 8,095 patients (Appendix Figure).

Patients in the SSI cohort included all patients who were treated with short-acting insulin only (regular insulin or rapid-acting [lispro, aspart, glulisine] insulin analogs) during the first 2 days of hospitalization. Patients who remained on only short-acting insulin during the entire hospitalization were defined as continuous SSI patients. Patients who subsequently received basal insulin after day 2 of hospitalization were defined as patients who transitioned to basal. Patients were stratified according to admission BG levels (first BG available on day of admission) and HbA1c (when available during index admission). We compared the baseline characteristics and clinical outcomes of patients who remained on SSI alone throughout the entirety of hospitalization with those of patients who required transition to basal insulin. The mean hospital BG was calculated by taking the average of all BG measurements during the hospital stay. We defined hypoglycemia as a BG <70 mg/dL and severe hypoglycemia as BG <40 mg/dL. Repeated hypoglycemia values were excluded if they occurred within a period of 2 hours.

Outcome Measures

The primary outcome was the percentage of patients with T2D achieving target glycemic control with SSI therapy, defined as mean hospital BG between 70 and 180 mg/dL without hypoglycemia <70 mg/dL during hospital stay. This threshold was determined based on 2019 ADA recommendations targeting hospital BG <180 mg/dL and avoidance of hypoglycemia.²³

Statistical Analysis

Patients were stratified according to continuous SSI versus transitioned to basal treatment. Patients who remained on continuous SSI were further categorized into four categories based on admission BG: <140 mg/dL, 140 to 180 mg/dL, 180 to 250 mg/dL, and ≥250 mg/dL. Clinical characteristics were compared using Wilcoxon rank-sum tests (if continuous) and chi-square tests or Fisher exact tests (if categorical). We then compared the clinical outcomes among continuous SSI patients with different admission BG levels (<140 mg/dL, 140-180 mg/dL, 180-250 mg/dL, and ≥250 mg/dL) and with different HbA1c levels (<7%, 7%-8%, 8%-9%, ≥9%). Within each scenario, logistic regression for the outcome of poor glycemic control, defined as mean hospital BG >180 mg/dL, was performed to evaluate the HbA1c levels and admission BG levels controlling for other factors (age, gender, body mass index [BMI], race, setting [medicine versus surgery] and Charlson Comorbidity Index score). A P value < .05 was regarded as statistically significant. All analyses were performed based on available cases and conducted in SAS version 9.4 (SAS Institute Inc.).

Among 25,813 adult patients with T2D, 8,095 patients (31.4%) were treated with SSI alone during the first 2 days of hospitalization. Of those patients treated with SSI, 6,903 (85%) remained on continuous SSI alone during the entire hospitalization, and 1,192 (15%) were transitioned to basal insulin. The clinical characteristics of these patients on continuous SSI and those who transitioned to basal insulin are shown in Table 1. Patients who transitioned to basal insulin had significantly higher mean (SD) admission BG (191.8 [88.2] mg/dL vs 156.4 [65.4] mg/dL, P < .001) and higher mean (SD) HbA1c (8.1% [2.0%] vs 7.01% [1.5%], P < .001), compared with those who remained on continuous SSI. Patients who transitioned to basal insulin were also younger and more likely to have chronic kidney disease (CKD), but less likely to have congestive heart failure, coronary artery disease, or chronic obstructive pulmonary disease (COPD). The Charlson Comorbidity Index score was significantly higher for patients who transitioned to basal (4.4 [2.5]) than for those who remained on continuous SSI (4.1 [2.5], P < .001). There were no significant differences among sex, BMI, or glomerular filtration rate (GFR) on admission. Of those transitioned to basal insulin, 53% achieved a mean hospitalization BG <180 mg/dL, compared with 82% of those on continuous SSI. The overall rate of hypoglycemia in the continuous SSI group was 8% compared with 18% in those transitioned to basal insulin.

Of the patients who remained on continuous SSI throughout the hospitalization, 3,319 patients (48%) had admission BG <140 mg/dL, 1,671 patients (24%) had admission BG 140 to 180 mg/dL, and 1,913 patients (28%) had admission BG >180 mg/dL. Only 9% of patients who remained on continuous SSI had admission BG ≥250 mg/dL. Patients with admission BG <140 mg/dL were older, had lower BMI and HbA1c, had higher rates of COPD and CKD, and were more likely to be admitted to a surgical service compared with patients with admission BG >140 mg/dL (P < .05 for all; Table 2).

Hospital glycemic control for patients on continuous SSI according to admission BG is displayed in Table 3. Among patients who remained on continuous SSI, 96% of patients with admission BG <140 mg/dL had a mean hospital BG <180 mg/dL; of them, 86% achieved target control without hypoglycemia. Similar rates of target control were achieved in patients with admission BG 140 to 180 mg/dL (83%), in contrast to patients with admission BG ≥250 mg/dL, of whom only 18% achieved target control (P < .001). These findings parallel those seen in patients transitioned to basal insulin. Of patients in the transition group admitted with BG <140 mg/dL and <180 mg/dL, 88.5% and 84.6% had mean hospital BG <180 mg/dL, respectively, while 69.1% and 68.9% had mean BG between 70 and 180 mg/dL without hypoglycemia. The overall frequency of hypoglycemia <70 mg/dL among patients on continuous SSI was 8% and was more common in patients with admission BG <140 mg/dL (10%) compared with patients with higher admission glucose levels (BG 140-180 mg/dL [4%], 180-250 mg/dL [4%], or ≥250 mg/dL [6%], P < .001). There was no difference in rates of severe hypoglycemia <40 mg/dL among groups.

HbA1c data were available for 2,560 of the patients on continuous SSI (Table 3). Mean hospital BG increased significantly with increasing HbA1c values. Patients admitted with HbA1c <7% had lower mean (SD) hospital BG (132.2 [28.2] mg/dL) and were more likely to achieve target glucose control during hospitalization (85%) compared with those with HbA1c 7% to 8% (mean BG, 148.7 [30.8] mg/dL; 80% target control), HbA1c 8% to 9% (mean BG, 169.1 [37.9] mg/dL; 61% target control), or HbA1c ≥9% (mean BG, 194.9 [53.4] mg/dL; 38% target control) (P < .001).

In a logistic regression analysis adjusted for age, gender, BMI, race, setting (medicine vs surgery), and Charlson Comorbidity Index score, the odds of poor glycemic control increased with higher admission BG (admission BG 140-180 mg/dL: odds ratio [OR], 1.8; 95% CI, 1.5-2.2; admission BG 180-250 mg/dL: OR, 3.7; 95% CI, 3.1-4.4; admission BG ≥250 mg/dL: OR, 7.2; 95% CI, 5.8-9.0; reference admission BG <140 mg/dL; Figure). Similarly, the logistic regression analysis showed greater odds of poor in-hospital glycemic control with increasing HbA1c (OR, 6.1; 95% CI, 4.3-8.8 for HbA1c >9% compared with HbA1c <7%).

This large retrospective cohort study examined the effectiveness of SSI for glycemic control in noncritical inpatients with T2D. Our results indicate that SSI is still widely used in our hospital system, with 31.4% of our initial cohort managed with SSI alone. We found that 86% of patients with BG <140 mg/dL and 83% of patients with BG 140 to 180 mg/dL achieved glycemic control without hypoglycemia when managed with SSI alone, compared with 53% of those admitted with BG 180 to 250 mg/dL and only 18% of those with admission BG ≥250 mg/dL. This high success rate of achieving optimal BG control with SSI alone is comparable to that seen with transition to basal insulin and may explain the prevalent use of SSI for the management of patients with T2D and mild to moderate hyperglycemia.

Published clinical guideline recommendations promoting the use of basal-bolus insulin treatment algorithms are based on the results of a few RCTs that compared the efficacy of SSI vs a basal-bolus insulin regimen. These studies reported significantly lower mean daily BG concentration with basal or basal-bolus insulin therapy compared with SSI.^10,11,24 However, it is interesting to note that the mean admission BG of patients treated with SSI in these RCTs ranged from 184 to 225 mg/dL. Patients in these trials were excluded if admission BG was <140 mg/dL.^10,11,24 This is in contrast to our study evaluating real-world data in non–critically ill settings in which we found that 48% of patients treated with SSI had admission BG <140 mg/dL, and nearly 75% had admission BG <180 mg/dL. This suggests that by nature of study design, most RCTs excluded the population of patients who do achieve good glycemic control with SSI and may have contributed to the perception that basal insulin is preferable in all populations.

Our analysis indicates that healthcare professionals should consider admission BG when selecting the type of insulin regimen to manage patients with T2D in the hospital. Our results suggest that SSI may be appropriate for many patients with admission BG <180 mg/dL and should be avoided as monotherapy in patients with admission BG ≥180 mg/dL, as the proportion of patients achieving target control decreased with increasing admission BG. More importantly, if a patient is not controlled with SSI alone, intensification of therapy with the addition of basal insulin is indicated to achieve glycemic control. In addition, we found that the admission HbA1c is an appropriate marker to consider as well, with hospital glycemic control deteriorating with increasing HbA1c values, paralleling the admission BG. The main limitation to widespread use of HbA1c for therapeutic decision-making is access to values at time of patient admission; in our population, only 37% of patients had an HbA1c value available during the index hospitalization.

Previous publications have reported that hypoglycemia carries significant safety concerns, especially among a hospitalized population.^25-27 As such, we included hypoglycemia as an important metric in our definition of target glycemic control rather than simply using mean hospital BG or number of hyperglycemic events to define treatment effectiveness. We did find a higher rate of hypoglycemia in patients with moderate admission BG treated with SSI compared with those with higher admission BG; however, few patients overall experienced clinically significant (<54 mg/dL) or severe (<40 mg/dL) hypoglycemia.

In our population, only 15% of patients started on SSI received additional basal insulin during hospitalization. This finding is similar to data reported in the Rabbit 2 trial, in which 14% of patients failed SSI alone, with a higher failure rate among those with higher BG on admission.¹⁰ Given the observational nature of this study, we cannot definitively state why certain patients in our population required additional basal insulin, but we can hypothesize that these patients admitted with BG ≥180 mg/dL had higher treatment failure rates and greater rates of hyperglycemia, therefore receiving intensified insulin therapy as clinically indicated at the discretion of the treating physician. Patients who transitioned from SSI to basal insulin had significantly higher admission BG and HbA1c compared with patients who remained on SSI alone. We noted that the rates of hypoglycemia were higher in the group that transitioned to basal (18% vs 8%) and similar to rates reported in previous RCTs.^11,24

This observational study takes advantage of a large, diverse study population and a combination of medicine and surgery patients in a real-world setting. We acknowledge several limitations in our study. Our primary data were observational in nature, and as such, some baseline patient characteristics were notably different between groups, suggesting selection bias for treatment allocation to SSI. We do not know which patients were managed by primary teams compared with specialized diabetes consult services, which may also influence treatment regimens. We did not have access to information about patients’ at-home diabetes medication regimens or duration of diabetes, both of which have been shown in prior publications to affect an individual’s overall hospital glycemic control. Data on HbA1c values were available for only approximately one-third of patients. In addition, our study did not include patients without a history of diabetes who developed stress-induced hyperglycemia, a population that may benefit from conservative therapy such as SSI.²⁸ A diagnosis of CKD was defined based on ICD 9/10 codes and not on admission estimated GFR. More specific data regarding stage of CKD or changes in renal function over the duration of hospitalization are not available, which could influence insulin prescribing practice. In addition, we defined the basal group as patients prescribed any form of basal insulin (NPH, glargine, detemir or degludec), and we do not have information on the use of prandial versus correction doses of rapid-acting insulin in the basal insulin–treated group.

In conclusion, our observational study indicates that the use of SSI results in appropriate target glycemic control for most noncritical medicine and surgery patients with admission BG <180 mg/dL. In agreement with previous RCTs, our study confirms that SSI as monotherapy is frequently inadequate in patients with significant hyperglycemia >180 mg/dL.^10,11,24,29 We propose that an individualized approach to inpatient glycemic management is imperative, and cautious use of SSI may be a viable option for certain patients with mild hyperglycemia and admission BG <180 mg/dL. Further observational and randomized studies are needed to confirm the efficacy of SSI therapy in T2D patients with mild hyperglycemia. By identifying which subset of patients can be safely managed with SSI alone, we can better understand which patients will require escalation of therapy with intensive glucose management.

Sliding scale insulin (SSI) for inpatient glycemic control was first proposed by Elliott P Joslin in 1934 when he recommended titration of insulin based on urine glucose levels.¹ As bedside glucose meters became widely available, physicians transitioned to dosing SSI based on capillary blood glucose (BG) levels,^2,3 and SSI became widely used for the management of inpatient hyperglycemia.¹ However, during the past decade, there has been strong opposition to the use of SSI in hospitals. Many authors oppose its use, highlighting the retrospective rather than prospective nature of SSI therapy and concerns about inadequate glycemic control.^4-6 In 2004, the American College of Endocrinology first released a position statement discouraging the use of SSI alone and recommended basal-bolus insulin as the preferred method of glycemic control for inpatients with type 2 diabetes (T2D).⁷ The American Diabetes Association (ADA) inpatient guidelines in 2005⁸ and the Endocrine Society guidelines in 2012⁹ also opposed SSI monotherapy and reaffirmed that a basal-bolus insulin regimen should be used for most non–critically ill patients with diabetes. Those guidelines remain in place currently.

Several randomized controlled trials (RCTs) and meta-analyses have shown that basal-bolus insulin regimens provide superior glycemic control in non–critical inpatients when compared with SSI alone.^10-14 In addition, the RABBIT 2 (Randomized Study of Basal-Bolus Insulin Therapy in the Inpatient Management of Patients With Type 2 Diabetes) trial showed a significant reduction in perioperative complications¹⁰ among surgical patients when treated with basal-bolus insulin therapy. Despite these studies and strong recommendations against its use, SSI continues to be widely used in the United States. According to a 2007 survey of 44 US hospitals, 41% of noncritical patients with hyperglycemia were treated with SSI alone.¹⁵ In addition, SSI remains one of the most commonly prescribed insulin regimens in many countries around the world.^16-19 The persistence of SSI use raises questions as to why clinicians continue to use a therapy that has been strongly criticized. Some authors point to convenience and fear of hypoglycemia with a basal-bolus insulin regimen.^20,21 Alternatively, it is possible that SSI usage remains so pervasive because it is effective in a subset of patients. In fact, a 2018 Cochrane review concluded that existing evidence is not sufficiently robust to definitively recommend basal-bolus insulin over SSI for inpatient diabetes management of non–critically ill patients despite existing guidelines.²²

Owing to the ongoing controversy and widespread use of SSI, we designed an exploratory analysis to understand the rationale for such therapy by investigating whether a certain subpopulation of hospitalized patients with T2D may achieve target glycemic control with SSI alone. We hypothesized that noncritical patients with mild hyperglycemia and admission BG <180 mg/dL would do well with SSI alone and may not require intensive treatment with basal-bolus insulin regimens. To address this question, we used electronic health records with individual-level patient data to assess inpatient glycemic control of non–critically ill patients with T2D treated with SSI alone.

Participants

Data from 25,813 adult noncritical inpatients with T2D, with an index admission between June 1, 2010, and June 30, 2018, were obtained through the Emory Healthcare Clinical Data Warehouse infrastructure program. All patients were admitted to Emory Healthcare hospitals, including Emory University Hospital, Emory University Hospital Midtown, and Emory Saint Joseph’s Hospital, in Atlanta, Georgia. Data were extracted for each patient during the index hospitalization, including demographics, anthropometrics, and admission and inpatient laboratory values. Information was collected on daily point-of-care glucose values, hemoglobin A1c (HbA1c), hypoglycemic events, insulin doses, hospital complications, comorbidities, and hospital setting (medical vs surgical admission). International Classification of Diseases, 9th and 10th Revisions (ICD-9/10) codes were used to determine diagnosis of T2D, comorbidities, and complications.

From our initial dataset, we identified 16,366 patients who were treated with SSI during hospitalization. We excluded patients who were admitted to the intensive care unit (ICU) or placed on intravenous insulin, patients with missing admission BG values, and patients with a length of stay less than 1 day. To prevent inclusion of patients presenting in diabetic ketoacidosis or hyperosmolar hyperglycemic syndrome, we excluded patients with an admission BG >500 mg/dL. We then excluded 6,739 patients who received basal insulin within the first 2 days of hospitalization, as well as 943 patients who were treated with noninsulin (oral or injectable) antidiabetic agents. Our final dataset included 8,095 patients (Appendix Figure).

Patients in the SSI cohort included all patients who were treated with short-acting insulin only (regular insulin or rapid-acting [lispro, aspart, glulisine] insulin analogs) during the first 2 days of hospitalization. Patients who remained on only short-acting insulin during the entire hospitalization were defined as continuous SSI patients. Patients who subsequently received basal insulin after day 2 of hospitalization were defined as patients who transitioned to basal. Patients were stratified according to admission BG levels (first BG available on day of admission) and HbA1c (when available during index admission). We compared the baseline characteristics and clinical outcomes of patients who remained on SSI alone throughout the entirety of hospitalization with those of patients who required transition to basal insulin. The mean hospital BG was calculated by taking the average of all BG measurements during the hospital stay. We defined hypoglycemia as a BG <70 mg/dL and severe hypoglycemia as BG <40 mg/dL. Repeated hypoglycemia values were excluded if they occurred within a period of 2 hours.

Outcome Measures

The primary outcome was the percentage of patients with T2D achieving target glycemic control with SSI therapy, defined as mean hospital BG between 70 and 180 mg/dL without hypoglycemia <70 mg/dL during hospital stay. This threshold was determined based on 2019 ADA recommendations targeting hospital BG <180 mg/dL and avoidance of hypoglycemia.²³

Statistical Analysis

Patients were stratified according to continuous SSI versus transitioned to basal treatment. Patients who remained on continuous SSI were further categorized into four categories based on admission BG: <140 mg/dL, 140 to 180 mg/dL, 180 to 250 mg/dL, and ≥250 mg/dL. Clinical characteristics were compared using Wilcoxon rank-sum tests (if continuous) and chi-square tests or Fisher exact tests (if categorical). We then compared the clinical outcomes among continuous SSI patients with different admission BG levels (<140 mg/dL, 140-180 mg/dL, 180-250 mg/dL, and ≥250 mg/dL) and with different HbA1c levels (<7%, 7%-8%, 8%-9%, ≥9%). Within each scenario, logistic regression for the outcome of poor glycemic control, defined as mean hospital BG >180 mg/dL, was performed to evaluate the HbA1c levels and admission BG levels controlling for other factors (age, gender, body mass index [BMI], race, setting [medicine versus surgery] and Charlson Comorbidity Index score). A P value < .05 was regarded as statistically significant. All analyses were performed based on available cases and conducted in SAS version 9.4 (SAS Institute Inc.).

Among 25,813 adult patients with T2D, 8,095 patients (31.4%) were treated with SSI alone during the first 2 days of hospitalization. Of those patients treated with SSI, 6,903 (85%) remained on continuous SSI alone during the entire hospitalization, and 1,192 (15%) were transitioned to basal insulin. The clinical characteristics of these patients on continuous SSI and those who transitioned to basal insulin are shown in Table 1. Patients who transitioned to basal insulin had significantly higher mean (SD) admission BG (191.8 [88.2] mg/dL vs 156.4 [65.4] mg/dL, P < .001) and higher mean (SD) HbA1c (8.1% [2.0%] vs 7.01% [1.5%], P < .001), compared with those who remained on continuous SSI. Patients who transitioned to basal insulin were also younger and more likely to have chronic kidney disease (CKD), but less likely to have congestive heart failure, coronary artery disease, or chronic obstructive pulmonary disease (COPD). The Charlson Comorbidity Index score was significantly higher for patients who transitioned to basal (4.4 [2.5]) than for those who remained on continuous SSI (4.1 [2.5], P < .001). There were no significant differences among sex, BMI, or glomerular filtration rate (GFR) on admission. Of those transitioned to basal insulin, 53% achieved a mean hospitalization BG <180 mg/dL, compared with 82% of those on continuous SSI. The overall rate of hypoglycemia in the continuous SSI group was 8% compared with 18% in those transitioned to basal insulin.

Of the patients who remained on continuous SSI throughout the hospitalization, 3,319 patients (48%) had admission BG <140 mg/dL, 1,671 patients (24%) had admission BG 140 to 180 mg/dL, and 1,913 patients (28%) had admission BG >180 mg/dL. Only 9% of patients who remained on continuous SSI had admission BG ≥250 mg/dL. Patients with admission BG <140 mg/dL were older, had lower BMI and HbA1c, had higher rates of COPD and CKD, and were more likely to be admitted to a surgical service compared with patients with admission BG >140 mg/dL (P < .05 for all; Table 2).

Hospital glycemic control for patients on continuous SSI according to admission BG is displayed in Table 3. Among patients who remained on continuous SSI, 96% of patients with admission BG <140 mg/dL had a mean hospital BG <180 mg/dL; of them, 86% achieved target control without hypoglycemia. Similar rates of target control were achieved in patients with admission BG 140 to 180 mg/dL (83%), in contrast to patients with admission BG ≥250 mg/dL, of whom only 18% achieved target control (P < .001). These findings parallel those seen in patients transitioned to basal insulin. Of patients in the transition group admitted with BG <140 mg/dL and <180 mg/dL, 88.5% and 84.6% had mean hospital BG <180 mg/dL, respectively, while 69.1% and 68.9% had mean BG between 70 and 180 mg/dL without hypoglycemia. The overall frequency of hypoglycemia <70 mg/dL among patients on continuous SSI was 8% and was more common in patients with admission BG <140 mg/dL (10%) compared with patients with higher admission glucose levels (BG 140-180 mg/dL [4%], 180-250 mg/dL [4%], or ≥250 mg/dL [6%], P < .001). There was no difference in rates of severe hypoglycemia <40 mg/dL among groups.

HbA1c data were available for 2,560 of the patients on continuous SSI (Table 3). Mean hospital BG increased significantly with increasing HbA1c values. Patients admitted with HbA1c <7% had lower mean (SD) hospital BG (132.2 [28.2] mg/dL) and were more likely to achieve target glucose control during hospitalization (85%) compared with those with HbA1c 7% to 8% (mean BG, 148.7 [30.8] mg/dL; 80% target control), HbA1c 8% to 9% (mean BG, 169.1 [37.9] mg/dL; 61% target control), or HbA1c ≥9% (mean BG, 194.9 [53.4] mg/dL; 38% target control) (P < .001).

In a logistic regression analysis adjusted for age, gender, BMI, race, setting (medicine vs surgery), and Charlson Comorbidity Index score, the odds of poor glycemic control increased with higher admission BG (admission BG 140-180 mg/dL: odds ratio [OR], 1.8; 95% CI, 1.5-2.2; admission BG 180-250 mg/dL: OR, 3.7; 95% CI, 3.1-4.4; admission BG ≥250 mg/dL: OR, 7.2; 95% CI, 5.8-9.0; reference admission BG <140 mg/dL; Figure). Similarly, the logistic regression analysis showed greater odds of poor in-hospital glycemic control with increasing HbA1c (OR, 6.1; 95% CI, 4.3-8.8 for HbA1c >9% compared with HbA1c <7%).

This large retrospective cohort study examined the effectiveness of SSI for glycemic control in noncritical inpatients with T2D. Our results indicate that SSI is still widely used in our hospital system, with 31.4% of our initial cohort managed with SSI alone. We found that 86% of patients with BG <140 mg/dL and 83% of patients with BG 140 to 180 mg/dL achieved glycemic control without hypoglycemia when managed with SSI alone, compared with 53% of those admitted with BG 180 to 250 mg/dL and only 18% of those with admission BG ≥250 mg/dL. This high success rate of achieving optimal BG control with SSI alone is comparable to that seen with transition to basal insulin and may explain the prevalent use of SSI for the management of patients with T2D and mild to moderate hyperglycemia.

Published clinical guideline recommendations promoting the use of basal-bolus insulin treatment algorithms are based on the results of a few RCTs that compared the efficacy of SSI vs a basal-bolus insulin regimen. These studies reported significantly lower mean daily BG concentration with basal or basal-bolus insulin therapy compared with SSI.^10,11,24 However, it is interesting to note that the mean admission BG of patients treated with SSI in these RCTs ranged from 184 to 225 mg/dL. Patients in these trials were excluded if admission BG was <140 mg/dL.^10,11,24 This is in contrast to our study evaluating real-world data in non–critically ill settings in which we found that 48% of patients treated with SSI had admission BG <140 mg/dL, and nearly 75% had admission BG <180 mg/dL. This suggests that by nature of study design, most RCTs excluded the population of patients who do achieve good glycemic control with SSI and may have contributed to the perception that basal insulin is preferable in all populations.

Our analysis indicates that healthcare professionals should consider admission BG when selecting the type of insulin regimen to manage patients with T2D in the hospital. Our results suggest that SSI may be appropriate for many patients with admission BG <180 mg/dL and should be avoided as monotherapy in patients with admission BG ≥180 mg/dL, as the proportion of patients achieving target control decreased with increasing admission BG. More importantly, if a patient is not controlled with SSI alone, intensification of therapy with the addition of basal insulin is indicated to achieve glycemic control. In addition, we found that the admission HbA1c is an appropriate marker to consider as well, with hospital glycemic control deteriorating with increasing HbA1c values, paralleling the admission BG. The main limitation to widespread use of HbA1c for therapeutic decision-making is access to values at time of patient admission; in our population, only 37% of patients had an HbA1c value available during the index hospitalization.

Previous publications have reported that hypoglycemia carries significant safety concerns, especially among a hospitalized population.^25-27 As such, we included hypoglycemia as an important metric in our definition of target glycemic control rather than simply using mean hospital BG or number of hyperglycemic events to define treatment effectiveness. We did find a higher rate of hypoglycemia in patients with moderate admission BG treated with SSI compared with those with higher admission BG; however, few patients overall experienced clinically significant (<54 mg/dL) or severe (<40 mg/dL) hypoglycemia.

In our population, only 15% of patients started on SSI received additional basal insulin during hospitalization. This finding is similar to data reported in the Rabbit 2 trial, in which 14% of patients failed SSI alone, with a higher failure rate among those with higher BG on admission.¹⁰ Given the observational nature of this study, we cannot definitively state why certain patients in our population required additional basal insulin, but we can hypothesize that these patients admitted with BG ≥180 mg/dL had higher treatment failure rates and greater rates of hyperglycemia, therefore receiving intensified insulin therapy as clinically indicated at the discretion of the treating physician. Patients who transitioned from SSI to basal insulin had significantly higher admission BG and HbA1c compared with patients who remained on SSI alone. We noted that the rates of hypoglycemia were higher in the group that transitioned to basal (18% vs 8%) and similar to rates reported in previous RCTs.^11,24

This observational study takes advantage of a large, diverse study population and a combination of medicine and surgery patients in a real-world setting. We acknowledge several limitations in our study. Our primary data were observational in nature, and as such, some baseline patient characteristics were notably different between groups, suggesting selection bias for treatment allocation to SSI. We do not know which patients were managed by primary teams compared with specialized diabetes consult services, which may also influence treatment regimens. We did not have access to information about patients’ at-home diabetes medication regimens or duration of diabetes, both of which have been shown in prior publications to affect an individual’s overall hospital glycemic control. Data on HbA1c values were available for only approximately one-third of patients. In addition, our study did not include patients without a history of diabetes who developed stress-induced hyperglycemia, a population that may benefit from conservative therapy such as SSI.²⁸ A diagnosis of CKD was defined based on ICD 9/10 codes and not on admission estimated GFR. More specific data regarding stage of CKD or changes in renal function over the duration of hospitalization are not available, which could influence insulin prescribing practice. In addition, we defined the basal group as patients prescribed any form of basal insulin (NPH, glargine, detemir or degludec), and we do not have information on the use of prandial versus correction doses of rapid-acting insulin in the basal insulin–treated group.

In conclusion, our observational study indicates that the use of SSI results in appropriate target glycemic control for most noncritical medicine and surgery patients with admission BG <180 mg/dL. In agreement with previous RCTs, our study confirms that SSI as monotherapy is frequently inadequate in patients with significant hyperglycemia >180 mg/dL.^10,11,24,29 We propose that an individualized approach to inpatient glycemic management is imperative, and cautious use of SSI may be a viable option for certain patients with mild hyperglycemia and admission BG <180 mg/dL. Further observational and randomized studies are needed to confirm the efficacy of SSI therapy in T2D patients with mild hyperglycemia. By identifying which subset of patients can be safely managed with SSI alone, we can better understand which patients will require escalation of therapy with intensive glucose management.

Sliding scale insulin (SSI) for inpatient glycemic control was first proposed by Elliott P Joslin in 1934 when he recommended titration of insulin based on urine glucose levels.¹ As bedside glucose meters became widely available, physicians transitioned to dosing SSI based on capillary blood glucose (BG) levels,^2,3 and SSI became widely used for the management of inpatient hyperglycemia.¹ However, during the past decade, there has been strong opposition to the use of SSI in hospitals. Many authors oppose its use, highlighting the retrospective rather than prospective nature of SSI therapy and concerns about inadequate glycemic control.^4-6 In 2004, the American College of Endocrinology first released a position statement discouraging the use of SSI alone and recommended basal-bolus insulin as the preferred method of glycemic control for inpatients with type 2 diabetes (T2D).⁷ The American Diabetes Association (ADA) inpatient guidelines in 2005⁸ and the Endocrine Society guidelines in 2012⁹ also opposed SSI monotherapy and reaffirmed that a basal-bolus insulin regimen should be used for most non–critically ill patients with diabetes. Those guidelines remain in place currently.

Several randomized controlled trials (RCTs) and meta-analyses have shown that basal-bolus insulin regimens provide superior glycemic control in non–critical inpatients when compared with SSI alone.^10-14 In addition, the RABBIT 2 (Randomized Study of Basal-Bolus Insulin Therapy in the Inpatient Management of Patients With Type 2 Diabetes) trial showed a significant reduction in perioperative complications¹⁰ among surgical patients when treated with basal-bolus insulin therapy. Despite these studies and strong recommendations against its use, SSI continues to be widely used in the United States. According to a 2007 survey of 44 US hospitals, 41% of noncritical patients with hyperglycemia were treated with SSI alone.¹⁵ In addition, SSI remains one of the most commonly prescribed insulin regimens in many countries around the world.^16-19 The persistence of SSI use raises questions as to why clinicians continue to use a therapy that has been strongly criticized. Some authors point to convenience and fear of hypoglycemia with a basal-bolus insulin regimen.^20,21 Alternatively, it is possible that SSI usage remains so pervasive because it is effective in a subset of patients. In fact, a 2018 Cochrane review concluded that existing evidence is not sufficiently robust to definitively recommend basal-bolus insulin over SSI for inpatient diabetes management of non–critically ill patients despite existing guidelines.²²

Owing to the ongoing controversy and widespread use of SSI, we designed an exploratory analysis to understand the rationale for such therapy by investigating whether a certain subpopulation of hospitalized patients with T2D may achieve target glycemic control with SSI alone. We hypothesized that noncritical patients with mild hyperglycemia and admission BG <180 mg/dL would do well with SSI alone and may not require intensive treatment with basal-bolus insulin regimens. To address this question, we used electronic health records with individual-level patient data to assess inpatient glycemic control of non–critically ill patients with T2D treated with SSI alone.

Participants

Data from 25,813 adult noncritical inpatients with T2D, with an index admission between June 1, 2010, and June 30, 2018, were obtained through the Emory Healthcare Clinical Data Warehouse infrastructure program. All patients were admitted to Emory Healthcare hospitals, including Emory University Hospital, Emory University Hospital Midtown, and Emory Saint Joseph’s Hospital, in Atlanta, Georgia. Data were extracted for each patient during the index hospitalization, including demographics, anthropometrics, and admission and inpatient laboratory values. Information was collected on daily point-of-care glucose values, hemoglobin A1c (HbA1c), hypoglycemic events, insulin doses, hospital complications, comorbidities, and hospital setting (medical vs surgical admission). International Classification of Diseases, 9th and 10th Revisions (ICD-9/10) codes were used to determine diagnosis of T2D, comorbidities, and complications.

From our initial dataset, we identified 16,366 patients who were treated with SSI during hospitalization. We excluded patients who were admitted to the intensive care unit (ICU) or placed on intravenous insulin, patients with missing admission BG values, and patients with a length of stay less than 1 day. To prevent inclusion of patients presenting in diabetic ketoacidosis or hyperosmolar hyperglycemic syndrome, we excluded patients with an admission BG >500 mg/dL. We then excluded 6,739 patients who received basal insulin within the first 2 days of hospitalization, as well as 943 patients who were treated with noninsulin (oral or injectable) antidiabetic agents. Our final dataset included 8,095 patients (Appendix Figure).

Patients in the SSI cohort included all patients who were treated with short-acting insulin only (regular insulin or rapid-acting [lispro, aspart, glulisine] insulin analogs) during the first 2 days of hospitalization. Patients who remained on only short-acting insulin during the entire hospitalization were defined as continuous SSI patients. Patients who subsequently received basal insulin after day 2 of hospitalization were defined as patients who transitioned to basal. Patients were stratified according to admission BG levels (first BG available on day of admission) and HbA1c (when available during index admission). We compared the baseline characteristics and clinical outcomes of patients who remained on SSI alone throughout the entirety of hospitalization with those of patients who required transition to basal insulin. The mean hospital BG was calculated by taking the average of all BG measurements during the hospital stay. We defined hypoglycemia as a BG <70 mg/dL and severe hypoglycemia as BG <40 mg/dL. Repeated hypoglycemia values were excluded if they occurred within a period of 2 hours.

Outcome Measures

The primary outcome was the percentage of patients with T2D achieving target glycemic control with SSI therapy, defined as mean hospital BG between 70 and 180 mg/dL without hypoglycemia <70 mg/dL during hospital stay. This threshold was determined based on 2019 ADA recommendations targeting hospital BG <180 mg/dL and avoidance of hypoglycemia.²³

Statistical Analysis

Patients were stratified according to continuous SSI versus transitioned to basal treatment. Patients who remained on continuous SSI were further categorized into four categories based on admission BG: <140 mg/dL, 140 to 180 mg/dL, 180 to 250 mg/dL, and ≥250 mg/dL. Clinical characteristics were compared using Wilcoxon rank-sum tests (if continuous) and chi-square tests or Fisher exact tests (if categorical). We then compared the clinical outcomes among continuous SSI patients with different admission BG levels (<140 mg/dL, 140-180 mg/dL, 180-250 mg/dL, and ≥250 mg/dL) and with different HbA1c levels (<7%, 7%-8%, 8%-9%, ≥9%). Within each scenario, logistic regression for the outcome of poor glycemic control, defined as mean hospital BG >180 mg/dL, was performed to evaluate the HbA1c levels and admission BG levels controlling for other factors (age, gender, body mass index [BMI], race, setting [medicine versus surgery] and Charlson Comorbidity Index score). A P value < .05 was regarded as statistically significant. All analyses were performed based on available cases and conducted in SAS version 9.4 (SAS Institute Inc.).

Among 25,813 adult patients with T2D, 8,095 patients (31.4%) were treated with SSI alone during the first 2 days of hospitalization. Of those patients treated with SSI, 6,903 (85%) remained on continuous SSI alone during the entire hospitalization, and 1,192 (15%) were transitioned to basal insulin. The clinical characteristics of these patients on continuous SSI and those who transitioned to basal insulin are shown in Table 1. Patients who transitioned to basal insulin had significantly higher mean (SD) admission BG (191.8 [88.2] mg/dL vs 156.4 [65.4] mg/dL, P < .001) and higher mean (SD) HbA1c (8.1% [2.0%] vs 7.01% [1.5%], P < .001), compared with those who remained on continuous SSI. Patients who transitioned to basal insulin were also younger and more likely to have chronic kidney disease (CKD), but less likely to have congestive heart failure, coronary artery disease, or chronic obstructive pulmonary disease (COPD). The Charlson Comorbidity Index score was significantly higher for patients who transitioned to basal (4.4 [2.5]) than for those who remained on continuous SSI (4.1 [2.5], P < .001). There were no significant differences among sex, BMI, or glomerular filtration rate (GFR) on admission. Of those transitioned to basal insulin, 53% achieved a mean hospitalization BG <180 mg/dL, compared with 82% of those on continuous SSI. The overall rate of hypoglycemia in the continuous SSI group was 8% compared with 18% in those transitioned to basal insulin.

Of the patients who remained on continuous SSI throughout the hospitalization, 3,319 patients (48%) had admission BG <140 mg/dL, 1,671 patients (24%) had admission BG 140 to 180 mg/dL, and 1,913 patients (28%) had admission BG >180 mg/dL. Only 9% of patients who remained on continuous SSI had admission BG ≥250 mg/dL. Patients with admission BG <140 mg/dL were older, had lower BMI and HbA1c, had higher rates of COPD and CKD, and were more likely to be admitted to a surgical service compared with patients with admission BG >140 mg/dL (P < .05 for all; Table 2).

Hospital glycemic control for patients on continuous SSI according to admission BG is displayed in Table 3. Among patients who remained on continuous SSI, 96% of patients with admission BG <140 mg/dL had a mean hospital BG <180 mg/dL; of them, 86% achieved target control without hypoglycemia. Similar rates of target control were achieved in patients with admission BG 140 to 180 mg/dL (83%), in contrast to patients with admission BG ≥250 mg/dL, of whom only 18% achieved target control (P < .001). These findings parallel those seen in patients transitioned to basal insulin. Of patients in the transition group admitted with BG <140 mg/dL and <180 mg/dL, 88.5% and 84.6% had mean hospital BG <180 mg/dL, respectively, while 69.1% and 68.9% had mean BG between 70 and 180 mg/dL without hypoglycemia. The overall frequency of hypoglycemia <70 mg/dL among patients on continuous SSI was 8% and was more common in patients with admission BG <140 mg/dL (10%) compared with patients with higher admission glucose levels (BG 140-180 mg/dL [4%], 180-250 mg/dL [4%], or ≥250 mg/dL [6%], P < .001). There was no difference in rates of severe hypoglycemia <40 mg/dL among groups.

HbA1c data were available for 2,560 of the patients on continuous SSI (Table 3). Mean hospital BG increased significantly with increasing HbA1c values. Patients admitted with HbA1c <7% had lower mean (SD) hospital BG (132.2 [28.2] mg/dL) and were more likely to achieve target glucose control during hospitalization (85%) compared with those with HbA1c 7% to 8% (mean BG, 148.7 [30.8] mg/dL; 80% target control), HbA1c 8% to 9% (mean BG, 169.1 [37.9] mg/dL; 61% target control), or HbA1c ≥9% (mean BG, 194.9 [53.4] mg/dL; 38% target control) (P < .001).

In a logistic regression analysis adjusted for age, gender, BMI, race, setting (medicine vs surgery), and Charlson Comorbidity Index score, the odds of poor glycemic control increased with higher admission BG (admission BG 140-180 mg/dL: odds ratio [OR], 1.8; 95% CI, 1.5-2.2; admission BG 180-250 mg/dL: OR, 3.7; 95% CI, 3.1-4.4; admission BG ≥250 mg/dL: OR, 7.2; 95% CI, 5.8-9.0; reference admission BG <140 mg/dL; Figure). Similarly, the logistic regression analysis showed greater odds of poor in-hospital glycemic control with increasing HbA1c (OR, 6.1; 95% CI, 4.3-8.8 for HbA1c >9% compared with HbA1c <7%).

This large retrospective cohort study examined the effectiveness of SSI for glycemic control in noncritical inpatients with T2D. Our results indicate that SSI is still widely used in our hospital system, with 31.4% of our initial cohort managed with SSI alone. We found that 86% of patients with BG <140 mg/dL and 83% of patients with BG 140 to 180 mg/dL achieved glycemic control without hypoglycemia when managed with SSI alone, compared with 53% of those admitted with BG 180 to 250 mg/dL and only 18% of those with admission BG ≥250 mg/dL. This high success rate of achieving optimal BG control with SSI alone is comparable to that seen with transition to basal insulin and may explain the prevalent use of SSI for the management of patients with T2D and mild to moderate hyperglycemia.

Published clinical guideline recommendations promoting the use of basal-bolus insulin treatment algorithms are based on the results of a few RCTs that compared the efficacy of SSI vs a basal-bolus insulin regimen. These studies reported significantly lower mean daily BG concentration with basal or basal-bolus insulin therapy compared with SSI.^10,11,24 However, it is interesting to note that the mean admission BG of patients treated with SSI in these RCTs ranged from 184 to 225 mg/dL. Patients in these trials were excluded if admission BG was <140 mg/dL.^10,11,24 This is in contrast to our study evaluating real-world data in non–critically ill settings in which we found that 48% of patients treated with SSI had admission BG <140 mg/dL, and nearly 75% had admission BG <180 mg/dL. This suggests that by nature of study design, most RCTs excluded the population of patients who do achieve good glycemic control with SSI and may have contributed to the perception that basal insulin is preferable in all populations.

Our analysis indicates that healthcare professionals should consider admission BG when selecting the type of insulin regimen to manage patients with T2D in the hospital. Our results suggest that SSI may be appropriate for many patients with admission BG <180 mg/dL and should be avoided as monotherapy in patients with admission BG ≥180 mg/dL, as the proportion of patients achieving target control decreased with increasing admission BG. More importantly, if a patient is not controlled with SSI alone, intensification of therapy with the addition of basal insulin is indicated to achieve glycemic control. In addition, we found that the admission HbA1c is an appropriate marker to consider as well, with hospital glycemic control deteriorating with increasing HbA1c values, paralleling the admission BG. The main limitation to widespread use of HbA1c for therapeutic decision-making is access to values at time of patient admission; in our population, only 37% of patients had an HbA1c value available during the index hospitalization.

Previous publications have reported that hypoglycemia carries significant safety concerns, especially among a hospitalized population.^25-27 As such, we included hypoglycemia as an important metric in our definition of target glycemic control rather than simply using mean hospital BG or number of hyperglycemic events to define treatment effectiveness. We did find a higher rate of hypoglycemia in patients with moderate admission BG treated with SSI compared with those with higher admission BG; however, few patients overall experienced clinically significant (<54 mg/dL) or severe (<40 mg/dL) hypoglycemia.

In our population, only 15% of patients started on SSI received additional basal insulin during hospitalization. This finding is similar to data reported in the Rabbit 2 trial, in which 14% of patients failed SSI alone, with a higher failure rate among those with higher BG on admission.¹⁰ Given the observational nature of this study, we cannot definitively state why certain patients in our population required additional basal insulin, but we can hypothesize that these patients admitted with BG ≥180 mg/dL had higher treatment failure rates and greater rates of hyperglycemia, therefore receiving intensified insulin therapy as clinically indicated at the discretion of the treating physician. Patients who transitioned from SSI to basal insulin had significantly higher admission BG and HbA1c compared with patients who remained on SSI alone. We noted that the rates of hypoglycemia were higher in the group that transitioned to basal (18% vs 8%) and similar to rates reported in previous RCTs.^11,24

This observational study takes advantage of a large, diverse study population and a combination of medicine and surgery patients in a real-world setting. We acknowledge several limitations in our study. Our primary data were observational in nature, and as such, some baseline patient characteristics were notably different between groups, suggesting selection bias for treatment allocation to SSI. We do not know which patients were managed by primary teams compared with specialized diabetes consult services, which may also influence treatment regimens. We did not have access to information about patients’ at-home diabetes medication regimens or duration of diabetes, both of which have been shown in prior publications to affect an individual’s overall hospital glycemic control. Data on HbA1c values were available for only approximately one-third of patients. In addition, our study did not include patients without a history of diabetes who developed stress-induced hyperglycemia, a population that may benefit from conservative therapy such as SSI.²⁸ A diagnosis of CKD was defined based on ICD 9/10 codes and not on admission estimated GFR. More specific data regarding stage of CKD or changes in renal function over the duration of hospitalization are not available, which could influence insulin prescribing practice. In addition, we defined the basal group as patients prescribed any form of basal insulin (NPH, glargine, detemir or degludec), and we do not have information on the use of prandial versus correction doses of rapid-acting insulin in the basal insulin–treated group.

In conclusion, our observational study indicates that the use of SSI results in appropriate target glycemic control for most noncritical medicine and surgery patients with admission BG <180 mg/dL. In agreement with previous RCTs, our study confirms that SSI as monotherapy is frequently inadequate in patients with significant hyperglycemia >180 mg/dL.^10,11,24,29 We propose that an individualized approach to inpatient glycemic management is imperative, and cautious use of SSI may be a viable option for certain patients with mild hyperglycemia and admission BG <180 mg/dL. Further observational and randomized studies are needed to confirm the efficacy of SSI therapy in T2D patients with mild hyperglycemia. By identifying which subset of patients can be safely managed with SSI alone, we can better understand which patients will require escalation of therapy with intensive glucose management.

Sepsis is the leading cause of in-hospital mortality in the United States.¹ Sepsis is present on admission in 85% of cases, and each hour delay in antibiotic treatment is associated with 4% to 7% increased odds of mortality.^2,3 Prompt identification and treatment of sepsis is essential for reducing morbidity and mortality, but identifying sepsis during triage is challenging.²

Risk stratification scores that rely solely on data readily available at the bedside have been developed to quickly identify those at greatest risk of poor outcomes from sepsis in real time. The quick Sequential Organ Failure Assessment (qSOFA) score, the National Early Warning System (NEWS2), and the Shock Index are easy-to-calculate measures that use routinely collected clinical data that are not subject to laboratory delay. These scores can be incorporated into electronic health record (EHR)-based alerts and can be calculated longitudinally to track the risk of poor outcomes over time. qSOFA was developed to quantify patient risk at bedside in non-intensive care unit (ICU) settings, but there is no consensus about its ability to predict adverse outcomes such as mortality and ICU admission.^4-6 The United Kingdom’s National Health Service uses NEWS2 to identify patients at risk for sepsis.⁷ NEWS has been shown to have similar or better sensitivity in identifying poorer outcomes in sepsis patients compared with systemic inflammatory response syndrome (SIRS) criteria and qSOFA.^4,8-11 However, since the latest update of NEWS2 in 2017, there has been little study of its predictive ability. The Shock Index is a simple bedside score (heart rate divided by systolic blood pressure) that was developed to detect changes in cardiovascular performance before systemic shock onset. Although it was not developed for infection and has not been regularly applied in the sepsis literature, the Shock Index might be useful for identifying patients at increased risk of poor outcomes. Patients with higher and sustained Shock Index scores are more likely to experience morbidity, such as hyperlactatemia, vasopressor use, and organ failure, and also have an increased risk of mortality.^12-14

Although the predictive abilities of these bedside risk stratification scores have been assessed individually using standard binary cut-points, the comparative performance of qSOFA, the Shock Index, and NEWS2 has not been evaluated in patients presenting to an emergency department (ED) with suspected sepsis. Our objective was to provide a head-to-head comparison of the test characteristics of qSOFA, the Shock Index, and NEWS2 calculated at ED triage for predicting in-hospital mortality and ED-to-ICU admission in patients with suspected sepsis to help health systems and providers select screening measures.

Design and Setting

We conducted a retrospective cohort study of ED patients who presented with suspected sepsis to the University of California San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights between June 1, 2012, and December 31, 2018. Our institution is a 785-bed academic teaching hospital with approximately 30,000 ED encounters per year. The study was approved with a waiver of informed consent by the UCSF Human Research Protection Program.

Participants

We use an Epic-based EHR platform (Epic 2017, Epic Systems Corporation) for clinical care, which was implemented on June 1, 2012. All data elements were obtained from Clarity, the relational database that stores Epic’s inpatient data. The study included encounters for patients age ≥18 years who had blood cultures ordered within 24 hours of ED presentation and administration of intravenous antibiotics within 24 hours. Repeat encounters were treated independently in our analysis.

Outcomes and Measures

We compared the ability of qSOFA, the Shock Index, and NEWS2 to predict in-hospital mortality and admission to the ICU from the ED (ED-to-ICU admission). We used the most abnormal vital signs and clinical assessments gathered within 30 minutes of ED presentation to identify patients who were qSOFA-positive, Shock Index-positive, and NEWS2-positive based on standard cut-points of risk. Data elements used to calculate qSOFA, Shock Index, and NEWS2 included blood pressure, heart rate, respiratory rate, Glasgow Coma Scale (GCS) score, oxygen saturation, requirement for supplemental oxygen, and temperature (Table 1). Patients were considered positive if they had a qSOFAscore ≥2, Shock Index of >0.7, and NEWS2 ≥5 based on triage vital signs.^7,15,16 We considered patients to have altered mental status, a criterion used for NEWS2, if they had a GCS score <15 instead of using the “alert, verbal, confusion, pain, unresponsive” scale, which is not captured in our EHR, a method that has been used in earlier studies.^17,18 Missing assessments were considered normal. Although our primary analysis focused on the scores calculated within 30 minutes of ED presentation, we performed a sensitivity analysis examining scores calculated within 1 hour of ED presentation in the event of a delay in gathering triage vital sign data.

We compared demographic and clinical characteristics of patients who were positive for qSOFA, the Shock Index, and NEWS2. Demographic data were extracted from the EHR and included primary language, age, sex, and insurance status. All International Classification of Diseases (ICD)-9/10 diagnosis codes were pulled from Clarity billing tables. We used the Elixhauser comorbidity groupings¹⁹ of ICD-9/10 codes present on admission to identify preexisting comorbidities and underlying organ dysfunction. To estimate burden of comorbid illnesses, we calculated the validated van Walraven comorbidity index,²⁰ which provides an estimated risk of in-hospital death based on documented Elixhauser comorbidities. Admission level of care (acute, stepdown, or intensive care) was collected for inpatient admissions to assess initial illness severity.²¹ We also evaluated discharge disposition and in-hospital mortality. Index blood culture results were collected, and dates and timestamps of mechanical ventilation, fluid, vasopressor, and antibiotic administration were obtained for the duration of the encounter.

UCSF uses an automated, real-time, algorithm-based severe sepsis alert that is triggered when a patient meets ≥2 SIRS criteria and again when the patient meets severe sepsis or septic shock criteria (ie, ≥2 SIRS criteria in addition to end-organ dysfunction and/or fluid nonresponsive hypotension). This sepsis screening alert was in use for the duration of our study.²²

Statistical Analysis

We performed a subgroup analysis among those who were diagnosed with sepsis, according to the 2016 Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria. Sepsis is defined as a change in Sequential Organ Failure Assessment (SOFA) score of ≥2 points within the first 48 hours.²³ Additionally, patients meeting Sepsis-3 criteria needed to (1) receive ≥4 days of sequential antibiotic therapy or experience death or discharge to hospice before 4 days of antibiotic therapy or (2) have a validated sepsis discharge billing code. These parameters were added to increase the specificity of our sample.²⁴

All statistical analyses were conducted using Stata 14 (StataCorp). We summarized differences in demographic and clinical characteristics among the populations meeting each severity score but elected not to conduct hypothesis testing because patients could be positive for one or more scores. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each score to predict in-hospital mortality and ED-to-ICU admission. To allow comparison with other studies, we also created a composite outcome of either in-hospital mortality or ED-to-ICU admission. To assess score discrimination to predict in-hospital mortality and ED-to-ICU admission, we calculated the area under the receiver operating characteristic curve (AUROC) along with asymptotic normal 95% CI using the “roctab” command considering a binary cut-point, as well as the full range of scores measured in the cohort. The AUROC range from 0.50 to 1.00 and a score in the 0.70 to 0.80 range can be considered fair.²⁵ We assessed significant differences between severity score AUROCs using the DeLong method²⁶ implemented through Stata 14’s “roccomp” command. As a sensitivity analysis, we explored whether the standard cut-points for qSOFA, the Shock Index, and NEWS2 provided the highest AUROC in our population by calculating test characteristics for several score cut-points.

Within our sample 23,837 ED patients had blood cultures ordered within 24 hours of ED presentation and were considered to have suspected sepsis. The mean age of the cohort was 60.8 years, and 1,612 (6.8%) had positive blood cultures. A total of 12,928 patients (54.2%) were found to have sepsis. We documented 1,427 in-hospital deaths (6.0%) and 3,149 (13.2%) ED-to-ICU admissions. At ED triage 1,921 (8.1%) were qSOFA-positive, 4,273 (17.9%) were Shock Index-positive, and 11,832 (49.6%) were NEWS2-positive. At ED triage, blood pressure, heart rate, respiratory rate, and oxygen saturated were documented in >99% of patients, 93.5% had temperature documented, and 28.5% had GCS recorded. If the window of assessment was widened to 1 hour, GCS was only documented among 44.2% of those with suspected sepsis.

Demographic Characteristics and Clinical Course

We identified significant differences when comparing demographic and clinical characteristics among patients who scored positive for the three severity measures at triage (Table 2). Although no hypothesis testing was conducted because patients could meet one or more scores, qSOFA-positive patients were older (median 70, 66, and 64 years, respectively), more likely to have Medicare as the primary payor (67.6% vs 59.7% vs 56.6%), to have chronic renal failure (26.1%, 23.1%, and 23.3%, respectively), to have a greater degree of underlying comorbidities based on the van Walraven Comorbidity Index (median 15, 12, and 11, respectively), and to be admitted to the ICU from the ED (48.1%, 36.3%, and 21.0%, respectively) compared with those positive for the Shock Index or NEWS2.

qSOFA-positive patients received antibiotics more quickly than those who were Shock Index-positive or NEWS2-positive (median 1.5, 1.8, and 2.8 hours after admission, respectively). In addition, those who were qSOFA-positive were more likely to have a positive blood culture (10.9%, 9.4%, and 8.5%, respectively) and to receive an EHR-based diagnosis of sepsis (77.0%, 69.6%, and 60.9%, respectively) than those who were Shock Index- or NEWS2-positive. Those who were qSOFA-positive also were more likely to be mechanically ventilated during their hospital stay (25.4%, 19.2%, and 10.8%, respectively) and to receive vasopressors (33.5%, 22.5%, and 12.2%, respectively). In-hospital mortality also was more common among those who were qSOFA-positive at triage (23.4%, 15.3%, and 9.2%, respectively).

Because both qSOFA and NEWS2 incorporate GCS, we explored baseline characteristics of patients with GCS documented at triage (n = 6,794). These patients were older (median age 63 and 61 years, P < .0001), more likely to be male (54.9% and 53.4%, P = .0031), more likely to have renal failure (22.8% and 20.1%, P < .0001), more likely to have liver disease (14.2% and 12.8%, P = .006), had a higher van Walraven comorbidity score on presentation (median 10 and 8, P < .0001), and were more likely to go directly to the ICU from the ED (20.2% and 10.6%, P < .0001). However, among the 6,397 GCS scores documented at triage, only 1,579 (24.7%) were abnormal.

Test Characteristics of qSOFA, Shock Index, and NEWS2 for Predicting In-hospital Mortality and ED-to-ICU Admission

Among 23,837 patients with suspected sepsis, NEWS2 had the highest sensitivity for predicting in-hospital mortality (76.0%; 95% CI, 73.7%-78.2%) and ED-to-ICU admission (78.9%; 95% CI, 77.5%-80.4%) but had the lowest specificity for in-hospital mortality (52.0%; 95% CI, 51.4%-52.7%) and for ED-to-ICU admission (54.8%; 95% CI, 54.1%-55.5%) (Table 3). qSOFA had the lowest sensitivity for in-hospital mortality (31.5%; 95% CI, 29.1%-33.9%) and ED-to-ICU admission (29.3%; 95% CI, 27.7%-30.9%) but the highest specificity for in-hospital mortality (93.4%; 95% CI, 93.1%-93.8%) and ED-to-ICU admission (95.2%; 95% CI, 94.9%-95.5%). The Shock Index had a sensitivity that fell between qSOFA and NEWS2 for in-hospital mortality (45.8%; 95% CI, 43.2%-48.5%) and ED-to-ICU admission (49.2%; 95% CI, 47.5%-51.0%). The specificity of the Shock Index also was between qSOFA and NEWS2 for in-hospital mortality (83.9%; 95% CI, 83.4%-84.3%) and ED-to-ICU admission (86.8%; 95% CI, 86.4%-87.3%). All three scores exhibited relatively low PPV, ranging from 9.2% to 23.4% for in-hospital mortality and 21.0% to 48.0% for ED-to-ICU triage. Conversely, all three scores exhibited relatively high NPV, ranging from 95.5% to 97.1% for in-hospital mortality and 89.8% to 94.5% for ED-to-ICU triage. The patterns in sensitivity and specificity for in-hospital mortality and ED-to-ICU admission were similar among the 12,928 patients who received an EHR-based sepsis diagnosis with the tests generally demonstrating lower specificities, higher PPVs, and lower NPVs (Table 3).

When considering a binary cutoff, the Shock Index exhibited the highest AUROC for in-hospital mortality (0.648; 95% CI, 0.635-0.662) and had a significantly higher AUROC than qSOFA (AUROC, 0.625; 95% CI, 0.612-0.637; P = .0005), but there was no difference compared with NEWS2 (AUROC, 0.640; 95% CI, 0.628-0.652; P = .2112). NEWS2 had a significantly higher AUROC than qSOFA for predicting in-hospital mortality (P = .0227). The Shock Index also exhibited the highest AUROC for ED-to-ICU admission (0.680; 95% CI, 0.617-0.689), which was significantly higher than the AUROC for qSOFA (P < .0001) and NEWS2 (P = 0.0151). NEWS2 had a significantly higher AUROC than qSOFA for predicting ED-to-ICU admission (P < .0001). Similar findings were seen in patients found to have sepsis. When considering the range of possible scores measured in our cohort, qSOFA and NEWS2 exhibited higher AUROCs for in-hospital mortality and ED-to-ICU admission than the Shock Index among patients with suspected infection and the subgroup with a sepsis diagnosis (Figure).

In this retrospective cohort study of 23,837 patients who presented to the ED with suspected sepsis, the standard qSOFA threshold was met least frequently, followed by the Shock Index and NEWS2. NEWS2 had the highest sensitivity but the lowest specificity for predicting in-hospital mortality and ED-to-ICU admission, making it a challenging bedside risk stratification scale for identifying patients at risk of poor clinical outcomes. When comparing predictive performance among the three scales, qSOFA had the highest specificity and the Shock Index had the highest AUROC for in-hospital mortality and ED-to-ICU admission in this cohort of patients with suspected sepsis. These trends in sensitivity, specificity, and AUROC were consistent among those who met EHR criteria for a sepsis diagnosis. In the analysis of the three scoring systems using all available cut-points, qSOFA and NEWS2 had the highest AUROCs, followed by the Shock Index.

Considering the rapid progression from organ dysfunction to death in sepsis patients, as well as the difficulty establishing a sepsis diagnosis at triage,²³ providers must quickly identify patients at increased risk of poor outcomes when they present to the ED. Sepsis alerts often are built using SIRS criteria,²⁷ including the one used for sepsis surveillance at UCSF since 2012,²² but the white blood cell count criterion is subject to a laboratory lag and could lead to a delay in identification. Implementation of a point-of-care bedside score alert that uses readily available clinical data could allow providers to identify patients at greatest risk of poor outcomes immediately at ED presentation and triage, which motivated us to explore the predictive performance of qSOFA, the Shock Index, and NEWS2.

Our study is the first to provide a head-to-head comparison of the predictive performance of qSOFA, the Shock Index, and NEWS2, three easy-to-calculate bedside risk scores that use EHR data collected among patients with suspected sepsis. The Sepsis-3 guidelines recommend qSOFA to quickly identify non-ICU patients at greatest risk of poor outcomes because the measure exhibited predictive performance similar to the more extensive SOFA score outside the ICU.^16,23 Although some studies have confirmed qSOFA’s high predictive performance,^28-31 our test characteristics and AUROC findings are in line with other published analyses.^4,6,10,17 The UK National Health Service is using NEWS2 to screen for patients at risk of poor outcomes from sepsis. Several analyses that assessed the predictive ability of NEWS have reported estimates in line with our findings.^4,10,32 The Shock Index was introduced in 1967 and provided a metric to evaluate hemodynamic stability based on heart rate and systolic blood pressure.³³ The Shock Index has been studied in several contexts, including sepsis,³⁴ and studies show that a sustained Shock Index is associated with increased odds of vasopressor administration, higher prevalence of hyperlactatemia, and increased risk of poor outcomes in the ICU.^13,14

For our study, we were particularly interested in exploring how the Shock Index would compare with more frequently used severity scores such as qSOFA and NEWS2 among patients with suspected sepsis, given the simplicity of its calculation and the easy availability of required data. In our cohort of 23,837 patients, only 159 people had missing blood pressure and only 71 had omitted heart rate. In contrast, both qSOFA and NEWS2 include an assessment of level of consciousness that can be subject to variability in assessment methods and EHR documentation across institutions.¹¹ In our cohort, GCS within 30 minutes of ED presentation was missing in 72 patients, which could have led to incomplete calculation of qSOFA and NEWS2 if a missing value was not actually within normal limits.

Several investigations relate qSOFA to NEWS but few compare qSOFA with the newer NEWS2, and even fewer evaluate the Shock Index with any of these scores.^{10,11,18,29,35-37} In general, studies have shown that NEWS exhibits a higher AUROC for predicting mortality, sepsis with organ dysfunction, and ICU admission, often as a composite outcome.^{4,11,18,37,38} A handful of studies compare the Shock Index to SIRS; however, little has been done to compare the Shock Index to qSOFA or NEWS2, scores that have been used specifically for sepsis and might be more predictive of poor outcomes than SIRS.³³ In our study, the Shock Index had a higher AUROC than either qSOFA or NEWS2 for predicting in-hospital mortality and ED-to-ICU admission measured as separate outcomes and as a composite outcome using standard cut-points for these scores.

When selecting a severity score to apply in an institution, it is important to carefully evaluate the score’s test characteristics, in addition to considering the availability of reliable data. Tests with high sensitivity and NPV for the population being studied can be useful to rule out disease or risk of poor outcome, while tests with high specificity and PPV can be useful to rule in disease or risk of poor outcome.³⁹ When considering specificity, qSOFA’s performance was superior to the Shock Index and NEWS2 in our study, but a small percentage of the population was identified using a cut-point of qSOFA ≥2. If we used qSOFA and applied this standard cut-point at our institution, we could be confident that those identified were at increased risk, but we would miss a significant number of patients who would experience a poor outcome. When considering sensitivity, performance of NEWS2 was superior to qSOFA and the Shock Index in our study, but one-half of the population was identified using a cut-point of NEWS2 ≥5. If we were to apply this standard NEWS2 cut-point at our institution, we would assume that one-half of our population was at risk, which might drive resource use towards patients who will not experience a poor outcome. Although none of the scores exhibited a robust AUROC measure, the Shock Index had the highest AUROC for in-hospital mortality and ED-to-ICU admission when using the standard binary cut-point, and its sensitivity and specificity is between that of qSOFA and NEWS2, potentially making it a score to use in settings where qSOFA and NEWS2 score components, such as altered mentation, are not reliably collected. Finally, our sensitivity analysis varying the binary cut-point of each score within our population demonstrated that the standard cut-points might not be as useful within a specific population and might need to be tailored for implementation, balancing sensitivity, specificity, PPV, and NPV to meet local priorities and ICU capacity.

Our study has limitations. It is a single-center, retrospective analysis, factors that could reduce generalizability. However, it does include a large and diverse patient population spanning several years. Missing GCS data could have affected the predictive ability of qSOFA and NEWS2 in our cohort. We could not reliably perform imputation of GCS because of the high missingness and therefore we assumed missing was normal, as was done in the Sepsis-3 derivation studies.¹⁶ Previous studies have attempted to impute GCS and have not observed improved performance of qSOFA to predict mortality.⁴⁰ Because manually collected variables such as GCS are less reliably documented in the EHR, there might be limitations in their use for triage risk scores.

Although the current analysis focused on the predictive performance of qSOFA, the Shock Index, and NEWS2 at triage, performance of these scores could affect the ED team’s treatment decisions before handoff to the hospitalist team and the expected level of care the patient will receive after in-patient admission. These tests also have the advantage of being easy to calculate at the bedside over time, which could provide an objective assessment of longitudinal predicted prognosis. Future work should assess the longitudinal performance of each of these scores among those with suspected sepsis and to determine the impact using these scores would have on clinical and resource utilization outcomes.

Local priorities should drive selection of a screening tool, balancing sensitivity, specificity, PPV, and NPV to achieve the institution’s goals. qSOFA, Shock Index, and NEWS2 are risk stratification tools that can be easily implemented at ED triage using data available at the bedside. Although none of these scores performed strongly when comparing AUROCs, qSOFA was highly specific for identifying patients with poor outcomes, and NEWS2 was the most sensitive for ruling out those at high risk among patients with suspected sepsis. The Shock Index exhibited a sensitivity and specificity that fell between qSOFA and NEWS2 and also might be considered to identify those at increased risk, given its ease of implementation, particularly in settings where altered mentation is unreliably or inconsistently documented.

The authors thank the UCSF Division of Hospital Medicine Data Core for their assistance with data acquisition.

Sepsis is the leading cause of in-hospital mortality in the United States.¹ Sepsis is present on admission in 85% of cases, and each hour delay in antibiotic treatment is associated with 4% to 7% increased odds of mortality.^2,3 Prompt identification and treatment of sepsis is essential for reducing morbidity and mortality, but identifying sepsis during triage is challenging.²

Risk stratification scores that rely solely on data readily available at the bedside have been developed to quickly identify those at greatest risk of poor outcomes from sepsis in real time. The quick Sequential Organ Failure Assessment (qSOFA) score, the National Early Warning System (NEWS2), and the Shock Index are easy-to-calculate measures that use routinely collected clinical data that are not subject to laboratory delay. These scores can be incorporated into electronic health record (EHR)-based alerts and can be calculated longitudinally to track the risk of poor outcomes over time. qSOFA was developed to quantify patient risk at bedside in non-intensive care unit (ICU) settings, but there is no consensus about its ability to predict adverse outcomes such as mortality and ICU admission.^4-6 The United Kingdom’s National Health Service uses NEWS2 to identify patients at risk for sepsis.⁷ NEWS has been shown to have similar or better sensitivity in identifying poorer outcomes in sepsis patients compared with systemic inflammatory response syndrome (SIRS) criteria and qSOFA.^4,8-11 However, since the latest update of NEWS2 in 2017, there has been little study of its predictive ability. The Shock Index is a simple bedside score (heart rate divided by systolic blood pressure) that was developed to detect changes in cardiovascular performance before systemic shock onset. Although it was not developed for infection and has not been regularly applied in the sepsis literature, the Shock Index might be useful for identifying patients at increased risk of poor outcomes. Patients with higher and sustained Shock Index scores are more likely to experience morbidity, such as hyperlactatemia, vasopressor use, and organ failure, and also have an increased risk of mortality.^12-14

Although the predictive abilities of these bedside risk stratification scores have been assessed individually using standard binary cut-points, the comparative performance of qSOFA, the Shock Index, and NEWS2 has not been evaluated in patients presenting to an emergency department (ED) with suspected sepsis. Our objective was to provide a head-to-head comparison of the test characteristics of qSOFA, the Shock Index, and NEWS2 calculated at ED triage for predicting in-hospital mortality and ED-to-ICU admission in patients with suspected sepsis to help health systems and providers select screening measures.

Design and Setting

We conducted a retrospective cohort study of ED patients who presented with suspected sepsis to the University of California San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights between June 1, 2012, and December 31, 2018. Our institution is a 785-bed academic teaching hospital with approximately 30,000 ED encounters per year. The study was approved with a waiver of informed consent by the UCSF Human Research Protection Program.

Participants

We use an Epic-based EHR platform (Epic 2017, Epic Systems Corporation) for clinical care, which was implemented on June 1, 2012. All data elements were obtained from Clarity, the relational database that stores Epic’s inpatient data. The study included encounters for patients age ≥18 years who had blood cultures ordered within 24 hours of ED presentation and administration of intravenous antibiotics within 24 hours. Repeat encounters were treated independently in our analysis.

Outcomes and Measures

We compared the ability of qSOFA, the Shock Index, and NEWS2 to predict in-hospital mortality and admission to the ICU from the ED (ED-to-ICU admission). We used the most abnormal vital signs and clinical assessments gathered within 30 minutes of ED presentation to identify patients who were qSOFA-positive, Shock Index-positive, and NEWS2-positive based on standard cut-points of risk. Data elements used to calculate qSOFA, Shock Index, and NEWS2 included blood pressure, heart rate, respiratory rate, Glasgow Coma Scale (GCS) score, oxygen saturation, requirement for supplemental oxygen, and temperature (Table 1). Patients were considered positive if they had a qSOFAscore ≥2, Shock Index of >0.7, and NEWS2 ≥5 based on triage vital signs.^7,15,16 We considered patients to have altered mental status, a criterion used for NEWS2, if they had a GCS score <15 instead of using the “alert, verbal, confusion, pain, unresponsive” scale, which is not captured in our EHR, a method that has been used in earlier studies.^17,18 Missing assessments were considered normal. Although our primary analysis focused on the scores calculated within 30 minutes of ED presentation, we performed a sensitivity analysis examining scores calculated within 1 hour of ED presentation in the event of a delay in gathering triage vital sign data.

We compared demographic and clinical characteristics of patients who were positive for qSOFA, the Shock Index, and NEWS2. Demographic data were extracted from the EHR and included primary language, age, sex, and insurance status. All International Classification of Diseases (ICD)-9/10 diagnosis codes were pulled from Clarity billing tables. We used the Elixhauser comorbidity groupings¹⁹ of ICD-9/10 codes present on admission to identify preexisting comorbidities and underlying organ dysfunction. To estimate burden of comorbid illnesses, we calculated the validated van Walraven comorbidity index,²⁰ which provides an estimated risk of in-hospital death based on documented Elixhauser comorbidities. Admission level of care (acute, stepdown, or intensive care) was collected for inpatient admissions to assess initial illness severity.²¹ We also evaluated discharge disposition and in-hospital mortality. Index blood culture results were collected, and dates and timestamps of mechanical ventilation, fluid, vasopressor, and antibiotic administration were obtained for the duration of the encounter.

UCSF uses an automated, real-time, algorithm-based severe sepsis alert that is triggered when a patient meets ≥2 SIRS criteria and again when the patient meets severe sepsis or septic shock criteria (ie, ≥2 SIRS criteria in addition to end-organ dysfunction and/or fluid nonresponsive hypotension). This sepsis screening alert was in use for the duration of our study.²²

Statistical Analysis

We performed a subgroup analysis among those who were diagnosed with sepsis, according to the 2016 Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria. Sepsis is defined as a change in Sequential Organ Failure Assessment (SOFA) score of ≥2 points within the first 48 hours.²³ Additionally, patients meeting Sepsis-3 criteria needed to (1) receive ≥4 days of sequential antibiotic therapy or experience death or discharge to hospice before 4 days of antibiotic therapy or (2) have a validated sepsis discharge billing code. These parameters were added to increase the specificity of our sample.²⁴

All statistical analyses were conducted using Stata 14 (StataCorp). We summarized differences in demographic and clinical characteristics among the populations meeting each severity score but elected not to conduct hypothesis testing because patients could be positive for one or more scores. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each score to predict in-hospital mortality and ED-to-ICU admission. To allow comparison with other studies, we also created a composite outcome of either in-hospital mortality or ED-to-ICU admission. To assess score discrimination to predict in-hospital mortality and ED-to-ICU admission, we calculated the area under the receiver operating characteristic curve (AUROC) along with asymptotic normal 95% CI using the “roctab” command considering a binary cut-point, as well as the full range of scores measured in the cohort. The AUROC range from 0.50 to 1.00 and a score in the 0.70 to 0.80 range can be considered fair.²⁵ We assessed significant differences between severity score AUROCs using the DeLong method²⁶ implemented through Stata 14’s “roccomp” command. As a sensitivity analysis, we explored whether the standard cut-points for qSOFA, the Shock Index, and NEWS2 provided the highest AUROC in our population by calculating test characteristics for several score cut-points.

Within our sample 23,837 ED patients had blood cultures ordered within 24 hours of ED presentation and were considered to have suspected sepsis. The mean age of the cohort was 60.8 years, and 1,612 (6.8%) had positive blood cultures. A total of 12,928 patients (54.2%) were found to have sepsis. We documented 1,427 in-hospital deaths (6.0%) and 3,149 (13.2%) ED-to-ICU admissions. At ED triage 1,921 (8.1%) were qSOFA-positive, 4,273 (17.9%) were Shock Index-positive, and 11,832 (49.6%) were NEWS2-positive. At ED triage, blood pressure, heart rate, respiratory rate, and oxygen saturated were documented in >99% of patients, 93.5% had temperature documented, and 28.5% had GCS recorded. If the window of assessment was widened to 1 hour, GCS was only documented among 44.2% of those with suspected sepsis.

Demographic Characteristics and Clinical Course

We identified significant differences when comparing demographic and clinical characteristics among patients who scored positive for the three severity measures at triage (Table 2). Although no hypothesis testing was conducted because patients could meet one or more scores, qSOFA-positive patients were older (median 70, 66, and 64 years, respectively), more likely to have Medicare as the primary payor (67.6% vs 59.7% vs 56.6%), to have chronic renal failure (26.1%, 23.1%, and 23.3%, respectively), to have a greater degree of underlying comorbidities based on the van Walraven Comorbidity Index (median 15, 12, and 11, respectively), and to be admitted to the ICU from the ED (48.1%, 36.3%, and 21.0%, respectively) compared with those positive for the Shock Index or NEWS2.

qSOFA-positive patients received antibiotics more quickly than those who were Shock Index-positive or NEWS2-positive (median 1.5, 1.8, and 2.8 hours after admission, respectively). In addition, those who were qSOFA-positive were more likely to have a positive blood culture (10.9%, 9.4%, and 8.5%, respectively) and to receive an EHR-based diagnosis of sepsis (77.0%, 69.6%, and 60.9%, respectively) than those who were Shock Index- or NEWS2-positive. Those who were qSOFA-positive also were more likely to be mechanically ventilated during their hospital stay (25.4%, 19.2%, and 10.8%, respectively) and to receive vasopressors (33.5%, 22.5%, and 12.2%, respectively). In-hospital mortality also was more common among those who were qSOFA-positive at triage (23.4%, 15.3%, and 9.2%, respectively).

Because both qSOFA and NEWS2 incorporate GCS, we explored baseline characteristics of patients with GCS documented at triage (n = 6,794). These patients were older (median age 63 and 61 years, P < .0001), more likely to be male (54.9% and 53.4%, P = .0031), more likely to have renal failure (22.8% and 20.1%, P < .0001), more likely to have liver disease (14.2% and 12.8%, P = .006), had a higher van Walraven comorbidity score on presentation (median 10 and 8, P < .0001), and were more likely to go directly to the ICU from the ED (20.2% and 10.6%, P < .0001). However, among the 6,397 GCS scores documented at triage, only 1,579 (24.7%) were abnormal.

Test Characteristics of qSOFA, Shock Index, and NEWS2 for Predicting In-hospital Mortality and ED-to-ICU Admission

Among 23,837 patients with suspected sepsis, NEWS2 had the highest sensitivity for predicting in-hospital mortality (76.0%; 95% CI, 73.7%-78.2%) and ED-to-ICU admission (78.9%; 95% CI, 77.5%-80.4%) but had the lowest specificity for in-hospital mortality (52.0%; 95% CI, 51.4%-52.7%) and for ED-to-ICU admission (54.8%; 95% CI, 54.1%-55.5%) (Table 3). qSOFA had the lowest sensitivity for in-hospital mortality (31.5%; 95% CI, 29.1%-33.9%) and ED-to-ICU admission (29.3%; 95% CI, 27.7%-30.9%) but the highest specificity for in-hospital mortality (93.4%; 95% CI, 93.1%-93.8%) and ED-to-ICU admission (95.2%; 95% CI, 94.9%-95.5%). The Shock Index had a sensitivity that fell between qSOFA and NEWS2 for in-hospital mortality (45.8%; 95% CI, 43.2%-48.5%) and ED-to-ICU admission (49.2%; 95% CI, 47.5%-51.0%). The specificity of the Shock Index also was between qSOFA and NEWS2 for in-hospital mortality (83.9%; 95% CI, 83.4%-84.3%) and ED-to-ICU admission (86.8%; 95% CI, 86.4%-87.3%). All three scores exhibited relatively low PPV, ranging from 9.2% to 23.4% for in-hospital mortality and 21.0% to 48.0% for ED-to-ICU triage. Conversely, all three scores exhibited relatively high NPV, ranging from 95.5% to 97.1% for in-hospital mortality and 89.8% to 94.5% for ED-to-ICU triage. The patterns in sensitivity and specificity for in-hospital mortality and ED-to-ICU admission were similar among the 12,928 patients who received an EHR-based sepsis diagnosis with the tests generally demonstrating lower specificities, higher PPVs, and lower NPVs (Table 3).

When considering a binary cutoff, the Shock Index exhibited the highest AUROC for in-hospital mortality (0.648; 95% CI, 0.635-0.662) and had a significantly higher AUROC than qSOFA (AUROC, 0.625; 95% CI, 0.612-0.637; P = .0005), but there was no difference compared with NEWS2 (AUROC, 0.640; 95% CI, 0.628-0.652; P = .2112). NEWS2 had a significantly higher AUROC than qSOFA for predicting in-hospital mortality (P = .0227). The Shock Index also exhibited the highest AUROC for ED-to-ICU admission (0.680; 95% CI, 0.617-0.689), which was significantly higher than the AUROC for qSOFA (P < .0001) and NEWS2 (P = 0.0151). NEWS2 had a significantly higher AUROC than qSOFA for predicting ED-to-ICU admission (P < .0001). Similar findings were seen in patients found to have sepsis. When considering the range of possible scores measured in our cohort, qSOFA and NEWS2 exhibited higher AUROCs for in-hospital mortality and ED-to-ICU admission than the Shock Index among patients with suspected infection and the subgroup with a sepsis diagnosis (Figure).

In this retrospective cohort study of 23,837 patients who presented to the ED with suspected sepsis, the standard qSOFA threshold was met least frequently, followed by the Shock Index and NEWS2. NEWS2 had the highest sensitivity but the lowest specificity for predicting in-hospital mortality and ED-to-ICU admission, making it a challenging bedside risk stratification scale for identifying patients at risk of poor clinical outcomes. When comparing predictive performance among the three scales, qSOFA had the highest specificity and the Shock Index had the highest AUROC for in-hospital mortality and ED-to-ICU admission in this cohort of patients with suspected sepsis. These trends in sensitivity, specificity, and AUROC were consistent among those who met EHR criteria for a sepsis diagnosis. In the analysis of the three scoring systems using all available cut-points, qSOFA and NEWS2 had the highest AUROCs, followed by the Shock Index.

Considering the rapid progression from organ dysfunction to death in sepsis patients, as well as the difficulty establishing a sepsis diagnosis at triage,²³ providers must quickly identify patients at increased risk of poor outcomes when they present to the ED. Sepsis alerts often are built using SIRS criteria,²⁷ including the one used for sepsis surveillance at UCSF since 2012,²² but the white blood cell count criterion is subject to a laboratory lag and could lead to a delay in identification. Implementation of a point-of-care bedside score alert that uses readily available clinical data could allow providers to identify patients at greatest risk of poor outcomes immediately at ED presentation and triage, which motivated us to explore the predictive performance of qSOFA, the Shock Index, and NEWS2.

Our study is the first to provide a head-to-head comparison of the predictive performance of qSOFA, the Shock Index, and NEWS2, three easy-to-calculate bedside risk scores that use EHR data collected among patients with suspected sepsis. The Sepsis-3 guidelines recommend qSOFA to quickly identify non-ICU patients at greatest risk of poor outcomes because the measure exhibited predictive performance similar to the more extensive SOFA score outside the ICU.^16,23 Although some studies have confirmed qSOFA’s high predictive performance,^28-31 our test characteristics and AUROC findings are in line with other published analyses.^4,6,10,17 The UK National Health Service is using NEWS2 to screen for patients at risk of poor outcomes from sepsis. Several analyses that assessed the predictive ability of NEWS have reported estimates in line with our findings.^4,10,32 The Shock Index was introduced in 1967 and provided a metric to evaluate hemodynamic stability based on heart rate and systolic blood pressure.³³ The Shock Index has been studied in several contexts, including sepsis,³⁴ and studies show that a sustained Shock Index is associated with increased odds of vasopressor administration, higher prevalence of hyperlactatemia, and increased risk of poor outcomes in the ICU.^13,14

For our study, we were particularly interested in exploring how the Shock Index would compare with more frequently used severity scores such as qSOFA and NEWS2 among patients with suspected sepsis, given the simplicity of its calculation and the easy availability of required data. In our cohort of 23,837 patients, only 159 people had missing blood pressure and only 71 had omitted heart rate. In contrast, both qSOFA and NEWS2 include an assessment of level of consciousness that can be subject to variability in assessment methods and EHR documentation across institutions.¹¹ In our cohort, GCS within 30 minutes of ED presentation was missing in 72 patients, which could have led to incomplete calculation of qSOFA and NEWS2 if a missing value was not actually within normal limits.

Several investigations relate qSOFA to NEWS but few compare qSOFA with the newer NEWS2, and even fewer evaluate the Shock Index with any of these scores.^{10,11,18,29,35-37} In general, studies have shown that NEWS exhibits a higher AUROC for predicting mortality, sepsis with organ dysfunction, and ICU admission, often as a composite outcome.^{4,11,18,37,38} A handful of studies compare the Shock Index to SIRS; however, little has been done to compare the Shock Index to qSOFA or NEWS2, scores that have been used specifically for sepsis and might be more predictive of poor outcomes than SIRS.³³ In our study, the Shock Index had a higher AUROC than either qSOFA or NEWS2 for predicting in-hospital mortality and ED-to-ICU admission measured as separate outcomes and as a composite outcome using standard cut-points for these scores.

When selecting a severity score to apply in an institution, it is important to carefully evaluate the score’s test characteristics, in addition to considering the availability of reliable data. Tests with high sensitivity and NPV for the population being studied can be useful to rule out disease or risk of poor outcome, while tests with high specificity and PPV can be useful to rule in disease or risk of poor outcome.³⁹ When considering specificity, qSOFA’s performance was superior to the Shock Index and NEWS2 in our study, but a small percentage of the population was identified using a cut-point of qSOFA ≥2. If we used qSOFA and applied this standard cut-point at our institution, we could be confident that those identified were at increased risk, but we would miss a significant number of patients who would experience a poor outcome. When considering sensitivity, performance of NEWS2 was superior to qSOFA and the Shock Index in our study, but one-half of the population was identified using a cut-point of NEWS2 ≥5. If we were to apply this standard NEWS2 cut-point at our institution, we would assume that one-half of our population was at risk, which might drive resource use towards patients who will not experience a poor outcome. Although none of the scores exhibited a robust AUROC measure, the Shock Index had the highest AUROC for in-hospital mortality and ED-to-ICU admission when using the standard binary cut-point, and its sensitivity and specificity is between that of qSOFA and NEWS2, potentially making it a score to use in settings where qSOFA and NEWS2 score components, such as altered mentation, are not reliably collected. Finally, our sensitivity analysis varying the binary cut-point of each score within our population demonstrated that the standard cut-points might not be as useful within a specific population and might need to be tailored for implementation, balancing sensitivity, specificity, PPV, and NPV to meet local priorities and ICU capacity.

Our study has limitations. It is a single-center, retrospective analysis, factors that could reduce generalizability. However, it does include a large and diverse patient population spanning several years. Missing GCS data could have affected the predictive ability of qSOFA and NEWS2 in our cohort. We could not reliably perform imputation of GCS because of the high missingness and therefore we assumed missing was normal, as was done in the Sepsis-3 derivation studies.¹⁶ Previous studies have attempted to impute GCS and have not observed improved performance of qSOFA to predict mortality.⁴⁰ Because manually collected variables such as GCS are less reliably documented in the EHR, there might be limitations in their use for triage risk scores.

Although the current analysis focused on the predictive performance of qSOFA, the Shock Index, and NEWS2 at triage, performance of these scores could affect the ED team’s treatment decisions before handoff to the hospitalist team and the expected level of care the patient will receive after in-patient admission. These tests also have the advantage of being easy to calculate at the bedside over time, which could provide an objective assessment of longitudinal predicted prognosis. Future work should assess the longitudinal performance of each of these scores among those with suspected sepsis and to determine the impact using these scores would have on clinical and resource utilization outcomes.

Local priorities should drive selection of a screening tool, balancing sensitivity, specificity, PPV, and NPV to achieve the institution’s goals. qSOFA, Shock Index, and NEWS2 are risk stratification tools that can be easily implemented at ED triage using data available at the bedside. Although none of these scores performed strongly when comparing AUROCs, qSOFA was highly specific for identifying patients with poor outcomes, and NEWS2 was the most sensitive for ruling out those at high risk among patients with suspected sepsis. The Shock Index exhibited a sensitivity and specificity that fell between qSOFA and NEWS2 and also might be considered to identify those at increased risk, given its ease of implementation, particularly in settings where altered mentation is unreliably or inconsistently documented.

The authors thank the UCSF Division of Hospital Medicine Data Core for their assistance with data acquisition.

Sepsis is the leading cause of in-hospital mortality in the United States.¹ Sepsis is present on admission in 85% of cases, and each hour delay in antibiotic treatment is associated with 4% to 7% increased odds of mortality.^2,3 Prompt identification and treatment of sepsis is essential for reducing morbidity and mortality, but identifying sepsis during triage is challenging.²

Risk stratification scores that rely solely on data readily available at the bedside have been developed to quickly identify those at greatest risk of poor outcomes from sepsis in real time. The quick Sequential Organ Failure Assessment (qSOFA) score, the National Early Warning System (NEWS2), and the Shock Index are easy-to-calculate measures that use routinely collected clinical data that are not subject to laboratory delay. These scores can be incorporated into electronic health record (EHR)-based alerts and can be calculated longitudinally to track the risk of poor outcomes over time. qSOFA was developed to quantify patient risk at bedside in non-intensive care unit (ICU) settings, but there is no consensus about its ability to predict adverse outcomes such as mortality and ICU admission.^4-6 The United Kingdom’s National Health Service uses NEWS2 to identify patients at risk for sepsis.⁷ NEWS has been shown to have similar or better sensitivity in identifying poorer outcomes in sepsis patients compared with systemic inflammatory response syndrome (SIRS) criteria and qSOFA.^4,8-11 However, since the latest update of NEWS2 in 2017, there has been little study of its predictive ability. The Shock Index is a simple bedside score (heart rate divided by systolic blood pressure) that was developed to detect changes in cardiovascular performance before systemic shock onset. Although it was not developed for infection and has not been regularly applied in the sepsis literature, the Shock Index might be useful for identifying patients at increased risk of poor outcomes. Patients with higher and sustained Shock Index scores are more likely to experience morbidity, such as hyperlactatemia, vasopressor use, and organ failure, and also have an increased risk of mortality.^12-14

Although the predictive abilities of these bedside risk stratification scores have been assessed individually using standard binary cut-points, the comparative performance of qSOFA, the Shock Index, and NEWS2 has not been evaluated in patients presenting to an emergency department (ED) with suspected sepsis. Our objective was to provide a head-to-head comparison of the test characteristics of qSOFA, the Shock Index, and NEWS2 calculated at ED triage for predicting in-hospital mortality and ED-to-ICU admission in patients with suspected sepsis to help health systems and providers select screening measures.

Design and Setting

We conducted a retrospective cohort study of ED patients who presented with suspected sepsis to the University of California San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights between June 1, 2012, and December 31, 2018. Our institution is a 785-bed academic teaching hospital with approximately 30,000 ED encounters per year. The study was approved with a waiver of informed consent by the UCSF Human Research Protection Program.

Participants

We use an Epic-based EHR platform (Epic 2017, Epic Systems Corporation) for clinical care, which was implemented on June 1, 2012. All data elements were obtained from Clarity, the relational database that stores Epic’s inpatient data. The study included encounters for patients age ≥18 years who had blood cultures ordered within 24 hours of ED presentation and administration of intravenous antibiotics within 24 hours. Repeat encounters were treated independently in our analysis.

Outcomes and Measures

We compared the ability of qSOFA, the Shock Index, and NEWS2 to predict in-hospital mortality and admission to the ICU from the ED (ED-to-ICU admission). We used the most abnormal vital signs and clinical assessments gathered within 30 minutes of ED presentation to identify patients who were qSOFA-positive, Shock Index-positive, and NEWS2-positive based on standard cut-points of risk. Data elements used to calculate qSOFA, Shock Index, and NEWS2 included blood pressure, heart rate, respiratory rate, Glasgow Coma Scale (GCS) score, oxygen saturation, requirement for supplemental oxygen, and temperature (Table 1). Patients were considered positive if they had a qSOFAscore ≥2, Shock Index of >0.7, and NEWS2 ≥5 based on triage vital signs.^7,15,16 We considered patients to have altered mental status, a criterion used for NEWS2, if they had a GCS score <15 instead of using the “alert, verbal, confusion, pain, unresponsive” scale, which is not captured in our EHR, a method that has been used in earlier studies.^17,18 Missing assessments were considered normal. Although our primary analysis focused on the scores calculated within 30 minutes of ED presentation, we performed a sensitivity analysis examining scores calculated within 1 hour of ED presentation in the event of a delay in gathering triage vital sign data.

We compared demographic and clinical characteristics of patients who were positive for qSOFA, the Shock Index, and NEWS2. Demographic data were extracted from the EHR and included primary language, age, sex, and insurance status. All International Classification of Diseases (ICD)-9/10 diagnosis codes were pulled from Clarity billing tables. We used the Elixhauser comorbidity groupings¹⁹ of ICD-9/10 codes present on admission to identify preexisting comorbidities and underlying organ dysfunction. To estimate burden of comorbid illnesses, we calculated the validated van Walraven comorbidity index,²⁰ which provides an estimated risk of in-hospital death based on documented Elixhauser comorbidities. Admission level of care (acute, stepdown, or intensive care) was collected for inpatient admissions to assess initial illness severity.²¹ We also evaluated discharge disposition and in-hospital mortality. Index blood culture results were collected, and dates and timestamps of mechanical ventilation, fluid, vasopressor, and antibiotic administration were obtained for the duration of the encounter.

UCSF uses an automated, real-time, algorithm-based severe sepsis alert that is triggered when a patient meets ≥2 SIRS criteria and again when the patient meets severe sepsis or septic shock criteria (ie, ≥2 SIRS criteria in addition to end-organ dysfunction and/or fluid nonresponsive hypotension). This sepsis screening alert was in use for the duration of our study.²²

Statistical Analysis

We performed a subgroup analysis among those who were diagnosed with sepsis, according to the 2016 Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria. Sepsis is defined as a change in Sequential Organ Failure Assessment (SOFA) score of ≥2 points within the first 48 hours.²³ Additionally, patients meeting Sepsis-3 criteria needed to (1) receive ≥4 days of sequential antibiotic therapy or experience death or discharge to hospice before 4 days of antibiotic therapy or (2) have a validated sepsis discharge billing code. These parameters were added to increase the specificity of our sample.²⁴

All statistical analyses were conducted using Stata 14 (StataCorp). We summarized differences in demographic and clinical characteristics among the populations meeting each severity score but elected not to conduct hypothesis testing because patients could be positive for one or more scores. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each score to predict in-hospital mortality and ED-to-ICU admission. To allow comparison with other studies, we also created a composite outcome of either in-hospital mortality or ED-to-ICU admission. To assess score discrimination to predict in-hospital mortality and ED-to-ICU admission, we calculated the area under the receiver operating characteristic curve (AUROC) along with asymptotic normal 95% CI using the “roctab” command considering a binary cut-point, as well as the full range of scores measured in the cohort. The AUROC range from 0.50 to 1.00 and a score in the 0.70 to 0.80 range can be considered fair.²⁵ We assessed significant differences between severity score AUROCs using the DeLong method²⁶ implemented through Stata 14’s “roccomp” command. As a sensitivity analysis, we explored whether the standard cut-points for qSOFA, the Shock Index, and NEWS2 provided the highest AUROC in our population by calculating test characteristics for several score cut-points.

Within our sample 23,837 ED patients had blood cultures ordered within 24 hours of ED presentation and were considered to have suspected sepsis. The mean age of the cohort was 60.8 years, and 1,612 (6.8%) had positive blood cultures. A total of 12,928 patients (54.2%) were found to have sepsis. We documented 1,427 in-hospital deaths (6.0%) and 3,149 (13.2%) ED-to-ICU admissions. At ED triage 1,921 (8.1%) were qSOFA-positive, 4,273 (17.9%) were Shock Index-positive, and 11,832 (49.6%) were NEWS2-positive. At ED triage, blood pressure, heart rate, respiratory rate, and oxygen saturated were documented in >99% of patients, 93.5% had temperature documented, and 28.5% had GCS recorded. If the window of assessment was widened to 1 hour, GCS was only documented among 44.2% of those with suspected sepsis.

Demographic Characteristics and Clinical Course

We identified significant differences when comparing demographic and clinical characteristics among patients who scored positive for the three severity measures at triage (Table 2). Although no hypothesis testing was conducted because patients could meet one or more scores, qSOFA-positive patients were older (median 70, 66, and 64 years, respectively), more likely to have Medicare as the primary payor (67.6% vs 59.7% vs 56.6%), to have chronic renal failure (26.1%, 23.1%, and 23.3%, respectively), to have a greater degree of underlying comorbidities based on the van Walraven Comorbidity Index (median 15, 12, and 11, respectively), and to be admitted to the ICU from the ED (48.1%, 36.3%, and 21.0%, respectively) compared with those positive for the Shock Index or NEWS2.

qSOFA-positive patients received antibiotics more quickly than those who were Shock Index-positive or NEWS2-positive (median 1.5, 1.8, and 2.8 hours after admission, respectively). In addition, those who were qSOFA-positive were more likely to have a positive blood culture (10.9%, 9.4%, and 8.5%, respectively) and to receive an EHR-based diagnosis of sepsis (77.0%, 69.6%, and 60.9%, respectively) than those who were Shock Index- or NEWS2-positive. Those who were qSOFA-positive also were more likely to be mechanically ventilated during their hospital stay (25.4%, 19.2%, and 10.8%, respectively) and to receive vasopressors (33.5%, 22.5%, and 12.2%, respectively). In-hospital mortality also was more common among those who were qSOFA-positive at triage (23.4%, 15.3%, and 9.2%, respectively).

Because both qSOFA and NEWS2 incorporate GCS, we explored baseline characteristics of patients with GCS documented at triage (n = 6,794). These patients were older (median age 63 and 61 years, P < .0001), more likely to be male (54.9% and 53.4%, P = .0031), more likely to have renal failure (22.8% and 20.1%, P < .0001), more likely to have liver disease (14.2% and 12.8%, P = .006), had a higher van Walraven comorbidity score on presentation (median 10 and 8, P < .0001), and were more likely to go directly to the ICU from the ED (20.2% and 10.6%, P < .0001). However, among the 6,397 GCS scores documented at triage, only 1,579 (24.7%) were abnormal.

Test Characteristics of qSOFA, Shock Index, and NEWS2 for Predicting In-hospital Mortality and ED-to-ICU Admission

Among 23,837 patients with suspected sepsis, NEWS2 had the highest sensitivity for predicting in-hospital mortality (76.0%; 95% CI, 73.7%-78.2%) and ED-to-ICU admission (78.9%; 95% CI, 77.5%-80.4%) but had the lowest specificity for in-hospital mortality (52.0%; 95% CI, 51.4%-52.7%) and for ED-to-ICU admission (54.8%; 95% CI, 54.1%-55.5%) (Table 3). qSOFA had the lowest sensitivity for in-hospital mortality (31.5%; 95% CI, 29.1%-33.9%) and ED-to-ICU admission (29.3%; 95% CI, 27.7%-30.9%) but the highest specificity for in-hospital mortality (93.4%; 95% CI, 93.1%-93.8%) and ED-to-ICU admission (95.2%; 95% CI, 94.9%-95.5%). The Shock Index had a sensitivity that fell between qSOFA and NEWS2 for in-hospital mortality (45.8%; 95% CI, 43.2%-48.5%) and ED-to-ICU admission (49.2%; 95% CI, 47.5%-51.0%). The specificity of the Shock Index also was between qSOFA and NEWS2 for in-hospital mortality (83.9%; 95% CI, 83.4%-84.3%) and ED-to-ICU admission (86.8%; 95% CI, 86.4%-87.3%). All three scores exhibited relatively low PPV, ranging from 9.2% to 23.4% for in-hospital mortality and 21.0% to 48.0% for ED-to-ICU triage. Conversely, all three scores exhibited relatively high NPV, ranging from 95.5% to 97.1% for in-hospital mortality and 89.8% to 94.5% for ED-to-ICU triage. The patterns in sensitivity and specificity for in-hospital mortality and ED-to-ICU admission were similar among the 12,928 patients who received an EHR-based sepsis diagnosis with the tests generally demonstrating lower specificities, higher PPVs, and lower NPVs (Table 3).

When considering a binary cutoff, the Shock Index exhibited the highest AUROC for in-hospital mortality (0.648; 95% CI, 0.635-0.662) and had a significantly higher AUROC than qSOFA (AUROC, 0.625; 95% CI, 0.612-0.637; P = .0005), but there was no difference compared with NEWS2 (AUROC, 0.640; 95% CI, 0.628-0.652; P = .2112). NEWS2 had a significantly higher AUROC than qSOFA for predicting in-hospital mortality (P = .0227). The Shock Index also exhibited the highest AUROC for ED-to-ICU admission (0.680; 95% CI, 0.617-0.689), which was significantly higher than the AUROC for qSOFA (P < .0001) and NEWS2 (P = 0.0151). NEWS2 had a significantly higher AUROC than qSOFA for predicting ED-to-ICU admission (P < .0001). Similar findings were seen in patients found to have sepsis. When considering the range of possible scores measured in our cohort, qSOFA and NEWS2 exhibited higher AUROCs for in-hospital mortality and ED-to-ICU admission than the Shock Index among patients with suspected infection and the subgroup with a sepsis diagnosis (Figure).

In this retrospective cohort study of 23,837 patients who presented to the ED with suspected sepsis, the standard qSOFA threshold was met least frequently, followed by the Shock Index and NEWS2. NEWS2 had the highest sensitivity but the lowest specificity for predicting in-hospital mortality and ED-to-ICU admission, making it a challenging bedside risk stratification scale for identifying patients at risk of poor clinical outcomes. When comparing predictive performance among the three scales, qSOFA had the highest specificity and the Shock Index had the highest AUROC for in-hospital mortality and ED-to-ICU admission in this cohort of patients with suspected sepsis. These trends in sensitivity, specificity, and AUROC were consistent among those who met EHR criteria for a sepsis diagnosis. In the analysis of the three scoring systems using all available cut-points, qSOFA and NEWS2 had the highest AUROCs, followed by the Shock Index.

Considering the rapid progression from organ dysfunction to death in sepsis patients, as well as the difficulty establishing a sepsis diagnosis at triage,²³ providers must quickly identify patients at increased risk of poor outcomes when they present to the ED. Sepsis alerts often are built using SIRS criteria,²⁷ including the one used for sepsis surveillance at UCSF since 2012,²² but the white blood cell count criterion is subject to a laboratory lag and could lead to a delay in identification. Implementation of a point-of-care bedside score alert that uses readily available clinical data could allow providers to identify patients at greatest risk of poor outcomes immediately at ED presentation and triage, which motivated us to explore the predictive performance of qSOFA, the Shock Index, and NEWS2.

Our study is the first to provide a head-to-head comparison of the predictive performance of qSOFA, the Shock Index, and NEWS2, three easy-to-calculate bedside risk scores that use EHR data collected among patients with suspected sepsis. The Sepsis-3 guidelines recommend qSOFA to quickly identify non-ICU patients at greatest risk of poor outcomes because the measure exhibited predictive performance similar to the more extensive SOFA score outside the ICU.^16,23 Although some studies have confirmed qSOFA’s high predictive performance,^28-31 our test characteristics and AUROC findings are in line with other published analyses.^4,6,10,17 The UK National Health Service is using NEWS2 to screen for patients at risk of poor outcomes from sepsis. Several analyses that assessed the predictive ability of NEWS have reported estimates in line with our findings.^4,10,32 The Shock Index was introduced in 1967 and provided a metric to evaluate hemodynamic stability based on heart rate and systolic blood pressure.³³ The Shock Index has been studied in several contexts, including sepsis,³⁴ and studies show that a sustained Shock Index is associated with increased odds of vasopressor administration, higher prevalence of hyperlactatemia, and increased risk of poor outcomes in the ICU.^13,14

For our study, we were particularly interested in exploring how the Shock Index would compare with more frequently used severity scores such as qSOFA and NEWS2 among patients with suspected sepsis, given the simplicity of its calculation and the easy availability of required data. In our cohort of 23,837 patients, only 159 people had missing blood pressure and only 71 had omitted heart rate. In contrast, both qSOFA and NEWS2 include an assessment of level of consciousness that can be subject to variability in assessment methods and EHR documentation across institutions.¹¹ In our cohort, GCS within 30 minutes of ED presentation was missing in 72 patients, which could have led to incomplete calculation of qSOFA and NEWS2 if a missing value was not actually within normal limits.

Several investigations relate qSOFA to NEWS but few compare qSOFA with the newer NEWS2, and even fewer evaluate the Shock Index with any of these scores.^{10,11,18,29,35-37} In general, studies have shown that NEWS exhibits a higher AUROC for predicting mortality, sepsis with organ dysfunction, and ICU admission, often as a composite outcome.^{4,11,18,37,38} A handful of studies compare the Shock Index to SIRS; however, little has been done to compare the Shock Index to qSOFA or NEWS2, scores that have been used specifically for sepsis and might be more predictive of poor outcomes than SIRS.³³ In our study, the Shock Index had a higher AUROC than either qSOFA or NEWS2 for predicting in-hospital mortality and ED-to-ICU admission measured as separate outcomes and as a composite outcome using standard cut-points for these scores.

When selecting a severity score to apply in an institution, it is important to carefully evaluate the score’s test characteristics, in addition to considering the availability of reliable data. Tests with high sensitivity and NPV for the population being studied can be useful to rule out disease or risk of poor outcome, while tests with high specificity and PPV can be useful to rule in disease or risk of poor outcome.³⁹ When considering specificity, qSOFA’s performance was superior to the Shock Index and NEWS2 in our study, but a small percentage of the population was identified using a cut-point of qSOFA ≥2. If we used qSOFA and applied this standard cut-point at our institution, we could be confident that those identified were at increased risk, but we would miss a significant number of patients who would experience a poor outcome. When considering sensitivity, performance of NEWS2 was superior to qSOFA and the Shock Index in our study, but one-half of the population was identified using a cut-point of NEWS2 ≥5. If we were to apply this standard NEWS2 cut-point at our institution, we would assume that one-half of our population was at risk, which might drive resource use towards patients who will not experience a poor outcome. Although none of the scores exhibited a robust AUROC measure, the Shock Index had the highest AUROC for in-hospital mortality and ED-to-ICU admission when using the standard binary cut-point, and its sensitivity and specificity is between that of qSOFA and NEWS2, potentially making it a score to use in settings where qSOFA and NEWS2 score components, such as altered mentation, are not reliably collected. Finally, our sensitivity analysis varying the binary cut-point of each score within our population demonstrated that the standard cut-points might not be as useful within a specific population and might need to be tailored for implementation, balancing sensitivity, specificity, PPV, and NPV to meet local priorities and ICU capacity.

Our study has limitations. It is a single-center, retrospective analysis, factors that could reduce generalizability. However, it does include a large and diverse patient population spanning several years. Missing GCS data could have affected the predictive ability of qSOFA and NEWS2 in our cohort. We could not reliably perform imputation of GCS because of the high missingness and therefore we assumed missing was normal, as was done in the Sepsis-3 derivation studies.¹⁶ Previous studies have attempted to impute GCS and have not observed improved performance of qSOFA to predict mortality.⁴⁰ Because manually collected variables such as GCS are less reliably documented in the EHR, there might be limitations in their use for triage risk scores.

Although the current analysis focused on the predictive performance of qSOFA, the Shock Index, and NEWS2 at triage, performance of these scores could affect the ED team’s treatment decisions before handoff to the hospitalist team and the expected level of care the patient will receive after in-patient admission. These tests also have the advantage of being easy to calculate at the bedside over time, which could provide an objective assessment of longitudinal predicted prognosis. Future work should assess the longitudinal performance of each of these scores among those with suspected sepsis and to determine the impact using these scores would have on clinical and resource utilization outcomes.

Local priorities should drive selection of a screening tool, balancing sensitivity, specificity, PPV, and NPV to achieve the institution’s goals. qSOFA, Shock Index, and NEWS2 are risk stratification tools that can be easily implemented at ED triage using data available at the bedside. Although none of these scores performed strongly when comparing AUROCs, qSOFA was highly specific for identifying patients with poor outcomes, and NEWS2 was the most sensitive for ruling out those at high risk among patients with suspected sepsis. The Shock Index exhibited a sensitivity and specificity that fell between qSOFA and NEWS2 and also might be considered to identify those at increased risk, given its ease of implementation, particularly in settings where altered mentation is unreliably or inconsistently documented.

The authors thank the UCSF Division of Hospital Medicine Data Core for their assistance with data acquisition.

The U.S. Food and Drug Administration (FDA) aims to update the labeling on all statins to remove the drugs’ blanket contraindication in all pregnant patients, the agency has announced. The change should reinforce for both physicians and patients that statin use in women with unrecognized pregnancy is unlikely to be harmful, it said.

“Because the benefits of statins may include prevention of serious or potentially fatal events in a small group of very high-risk pregnant patients, contraindicating these drugs in all pregnant women is not appropriate.”

The revision should emphasize for clinicians “that statins are safe to prescribe in patients who can become pregnant and help them reassure patients with unintended statin exposure in early pregnancy,” the FDA explained.

Removal of the broadly worded contraindication should “enable health care professionals and patients to make individual decisions about benefit and risk, especially for those at very high risk of heart attack or stroke." That includes women with homozygous familial hypercholesterolemia and those who are prescribed statins for secondary prevention, the agency said.

Clinicians “should discontinue statin therapy in most pregnant patients, or they can consider the ongoing therapeutic needs of the individual patient, particularly those at very high risk for cardiovascular events during pregnancy. Because of the chronic nature of cardiovascular disease, treatment of hyperlipidemia is not generally necessary during pregnancy.”

A version of this article first appeared on Medscape.com.

The U.S. Food and Drug Administration (FDA) aims to update the labeling on all statins to remove the drugs’ blanket contraindication in all pregnant patients, the agency has announced. The change should reinforce for both physicians and patients that statin use in women with unrecognized pregnancy is unlikely to be harmful, it said.

“Because the benefits of statins may include prevention of serious or potentially fatal events in a small group of very high-risk pregnant patients, contraindicating these drugs in all pregnant women is not appropriate.”

The revision should emphasize for clinicians “that statins are safe to prescribe in patients who can become pregnant and help them reassure patients with unintended statin exposure in early pregnancy,” the FDA explained.

Removal of the broadly worded contraindication should “enable health care professionals and patients to make individual decisions about benefit and risk, especially for those at very high risk of heart attack or stroke." That includes women with homozygous familial hypercholesterolemia and those who are prescribed statins for secondary prevention, the agency said.

Clinicians “should discontinue statin therapy in most pregnant patients, or they can consider the ongoing therapeutic needs of the individual patient, particularly those at very high risk for cardiovascular events during pregnancy. Because of the chronic nature of cardiovascular disease, treatment of hyperlipidemia is not generally necessary during pregnancy.”

A version of this article first appeared on Medscape.com.

The U.S. Food and Drug Administration (FDA) aims to update the labeling on all statins to remove the drugs’ blanket contraindication in all pregnant patients, the agency has announced. The change should reinforce for both physicians and patients that statin use in women with unrecognized pregnancy is unlikely to be harmful, it said.

“Because the benefits of statins may include prevention of serious or potentially fatal events in a small group of very high-risk pregnant patients, contraindicating these drugs in all pregnant women is not appropriate.”

The revision should emphasize for clinicians “that statins are safe to prescribe in patients who can become pregnant and help them reassure patients with unintended statin exposure in early pregnancy,” the FDA explained.

Removal of the broadly worded contraindication should “enable health care professionals and patients to make individual decisions about benefit and risk, especially for those at very high risk of heart attack or stroke." That includes women with homozygous familial hypercholesterolemia and those who are prescribed statins for secondary prevention, the agency said.

Clinicians “should discontinue statin therapy in most pregnant patients, or they can consider the ongoing therapeutic needs of the individual patient, particularly those at very high risk for cardiovascular events during pregnancy. Because of the chronic nature of cardiovascular disease, treatment of hyperlipidemia is not generally necessary during pregnancy.”

A version of this article first appeared on Medscape.com.

New COVID-19 cases in children rose for the third consecutive week, but first vaccinations among adolescents made a slight recovery after several weeks of mostly steady decline, new data show.

Children aged 12-15 years represented 13.5% of all first vaccinations received during the 2 weeks ending July 19, compared with 11.5% for the 2 weeks ending July 12, marking the first increase since the end of May. First vaccinations in 16- and 17-year-olds, who make up a much smaller share of the U.S. population, also went up, topping 5%, the Centers for Disease Control and Prevention said in its COVID Data Tracker.

The total number of vaccine initiations was almost 250,000 for the week ending July 19, after dropping to a low of 201,000 the previous week. Before that, first vaccinations had fallen in 5 of the previous 6 weeks, going from 1.4 million on May 24 to 307,000 on July 5, the CDC said.

New cases of COVID-19, unfortunately, continued to follow the trend among the larger population: As of July 15, weekly cases in children were up by 179% since dropping to 8,400 on June 24, the American Academy of Pediatrics and the Children’s Hospital Association said in a joint report. The 23,551 new cases in children for the week ending July 15 were 15.9% of all cases reported.

With those new cases, the total number of children infected with COVID-19 comes to almost 4.1 million since the start of the pandemic, the AAP and CHA said. The CDC data indicate that just over 5.35 million children aged 12-15 years and 3.53 million 16- and 17-year-olds have received at least one dose of the COVID-19 vaccine and that 6.8 million children aged 12-17 are fully vaccinated.

Fully vaccinated children represent 26.4% of all 12- to 15-year-olds and 38.3% of the 16- 17-year-olds as of July 19. The corresponding numbers for those who have received at least one dose are 35.2% (ages 12-15) and 46.8% (16-17), the CDC said.

The AAP recently recommended in-person learning with universal masking in schools this fall “because a significant portion of the student population is not yet eligible for vaccines. ... Many schools will not have a system to monitor vaccine status of students, teachers and staff, and some communities overall have low vaccination uptake where the virus may be circulating more prominently.”

[email protected]

New COVID-19 cases in children rose for the third consecutive week, but first vaccinations among adolescents made a slight recovery after several weeks of mostly steady decline, new data show.

Children aged 12-15 years represented 13.5% of all first vaccinations received during the 2 weeks ending July 19, compared with 11.5% for the 2 weeks ending July 12, marking the first increase since the end of May. First vaccinations in 16- and 17-year-olds, who make up a much smaller share of the U.S. population, also went up, topping 5%, the Centers for Disease Control and Prevention said in its COVID Data Tracker.

The total number of vaccine initiations was almost 250,000 for the week ending July 19, after dropping to a low of 201,000 the previous week. Before that, first vaccinations had fallen in 5 of the previous 6 weeks, going from 1.4 million on May 24 to 307,000 on July 5, the CDC said.

New cases of COVID-19, unfortunately, continued to follow the trend among the larger population: As of July 15, weekly cases in children were up by 179% since dropping to 8,400 on June 24, the American Academy of Pediatrics and the Children’s Hospital Association said in a joint report. The 23,551 new cases in children for the week ending July 15 were 15.9% of all cases reported.

With those new cases, the total number of children infected with COVID-19 comes to almost 4.1 million since the start of the pandemic, the AAP and CHA said. The CDC data indicate that just over 5.35 million children aged 12-15 years and 3.53 million 16- and 17-year-olds have received at least one dose of the COVID-19 vaccine and that 6.8 million children aged 12-17 are fully vaccinated.

Fully vaccinated children represent 26.4% of all 12- to 15-year-olds and 38.3% of the 16- 17-year-olds as of July 19. The corresponding numbers for those who have received at least one dose are 35.2% (ages 12-15) and 46.8% (16-17), the CDC said.

The AAP recently recommended in-person learning with universal masking in schools this fall “because a significant portion of the student population is not yet eligible for vaccines. ... Many schools will not have a system to monitor vaccine status of students, teachers and staff, and some communities overall have low vaccination uptake where the virus may be circulating more prominently.”

[email protected]

New COVID-19 cases in children rose for the third consecutive week, but first vaccinations among adolescents made a slight recovery after several weeks of mostly steady decline, new data show.

Children aged 12-15 years represented 13.5% of all first vaccinations received during the 2 weeks ending July 19, compared with 11.5% for the 2 weeks ending July 12, marking the first increase since the end of May. First vaccinations in 16- and 17-year-olds, who make up a much smaller share of the U.S. population, also went up, topping 5%, the Centers for Disease Control and Prevention said in its COVID Data Tracker.

The total number of vaccine initiations was almost 250,000 for the week ending July 19, after dropping to a low of 201,000 the previous week. Before that, first vaccinations had fallen in 5 of the previous 6 weeks, going from 1.4 million on May 24 to 307,000 on July 5, the CDC said.

New cases of COVID-19, unfortunately, continued to follow the trend among the larger population: As of July 15, weekly cases in children were up by 179% since dropping to 8,400 on June 24, the American Academy of Pediatrics and the Children’s Hospital Association said in a joint report. The 23,551 new cases in children for the week ending July 15 were 15.9% of all cases reported.

With those new cases, the total number of children infected with COVID-19 comes to almost 4.1 million since the start of the pandemic, the AAP and CHA said. The CDC data indicate that just over 5.35 million children aged 12-15 years and 3.53 million 16- and 17-year-olds have received at least one dose of the COVID-19 vaccine and that 6.8 million children aged 12-17 are fully vaccinated.

Fully vaccinated children represent 26.4% of all 12- to 15-year-olds and 38.3% of the 16- 17-year-olds as of July 19. The corresponding numbers for those who have received at least one dose are 35.2% (ages 12-15) and 46.8% (16-17), the CDC said.

The AAP recently recommended in-person learning with universal masking in schools this fall “because a significant portion of the student population is not yet eligible for vaccines. ... Many schools will not have a system to monitor vaccine status of students, teachers and staff, and some communities overall have low vaccination uptake where the virus may be circulating more prominently.”

[email protected]

A 6-year-old boy was seen in the hospital in consultation for a 3-week history of suspected cellulitis of the right ear. Drainage from the right ear was refractory to treatment with a 7-day course of cephalexin 15 mL po bid of 250 mg/5 mL solution and clindamycin 24.4 mL po tid of 75 mg/5 mL solution. Treatment was followed by admission to the hospital for treatment with intravenous (IV) cefazolin 1000 mg q6h and IV vancomycin 825 mg q6h for 1 week.

The patient had a significant past medical history for asthma, allergic rhinitis, and severe atopic dermatitis that had been treated with methotrexate 10 mg per week for 6 months beginning when the child was 5 years of age. When the methotrexate proved to be ineffective, the patient was started on Aquaphor and mometasone 0.1% ointment. A 6-month trial of these agents failed as well.

Physical examination revealed that the right ear and skin around it were edematous, erythematous, pruritic, and tender. There was also purulent drainage coming from the ear (FIGURE 1).

WHAT IS YOUR DIAGNOSIS?
HOW WOULD YOU TREAT THIS PATIENT?

Diagnosis: Infectious eczematoid dermatitis

The patient was referred to a dermatologist after seeing an ear, nose, and throat (ENT) specialist who made the diagnosis of otitis externa when the rash failed to respond to topical and systemic antibiotics. The patient’s tender, pruritic, oozing, edematous eruption was recognized as an infectious eczematoid dermatitis (IED).

The ears, nose, and face are predominantly involved in cases of infectious eczematoid dermatitis in the pediatric population, while the lower extremities are predominantly involved in adults.

Although it is not an uncommon condition, IED may be underrecognized. It accounted for 2.9% of admissions to a ­dermatology-run inpatient service between 2000 and 2010.¹ IED results from cutaneous sensitization to purulent drainage secondary to acute otitis externa or another primary infection.² In fact, cultures from the purulent drainage in this patient grew methicillin-­resistant Staphylococcus aureus. The patient’s right otitis externa drainage may have been associated with the previous history of atopic dermatitis. Atopic dermatitis is associated with an increased risk of skin infections due to decreased inflammatory mediators (defensins).

Cellulitis and herpes zoster oticus are part of the differential

The differential diagnosis in this case includes bacterial cellulitis, acute otitis media, and herpes zoster oticus.

Bacterial cellulitis manifests with erythema, edema, and tenderness with blistering when associated with bullous impetigo rather than pruritus. The clinical appearance of the patient’s diffuse, weeping, edematous external ear, the lack of response to guided antibiotic therapy, and the pruritus experienced by the patient argue against the diagnosis of bacterial cellulitis.

Acute otitis media, like otitis externa, produces ear discharge usually associated with significant pain. Thus, it is important when working through the differential to define the source of the ear discharge. In this case, a consultation with an ENT specialist confirmed that there was an intact tympanic membrane with no middle ear involvement, ruling out the diagnosis of acute otitis media.

Continue to: Herpes zoster oticus

Herpes zoster oticus. The absence of grouped vesicles at any point during the eruption, itching rather than pain, and negative viral culture and polymerase chain reaction studies for herpes simplex and varicella zoster virus excluded the diagnosis of herpes zoster oticus.

Diagnostic criteria were met

This case was compatible with the characterizations of IED as initially described by Engman³ in 1902 and further detailed by Sutton,⁴ who provided the following criteria for diagnosis:

• an initial eczematous or pustular lesion
• extension peripherally by autoinoculation
• an absence of central clearing
• Staphylococcus on culture of the initial lesion
• a history of infection.

Case reports have added to our understanding of the mechanism of autosensitization of surrounding skin.⁵ Yamany and Schwartz have proposed the diagnostic criteria summarized in the TABLE.²

Age factors into location. The ears, nose, and face are predominantly involved in cases of IED in the pediatric population, while the lower extremities are predominantly involved in adults.⁶ Laboratory tests and imaging may aid in excluding other potential diagnoses or complications, but the diagnosis remains clinical and requires the clinician to avoid jumping to the conclusion that every moist, erythematous crusting eruption is purely infectious in nature.

Tx and prevention hinge on a combination of antibiotics, steroids

The management of IED should be aimed at fighting the infection, eliminating the allergic contact dermatitis associated with infectious products, and improving barrier protection. Topical and/or systemic antibiotics guided by culture focus on killing bacteria. The allergic immune response is dampened by systemic steroids. Topical steroids, however, are difficult to utilize on moist, draining skin. In the case of otitis externa, a combination topical antibiotic and steroid otic drop can be utilized. As healing begins, emollients are applied to aid in skin repair.² Topical antibiotics containing neomycin or polymyxin should be avoided to eliminate the possibility of developing contact sensitivity to these agents.

Topical antibiotics containing neomycin or polymyxin should be avoided to eliminate the possibility of developing contact sensitivity to these agents.

For our patient, inpatient wound cultures demonstrated methicillin-resistant S aureus, and empiric treatment with IV cefepime and vancomycin was transitioned to IV clindamycin based on sensitivities and then transitioned to a 12-day course of oral clindamycin 150 mg bid. In addition, the patient received ciprofloxacin/dexamethasone otic drops 3 times/d to treat his otitis externa. After initiating prednisone 30 mg (1 mg/kg/d) for 10 days to cover the allergic component, the patient showed prompt clinical improvement. Gentle cleansing of the right ear with hypoallergenic soap and water followed by application of petrolatum ointment 4 times/d was used to promote healing and improve barrier function (FIGURE 2). The patient’s mother indicated during a follow-up call that the affected area had dramatically improved.

A 6-year-old boy was seen in the hospital in consultation for a 3-week history of suspected cellulitis of the right ear. Drainage from the right ear was refractory to treatment with a 7-day course of cephalexin 15 mL po bid of 250 mg/5 mL solution and clindamycin 24.4 mL po tid of 75 mg/5 mL solution. Treatment was followed by admission to the hospital for treatment with intravenous (IV) cefazolin 1000 mg q6h and IV vancomycin 825 mg q6h for 1 week.

The patient had a significant past medical history for asthma, allergic rhinitis, and severe atopic dermatitis that had been treated with methotrexate 10 mg per week for 6 months beginning when the child was 5 years of age. When the methotrexate proved to be ineffective, the patient was started on Aquaphor and mometasone 0.1% ointment. A 6-month trial of these agents failed as well.

Physical examination revealed that the right ear and skin around it were edematous, erythematous, pruritic, and tender. There was also purulent drainage coming from the ear (FIGURE 1).

WHAT IS YOUR DIAGNOSIS?
HOW WOULD YOU TREAT THIS PATIENT?

Diagnosis: Infectious eczematoid dermatitis

The patient was referred to a dermatologist after seeing an ear, nose, and throat (ENT) specialist who made the diagnosis of otitis externa when the rash failed to respond to topical and systemic antibiotics. The patient’s tender, pruritic, oozing, edematous eruption was recognized as an infectious eczematoid dermatitis (IED).

The ears, nose, and face are predominantly involved in cases of infectious eczematoid dermatitis in the pediatric population, while the lower extremities are predominantly involved in adults.

Although it is not an uncommon condition, IED may be underrecognized. It accounted for 2.9% of admissions to a ­dermatology-run inpatient service between 2000 and 2010.¹ IED results from cutaneous sensitization to purulent drainage secondary to acute otitis externa or another primary infection.² In fact, cultures from the purulent drainage in this patient grew methicillin-­resistant Staphylococcus aureus. The patient’s right otitis externa drainage may have been associated with the previous history of atopic dermatitis. Atopic dermatitis is associated with an increased risk of skin infections due to decreased inflammatory mediators (defensins).

Cellulitis and herpes zoster oticus are part of the differential

The differential diagnosis in this case includes bacterial cellulitis, acute otitis media, and herpes zoster oticus.

Bacterial cellulitis manifests with erythema, edema, and tenderness with blistering when associated with bullous impetigo rather than pruritus. The clinical appearance of the patient’s diffuse, weeping, edematous external ear, the lack of response to guided antibiotic therapy, and the pruritus experienced by the patient argue against the diagnosis of bacterial cellulitis.

Acute otitis media, like otitis externa, produces ear discharge usually associated with significant pain. Thus, it is important when working through the differential to define the source of the ear discharge. In this case, a consultation with an ENT specialist confirmed that there was an intact tympanic membrane with no middle ear involvement, ruling out the diagnosis of acute otitis media.

Continue to: Herpes zoster oticus

Herpes zoster oticus. The absence of grouped vesicles at any point during the eruption, itching rather than pain, and negative viral culture and polymerase chain reaction studies for herpes simplex and varicella zoster virus excluded the diagnosis of herpes zoster oticus.

Diagnostic criteria were met

This case was compatible with the characterizations of IED as initially described by Engman³ in 1902 and further detailed by Sutton,⁴ who provided the following criteria for diagnosis:

• an initial eczematous or pustular lesion
• extension peripherally by autoinoculation
• an absence of central clearing
• Staphylococcus on culture of the initial lesion
• a history of infection.

Case reports have added to our understanding of the mechanism of autosensitization of surrounding skin.⁵ Yamany and Schwartz have proposed the diagnostic criteria summarized in the TABLE.²

Age factors into location. The ears, nose, and face are predominantly involved in cases of IED in the pediatric population, while the lower extremities are predominantly involved in adults.⁶ Laboratory tests and imaging may aid in excluding other potential diagnoses or complications, but the diagnosis remains clinical and requires the clinician to avoid jumping to the conclusion that every moist, erythematous crusting eruption is purely infectious in nature.

Tx and prevention hinge on a combination of antibiotics, steroids

The management of IED should be aimed at fighting the infection, eliminating the allergic contact dermatitis associated with infectious products, and improving barrier protection. Topical and/or systemic antibiotics guided by culture focus on killing bacteria. The allergic immune response is dampened by systemic steroids. Topical steroids, however, are difficult to utilize on moist, draining skin. In the case of otitis externa, a combination topical antibiotic and steroid otic drop can be utilized. As healing begins, emollients are applied to aid in skin repair.² Topical antibiotics containing neomycin or polymyxin should be avoided to eliminate the possibility of developing contact sensitivity to these agents.

Topical antibiotics containing neomycin or polymyxin should be avoided to eliminate the possibility of developing contact sensitivity to these agents.

For our patient, inpatient wound cultures demonstrated methicillin-resistant S aureus, and empiric treatment with IV cefepime and vancomycin was transitioned to IV clindamycin based on sensitivities and then transitioned to a 12-day course of oral clindamycin 150 mg bid. In addition, the patient received ciprofloxacin/dexamethasone otic drops 3 times/d to treat his otitis externa. After initiating prednisone 30 mg (1 mg/kg/d) for 10 days to cover the allergic component, the patient showed prompt clinical improvement. Gentle cleansing of the right ear with hypoallergenic soap and water followed by application of petrolatum ointment 4 times/d was used to promote healing and improve barrier function (FIGURE 2). The patient’s mother indicated during a follow-up call that the affected area had dramatically improved.

A 6-year-old boy was seen in the hospital in consultation for a 3-week history of suspected cellulitis of the right ear. Drainage from the right ear was refractory to treatment with a 7-day course of cephalexin 15 mL po bid of 250 mg/5 mL solution and clindamycin 24.4 mL po tid of 75 mg/5 mL solution. Treatment was followed by admission to the hospital for treatment with intravenous (IV) cefazolin 1000 mg q6h and IV vancomycin 825 mg q6h for 1 week.

The patient had a significant past medical history for asthma, allergic rhinitis, and severe atopic dermatitis that had been treated with methotrexate 10 mg per week for 6 months beginning when the child was 5 years of age. When the methotrexate proved to be ineffective, the patient was started on Aquaphor and mometasone 0.1% ointment. A 6-month trial of these agents failed as well.

Physical examination revealed that the right ear and skin around it were edematous, erythematous, pruritic, and tender. There was also purulent drainage coming from the ear (FIGURE 1).

WHAT IS YOUR DIAGNOSIS?
HOW WOULD YOU TREAT THIS PATIENT?

Diagnosis: Infectious eczematoid dermatitis

The patient was referred to a dermatologist after seeing an ear, nose, and throat (ENT) specialist who made the diagnosis of otitis externa when the rash failed to respond to topical and systemic antibiotics. The patient’s tender, pruritic, oozing, edematous eruption was recognized as an infectious eczematoid dermatitis (IED).

The ears, nose, and face are predominantly involved in cases of infectious eczematoid dermatitis in the pediatric population, while the lower extremities are predominantly involved in adults.

Although it is not an uncommon condition, IED may be underrecognized. It accounted for 2.9% of admissions to a ­dermatology-run inpatient service between 2000 and 2010.¹ IED results from cutaneous sensitization to purulent drainage secondary to acute otitis externa or another primary infection.² In fact, cultures from the purulent drainage in this patient grew methicillin-­resistant Staphylococcus aureus. The patient’s right otitis externa drainage may have been associated with the previous history of atopic dermatitis. Atopic dermatitis is associated with an increased risk of skin infections due to decreased inflammatory mediators (defensins).

Cellulitis and herpes zoster oticus are part of the differential

The differential diagnosis in this case includes bacterial cellulitis, acute otitis media, and herpes zoster oticus.

Bacterial cellulitis manifests with erythema, edema, and tenderness with blistering when associated with bullous impetigo rather than pruritus. The clinical appearance of the patient’s diffuse, weeping, edematous external ear, the lack of response to guided antibiotic therapy, and the pruritus experienced by the patient argue against the diagnosis of bacterial cellulitis.

Acute otitis media, like otitis externa, produces ear discharge usually associated with significant pain. Thus, it is important when working through the differential to define the source of the ear discharge. In this case, a consultation with an ENT specialist confirmed that there was an intact tympanic membrane with no middle ear involvement, ruling out the diagnosis of acute otitis media.

Continue to: Herpes zoster oticus

Herpes zoster oticus. The absence of grouped vesicles at any point during the eruption, itching rather than pain, and negative viral culture and polymerase chain reaction studies for herpes simplex and varicella zoster virus excluded the diagnosis of herpes zoster oticus.

Diagnostic criteria were met

This case was compatible with the characterizations of IED as initially described by Engman³ in 1902 and further detailed by Sutton,⁴ who provided the following criteria for diagnosis:

• an initial eczematous or pustular lesion
• extension peripherally by autoinoculation
• an absence of central clearing
• Staphylococcus on culture of the initial lesion
• a history of infection.

Case reports have added to our understanding of the mechanism of autosensitization of surrounding skin.⁵ Yamany and Schwartz have proposed the diagnostic criteria summarized in the TABLE.²

Age factors into location. The ears, nose, and face are predominantly involved in cases of IED in the pediatric population, while the lower extremities are predominantly involved in adults.⁶ Laboratory tests and imaging may aid in excluding other potential diagnoses or complications, but the diagnosis remains clinical and requires the clinician to avoid jumping to the conclusion that every moist, erythematous crusting eruption is purely infectious in nature.

Tx and prevention hinge on a combination of antibiotics, steroids

The management of IED should be aimed at fighting the infection, eliminating the allergic contact dermatitis associated with infectious products, and improving barrier protection. Topical and/or systemic antibiotics guided by culture focus on killing bacteria. The allergic immune response is dampened by systemic steroids. Topical steroids, however, are difficult to utilize on moist, draining skin. In the case of otitis externa, a combination topical antibiotic and steroid otic drop can be utilized. As healing begins, emollients are applied to aid in skin repair.² Topical antibiotics containing neomycin or polymyxin should be avoided to eliminate the possibility of developing contact sensitivity to these agents.

Topical antibiotics containing neomycin or polymyxin should be avoided to eliminate the possibility of developing contact sensitivity to these agents.

For our patient, inpatient wound cultures demonstrated methicillin-resistant S aureus, and empiric treatment with IV cefepime and vancomycin was transitioned to IV clindamycin based on sensitivities and then transitioned to a 12-day course of oral clindamycin 150 mg bid. In addition, the patient received ciprofloxacin/dexamethasone otic drops 3 times/d to treat his otitis externa. After initiating prednisone 30 mg (1 mg/kg/d) for 10 days to cover the allergic component, the patient showed prompt clinical improvement. Gentle cleansing of the right ear with hypoallergenic soap and water followed by application of petrolatum ointment 4 times/d was used to promote healing and improve barrier function (FIGURE 2). The patient’s mother indicated during a follow-up call that the affected area had dramatically improved.

THE CASE

A 27-year-old woman presented to the family medicine clinic to establish care for a recent onset of seizures, for which she had previously been admitted, 4 months after delivering her first child. Her pregnancy was complicated by type 1 diabetes and poor glycemic control. Labor was induced at 37 weeks; however, vaginal delivery was impeded by arrest of dilation. An emergency cesarean section was performed under general anesthesia, resulting in a healthy newborn male.

Six weeks after giving birth, the patient was started on sertraline 50 mg/d for postpartum depression. Her history was significant for depression 8 years prior that was controlled with psychotherapy, and treated prior to coming to our clinic. She had not experienced any depressive symptoms during pregnancy.

Three months postpartum, she was hospitalized for recurrent syncopal episodes. They lasted about 2 minutes, with prodromal generalized weakness followed by loss of consciousness. There was no post-event confusion, tongue-biting, or incontinence. Physical exam, electroencephalogram (EEG), echocardiogram, and magnetic resonance imaging of the head and neck demonstrated no acute findings.

These episodes escalated in frequency weeks after they began, involving as many as 40 daily attacks, some of which lasted up to 45 minutes. During these events, the patient was nonresponsive but reported reliving the delivery of her child. Upon initial consultation with Neurology, no cause was found, and she was advised to wear a helmet, stop driving, and refrain from carrying her son. No antiepileptic medications were initiated because there were no EEG findings that supported seizure, and her mood had not improved, despite an increase in sertraline dosage, a switch to citalopram, and the addition of bupropion. She described anxiety, nightmares, and intrusive thoughts during psychotherapy sessions. Her psychiatrist gave her an additional diagnosis of posttraumatic stress disorder (PTSD) secondary to her delivery. The family medicine clinic assisted the patient and her family throughout her care by functioning as a home base for her.

Eight months following initial symptoms, repeat evaluation with a video-EEG revealed no evidence of EEG changes during seizure-like activity.

THE DIAGNOSIS

The patient was given a diagnosis of psychogenic nonepileptic seizure (PNES) by a neurologist experienced in diagnosing both epilepsy and PNES. The physician made the diagnosis after noting that the seizures on video had no corresponding epileptiform activity on EEG. This represents a “documented” diagnosis, the highest level of confidence possible in diagnosing PNES, as reported by the International League Against Epilepsy Nonepileptic Seizures Task Force.¹

DISCUSSION

With a prevalence of 5% to 10% and 20% to 40% in outpatient and inpatient epilepsy clinics respectively, PNES events have become of increasing interest to physicians.² There are few cases of PNES in women during pregnancy reported in the literature.^3,4 This is the first case report of PNES with postpartum onset.

Continue to: Epilepsy vs psychogenic nonepileptic seizures

PNES episodes appear similar to epileptic seizures, but without a definitive neurobiologic source.^2,3 However, recent literature suggests the root cause may be found in abnormalities in neurologic networks, such as dysfunction of frontal and parietal lobe connectivity and increased communication from emotional centers of the brain.^2,5 There are no typical pathognomonic symptoms of PNES, leading to diagnostic difficulty.² A definitive diagnosis may be made when a patient experiences seizures without EEG abnormalities.² Further diagnostic brain imaging is unnecessary.

Trauma may be the underlying cause

A predominance of PNES in both women and young adults, with no definitive associated factors, has been reported in the literature.² Studies suggest childhood sexual abuse, physical abuse, traumatic brain injury, and health-related trauma, such as distressing medical experiences and surgeries, may be risk factors, while depression, misdiagnosis, and mistreatment can heighten seizure activity.^2,3

Treatment requires a multidisciplinary team

Effective management of PNES requires collaboration between the primary care physician, neurologist, psychiatrist, and psychotherapist, with an emphasis on evaluation and control of the underlying trigger(s).³ Randomized controlled trials have demonstrated the efficacy of cognitive behavioral therapy (CBT), supportive care, and patient education in reducing seizure frequency at the 6-month follow-up.^3,6 Additional studies have reported the best prognostic factor in PNES management is patient employment of an internal locus of control—the patient’s belief that they control life events.^7,8 Case series suggest electroconvulsive therapy (ECT) is an effective alternative mood stabilization and seizure reduction therapy when tolerated.⁹

Our patient tried several combinations of treatment to manage PNES and comorbid psychiatric conditions, including CBT, antidepressants, and anxiolytics. After about 5 treatment failures, she pursued ECT for treatment-resistant depression and PNES frequency reduction but failed to tolerate therapy. Currently, her PNES has been reduced to 1 to 2 weekly episodes with a 200 mg/d dose of lamotrigine as a mood stabilizer combined with CBT.

THE TAKEAWAY

Providers should investigate a patient’s history and psychologic disposition when the patient presents with seizure-like behavior without a neurobiologic source or with a negative video-EEG study. A history of depression, traumatic experience, PTSD, or other psychosocial triggers must be noted early to prevent a delay in treatment when PNES is part of the differential. Due to a delayed diagnosis of PNES in our patient, she went without full treatment for almost 12 months and experienced worsening episodes. The primary care physician plays an integral role in early identification and intervention through anticipatory guidance, initial work-up, and support for patients with suspected PNES (TABLE).

CORRESPONDENCE
Karim Hanna, MD, 13330 USF Laurel Drive, Tampa, FL; [email protected]

THE CASE

A 27-year-old woman presented to the family medicine clinic to establish care for a recent onset of seizures, for which she had previously been admitted, 4 months after delivering her first child. Her pregnancy was complicated by type 1 diabetes and poor glycemic control. Labor was induced at 37 weeks; however, vaginal delivery was impeded by arrest of dilation. An emergency cesarean section was performed under general anesthesia, resulting in a healthy newborn male.

Six weeks after giving birth, the patient was started on sertraline 50 mg/d for postpartum depression. Her history was significant for depression 8 years prior that was controlled with psychotherapy, and treated prior to coming to our clinic. She had not experienced any depressive symptoms during pregnancy.

Three months postpartum, she was hospitalized for recurrent syncopal episodes. They lasted about 2 minutes, with prodromal generalized weakness followed by loss of consciousness. There was no post-event confusion, tongue-biting, or incontinence. Physical exam, electroencephalogram (EEG), echocardiogram, and magnetic resonance imaging of the head and neck demonstrated no acute findings.

These episodes escalated in frequency weeks after they began, involving as many as 40 daily attacks, some of which lasted up to 45 minutes. During these events, the patient was nonresponsive but reported reliving the delivery of her child. Upon initial consultation with Neurology, no cause was found, and she was advised to wear a helmet, stop driving, and refrain from carrying her son. No antiepileptic medications were initiated because there were no EEG findings that supported seizure, and her mood had not improved, despite an increase in sertraline dosage, a switch to citalopram, and the addition of bupropion. She described anxiety, nightmares, and intrusive thoughts during psychotherapy sessions. Her psychiatrist gave her an additional diagnosis of posttraumatic stress disorder (PTSD) secondary to her delivery. The family medicine clinic assisted the patient and her family throughout her care by functioning as a home base for her.

Eight months following initial symptoms, repeat evaluation with a video-EEG revealed no evidence of EEG changes during seizure-like activity.

THE DIAGNOSIS

The patient was given a diagnosis of psychogenic nonepileptic seizure (PNES) by a neurologist experienced in diagnosing both epilepsy and PNES. The physician made the diagnosis after noting that the seizures on video had no corresponding epileptiform activity on EEG. This represents a “documented” diagnosis, the highest level of confidence possible in diagnosing PNES, as reported by the International League Against Epilepsy Nonepileptic Seizures Task Force.¹

DISCUSSION

With a prevalence of 5% to 10% and 20% to 40% in outpatient and inpatient epilepsy clinics respectively, PNES events have become of increasing interest to physicians.² There are few cases of PNES in women during pregnancy reported in the literature.^3,4 This is the first case report of PNES with postpartum onset.

Continue to: Epilepsy vs psychogenic nonepileptic seizures

PNES episodes appear similar to epileptic seizures, but without a definitive neurobiologic source.^2,3 However, recent literature suggests the root cause may be found in abnormalities in neurologic networks, such as dysfunction of frontal and parietal lobe connectivity and increased communication from emotional centers of the brain.^2,5 There are no typical pathognomonic symptoms of PNES, leading to diagnostic difficulty.² A definitive diagnosis may be made when a patient experiences seizures without EEG abnormalities.² Further diagnostic brain imaging is unnecessary.

Trauma may be the underlying cause

A predominance of PNES in both women and young adults, with no definitive associated factors, has been reported in the literature.² Studies suggest childhood sexual abuse, physical abuse, traumatic brain injury, and health-related trauma, such as distressing medical experiences and surgeries, may be risk factors, while depression, misdiagnosis, and mistreatment can heighten seizure activity.^2,3

Treatment requires a multidisciplinary team

Effective management of PNES requires collaboration between the primary care physician, neurologist, psychiatrist, and psychotherapist, with an emphasis on evaluation and control of the underlying trigger(s).³ Randomized controlled trials have demonstrated the efficacy of cognitive behavioral therapy (CBT), supportive care, and patient education in reducing seizure frequency at the 6-month follow-up.^3,6 Additional studies have reported the best prognostic factor in PNES management is patient employment of an internal locus of control—the patient’s belief that they control life events.^7,8 Case series suggest electroconvulsive therapy (ECT) is an effective alternative mood stabilization and seizure reduction therapy when tolerated.⁹

Our patient tried several combinations of treatment to manage PNES and comorbid psychiatric conditions, including CBT, antidepressants, and anxiolytics. After about 5 treatment failures, she pursued ECT for treatment-resistant depression and PNES frequency reduction but failed to tolerate therapy. Currently, her PNES has been reduced to 1 to 2 weekly episodes with a 200 mg/d dose of lamotrigine as a mood stabilizer combined with CBT.

THE TAKEAWAY

Providers should investigate a patient’s history and psychologic disposition when the patient presents with seizure-like behavior without a neurobiologic source or with a negative video-EEG study. A history of depression, traumatic experience, PTSD, or other psychosocial triggers must be noted early to prevent a delay in treatment when PNES is part of the differential. Due to a delayed diagnosis of PNES in our patient, she went without full treatment for almost 12 months and experienced worsening episodes. The primary care physician plays an integral role in early identification and intervention through anticipatory guidance, initial work-up, and support for patients with suspected PNES (TABLE).

CORRESPONDENCE
Karim Hanna, MD, 13330 USF Laurel Drive, Tampa, FL; [email protected]

THE CASE

A 27-year-old woman presented to the family medicine clinic to establish care for a recent onset of seizures, for which she had previously been admitted, 4 months after delivering her first child. Her pregnancy was complicated by type 1 diabetes and poor glycemic control. Labor was induced at 37 weeks; however, vaginal delivery was impeded by arrest of dilation. An emergency cesarean section was performed under general anesthesia, resulting in a healthy newborn male.

Six weeks after giving birth, the patient was started on sertraline 50 mg/d for postpartum depression. Her history was significant for depression 8 years prior that was controlled with psychotherapy, and treated prior to coming to our clinic. She had not experienced any depressive symptoms during pregnancy.

Three months postpartum, she was hospitalized for recurrent syncopal episodes. They lasted about 2 minutes, with prodromal generalized weakness followed by loss of consciousness. There was no post-event confusion, tongue-biting, or incontinence. Physical exam, electroencephalogram (EEG), echocardiogram, and magnetic resonance imaging of the head and neck demonstrated no acute findings.

These episodes escalated in frequency weeks after they began, involving as many as 40 daily attacks, some of which lasted up to 45 minutes. During these events, the patient was nonresponsive but reported reliving the delivery of her child. Upon initial consultation with Neurology, no cause was found, and she was advised to wear a helmet, stop driving, and refrain from carrying her son. No antiepileptic medications were initiated because there were no EEG findings that supported seizure, and her mood had not improved, despite an increase in sertraline dosage, a switch to citalopram, and the addition of bupropion. She described anxiety, nightmares, and intrusive thoughts during psychotherapy sessions. Her psychiatrist gave her an additional diagnosis of posttraumatic stress disorder (PTSD) secondary to her delivery. The family medicine clinic assisted the patient and her family throughout her care by functioning as a home base for her.

Eight months following initial symptoms, repeat evaluation with a video-EEG revealed no evidence of EEG changes during seizure-like activity.

THE DIAGNOSIS

The patient was given a diagnosis of psychogenic nonepileptic seizure (PNES) by a neurologist experienced in diagnosing both epilepsy and PNES. The physician made the diagnosis after noting that the seizures on video had no corresponding epileptiform activity on EEG. This represents a “documented” diagnosis, the highest level of confidence possible in diagnosing PNES, as reported by the International League Against Epilepsy Nonepileptic Seizures Task Force.¹

DISCUSSION

With a prevalence of 5% to 10% and 20% to 40% in outpatient and inpatient epilepsy clinics respectively, PNES events have become of increasing interest to physicians.² There are few cases of PNES in women during pregnancy reported in the literature.^3,4 This is the first case report of PNES with postpartum onset.

Continue to: Epilepsy vs psychogenic nonepileptic seizures

PNES episodes appear similar to epileptic seizures, but without a definitive neurobiologic source.^2,3 However, recent literature suggests the root cause may be found in abnormalities in neurologic networks, such as dysfunction of frontal and parietal lobe connectivity and increased communication from emotional centers of the brain.^2,5 There are no typical pathognomonic symptoms of PNES, leading to diagnostic difficulty.² A definitive diagnosis may be made when a patient experiences seizures without EEG abnormalities.² Further diagnostic brain imaging is unnecessary.

Trauma may be the underlying cause

A predominance of PNES in both women and young adults, with no definitive associated factors, has been reported in the literature.² Studies suggest childhood sexual abuse, physical abuse, traumatic brain injury, and health-related trauma, such as distressing medical experiences and surgeries, may be risk factors, while depression, misdiagnosis, and mistreatment can heighten seizure activity.^2,3

Treatment requires a multidisciplinary team

Effective management of PNES requires collaboration between the primary care physician, neurologist, psychiatrist, and psychotherapist, with an emphasis on evaluation and control of the underlying trigger(s).³ Randomized controlled trials have demonstrated the efficacy of cognitive behavioral therapy (CBT), supportive care, and patient education in reducing seizure frequency at the 6-month follow-up.^3,6 Additional studies have reported the best prognostic factor in PNES management is patient employment of an internal locus of control—the patient’s belief that they control life events.^7,8 Case series suggest electroconvulsive therapy (ECT) is an effective alternative mood stabilization and seizure reduction therapy when tolerated.⁹

Our patient tried several combinations of treatment to manage PNES and comorbid psychiatric conditions, including CBT, antidepressants, and anxiolytics. After about 5 treatment failures, she pursued ECT for treatment-resistant depression and PNES frequency reduction but failed to tolerate therapy. Currently, her PNES has been reduced to 1 to 2 weekly episodes with a 200 mg/d dose of lamotrigine as a mood stabilizer combined with CBT.

THE TAKEAWAY

Providers should investigate a patient’s history and psychologic disposition when the patient presents with seizure-like behavior without a neurobiologic source or with a negative video-EEG study. A history of depression, traumatic experience, PTSD, or other psychosocial triggers must be noted early to prevent a delay in treatment when PNES is part of the differential. Due to a delayed diagnosis of PNES in our patient, she went without full treatment for almost 12 months and experienced worsening episodes. The primary care physician plays an integral role in early identification and intervention through anticipatory guidance, initial work-up, and support for patients with suspected PNES (TABLE).

CORRESPONDENCE
Karim Hanna, MD, 13330 USF Laurel Drive, Tampa, FL; [email protected]

In April 2021, the US Preventive Services Task Force (USPSTF) published an updated recommendation on screening for vitamin D deficiency in adults. It reaffirmed an “I” statement first made in 2014: evidence is insufficient to balance the benefits and harms of screening.¹ This recommendation applies to asymptomatic, community-dwelling, nonpregnant adults without conditions treatable with vitamin D. It’s important to remember that screening refers to testing asymptomatic individuals to detect a condition early before it causes illness. Testing performed to determine whether symptoms are evidence of an underlying condition is not screening but diagnostic testing.

The Task Force statement explains the problems they found with the current level of knowledge about screening for vitamin D deficiency. First, while 25-hydroxyvitamin D [25(OH)D] is considered the best test for vitamin D levels, it is hard to measure accurately and test results vary by the method used and laboratories doing the testing. There also is uncertainty about how best to measure vitamin D status in different racial and ethnic groups, especially those with dark skin pigmentation. In addition, 25(OH)D in the blood is predominantly the bound form, with only 10% to 15% being unbound and bioavailable. Current tests do not determine the amount of bound vs unbound 25(OH)D.^1-3

There is no consensus about the optimal blood level of vitamin D or the level that defines deficiency. The Institute of Medicine (now the National Academy of Medicine—NAM) stated that serum 25(OH)D levels ≥ 20 ng/mL are adequate to meet the ­metabolic needs of 97.5% of people, and that levels of 12 to 20 ng/mL pose a risk of deficiency, with levels < 12 considered to be very low.⁴ The Endocrine Society defines deficiency as < 20 ng/mL and insufficiency as 21 to 29 ng/mL.⁵

The rate of testing for vitamin D deficiency in primary care in unknown, but there is evidence that since 2000, it has increased 80 fold at least among those with Medicare.⁶ Data from the 2011-2014 National Health and Nutrition Examination Survey showed that 5% of the population had 25(OH)D levels < 12 ng/mL and 18% had levels between 12 and 19 ng/mL.⁷ Some have estimated that as many as half of all adults would be considered vitamin D deficient or insufficient using current less conservative definitions, with higher rates in racial/ethnic minorities.^2,8

There are no firm data on the frequency, or benefits, of screening for vitamin D levels in asymptomatic adults (and treating those found to have vitamin D deficiency). The Task Force looked for indirect evidence by examining the effect of treating vitamin D deficiency in a number of conditions and found that for some, there was adequate evidence of no benefit and for others there was inadequate evidence for possible benefits.⁹ No benefit was found for incidence of fractures, type 2 diabetes, and overall mortality.⁹ Inadequate evidence was found for incidence of cancer, cardiovascular disease, scores on measures of depression and physical functioning, and urinary tract infections in those with impaired fasting glucose.⁹

Known risk factors for low vitamin D levels include low vitamin D intake, older age, obesity, low UVB exposure or absorption due to long winter seasons in northern latitudes, sun avoidance, and dark skin pigmentation.¹ In addition, certain medical conditions contribute to, or are caused by, low vitamin D levels—eg, osteoporosis, chronic kidney disease, malabsorption syndromes, and medication use (ie, glucocorticoids).^1-3

The Task Force recommendation on screening for vitamin D deficiency differs from those of some other organizations. However, none recommend universal population-based screening. The Endocrine Society and the American Association of Clinical Endocrinologists recommend screening but only in those at risk for vitamin D deficiency.^5,10 The American Academy of Family Physicians endorses the USPSTF recommendation.¹¹

Continue to: Specific USPSTF topics related to vitamin D

The Task Force has specifically addressed 3 topics pertaining to vitamin D. In each instance, the recommendation relates not to providing vitamin D to those who have vitamin D deficiency, but to providing vitamin D supplementation universally to specific groups at risk of a particular condition (TABLE 1).

Prevention of falls in the elderly. In 2018 the Task Force recommended against the use of vitamin D to prevent falls in ­community-dwelling adults ≥ 65 years.¹² This reversed its 2012 recommendation advising vitamin D supplementation to prevent falls. The Task Force re-examined the old evidence and looked at newer studies and concluded that their previous conclusion was wrong and that the evidence showed no benefit from vitamin D in preventing falls in the elderly. The reversal of a prior recommendation is rare for the USPSTF because of the rigor of its evidence reviews and its policy of not making a recommendation unless solid evidence for or against exists.

Prevention of cardiovascular disease and cancer. The Task Force concludes that current evidence is insufficient to assess the balance of benefits and harms in the use of single- or paired-nutrient supplements to prevent cardiovascular disease or cancer.¹³ (The exceptions are beta-carotene and vitamin E, which the Task Force recommends against.) This statement is consistent with the lack of evidence the Task Force found regarding prevention of these conditions by vitamin D supplementation in those who are vitamin D deficient.

Prevention of fractures in men and in premenopausal and postmenopausal women. For men and premenopausal women, the Task Force concludes that evidence is insufficient to assess the benefits and harms of vitamin D and calcium supplementation, alone or in combination, to prevent fractures.¹⁴ For prevention of fractures in postmenopausal women, there are 2 recommendations. The first one advises against the use of ≤ 400 IU of vitamin D and ≤ 1000 mg of calcium because the evidence indicates ineffectiveness. The second one is another “I” statement for the use of doses > 400 IU of vitamin D and > 1000 mg of calcium. These 3 recommendations apply to adults who live in the community and not in nursing homes or other institutional care facilities; they do not apply to those who have osteoporosis.

What should the family physician do?

Encourage all patients to take the recommended dietary allowances (RDA) of vitamin D. The RDA is the average daily level of intake sufficient to meet the nutrient requirements of nearly all (97%-98%) healthy individuals. Most professional organizations recommend that adults ≥ 50 years consume 800 to 1000 IU of vitamin D daily. TABLE 2 lists the RDA for vitamin D by age and sex.¹⁵ The amount of vitamin D in selected food products is listed in TABLE 3.¹⁵ Some increase in levels of vitamin D can occur as a result of sun exposure, but current practices of sun avoidance make it difficult to achieve a significant contribution to vitamin D requirements.¹⁵

Continue to: Alternatives to universal screening

Alternatives to universal screening. Screening for vitamin D deficiency might benefit some patients, although there is no evidence to support it. Universal screening will likely lead to overdiagnosis and overtreatment based on what is essentially a poorly understood blood test. This was the concern expressed by the NAM.^4,16 An editorial accompanying publication of the recent USPSTF recommendation suggested not measuring vitamin D levels but instead advising patients to consume the age-based RDA of vitamin D.³ For those at increased risk for vitamin D deficiency, advise a higher dose of vitamin D (eg, 2000 IU/d, which is still lower than the upper daily limit).³

Evidence is insufficient to recommend vitamin D and calcium supplementation, alone or in combination, to prevent fractures in men or premenopausal women.

Other options are to screen for vitamin D deficiency only in those at high risk for low vitamin D levels, and to test for vitamin D deficiency in those with symptoms associated with deficiency such as bone pain and muscle weakness. These options would be consistent with recommendations from the Endocrine Society.⁵ Some have recommended that if testing is ordered, it should be performed by a laboratory that uses liquid chromatography-mass spectrometry because it is the criterion standard.²

Treatment options. Vitamin D deficiency can be treated with either ergocalciferol (vitamin D₂) or cholecalciferol (vitamin D₃). These treatments can also be recommended for those whose diets may not provide the RDA for vitamin D. Both are readily available over the counter and by prescription. The Task Force found that the harms of treating vitamin D deficiency with vitamin D at recommended doses are small to none.¹ There is possibly a small increase in kidney stones with the combined use of 1000 mg/d calcium and 10 mcg (400 IU)/d vitamin D.¹⁷ Large doses of vitamin D can cause toxicity including marked hypercalcemia, nausea, vomiting, muscle weakness, neuropsychiatric disturbances, pain, loss of appetite, dehydration, polyuria, excessive thirst, and kidney stones.¹⁵A cautious evidence-based approach would be to selectively screen for vitamin D deficiency, conduct diagnostic testing when indicated, and advise vitamin D supplementation as needed.

In April 2021, the US Preventive Services Task Force (USPSTF) published an updated recommendation on screening for vitamin D deficiency in adults. It reaffirmed an “I” statement first made in 2014: evidence is insufficient to balance the benefits and harms of screening.¹ This recommendation applies to asymptomatic, community-dwelling, nonpregnant adults without conditions treatable with vitamin D. It’s important to remember that screening refers to testing asymptomatic individuals to detect a condition early before it causes illness. Testing performed to determine whether symptoms are evidence of an underlying condition is not screening but diagnostic testing.

The Task Force statement explains the problems they found with the current level of knowledge about screening for vitamin D deficiency. First, while 25-hydroxyvitamin D [25(OH)D] is considered the best test for vitamin D levels, it is hard to measure accurately and test results vary by the method used and laboratories doing the testing. There also is uncertainty about how best to measure vitamin D status in different racial and ethnic groups, especially those with dark skin pigmentation. In addition, 25(OH)D in the blood is predominantly the bound form, with only 10% to 15% being unbound and bioavailable. Current tests do not determine the amount of bound vs unbound 25(OH)D.^1-3

There is no consensus about the optimal blood level of vitamin D or the level that defines deficiency. The Institute of Medicine (now the National Academy of Medicine—NAM) stated that serum 25(OH)D levels ≥ 20 ng/mL are adequate to meet the ­metabolic needs of 97.5% of people, and that levels of 12 to 20 ng/mL pose a risk of deficiency, with levels < 12 considered to be very low.⁴ The Endocrine Society defines deficiency as < 20 ng/mL and insufficiency as 21 to 29 ng/mL.⁵

The rate of testing for vitamin D deficiency in primary care in unknown, but there is evidence that since 2000, it has increased 80 fold at least among those with Medicare.⁶ Data from the 2011-2014 National Health and Nutrition Examination Survey showed that 5% of the population had 25(OH)D levels < 12 ng/mL and 18% had levels between 12 and 19 ng/mL.⁷ Some have estimated that as many as half of all adults would be considered vitamin D deficient or insufficient using current less conservative definitions, with higher rates in racial/ethnic minorities.^2,8

There are no firm data on the frequency, or benefits, of screening for vitamin D levels in asymptomatic adults (and treating those found to have vitamin D deficiency). The Task Force looked for indirect evidence by examining the effect of treating vitamin D deficiency in a number of conditions and found that for some, there was adequate evidence of no benefit and for others there was inadequate evidence for possible benefits.⁹ No benefit was found for incidence of fractures, type 2 diabetes, and overall mortality.⁹ Inadequate evidence was found for incidence of cancer, cardiovascular disease, scores on measures of depression and physical functioning, and urinary tract infections in those with impaired fasting glucose.⁹

Known risk factors for low vitamin D levels include low vitamin D intake, older age, obesity, low UVB exposure or absorption due to long winter seasons in northern latitudes, sun avoidance, and dark skin pigmentation.¹ In addition, certain medical conditions contribute to, or are caused by, low vitamin D levels—eg, osteoporosis, chronic kidney disease, malabsorption syndromes, and medication use (ie, glucocorticoids).^1-3

The Task Force recommendation on screening for vitamin D deficiency differs from those of some other organizations. However, none recommend universal population-based screening. The Endocrine Society and the American Association of Clinical Endocrinologists recommend screening but only in those at risk for vitamin D deficiency.^5,10 The American Academy of Family Physicians endorses the USPSTF recommendation.¹¹

Continue to: Specific USPSTF topics related to vitamin D

The Task Force has specifically addressed 3 topics pertaining to vitamin D. In each instance, the recommendation relates not to providing vitamin D to those who have vitamin D deficiency, but to providing vitamin D supplementation universally to specific groups at risk of a particular condition (TABLE 1).

Prevention of falls in the elderly. In 2018 the Task Force recommended against the use of vitamin D to prevent falls in ­community-dwelling adults ≥ 65 years.¹² This reversed its 2012 recommendation advising vitamin D supplementation to prevent falls. The Task Force re-examined the old evidence and looked at newer studies and concluded that their previous conclusion was wrong and that the evidence showed no benefit from vitamin D in preventing falls in the elderly. The reversal of a prior recommendation is rare for the USPSTF because of the rigor of its evidence reviews and its policy of not making a recommendation unless solid evidence for or against exists.

Prevention of cardiovascular disease and cancer. The Task Force concludes that current evidence is insufficient to assess the balance of benefits and harms in the use of single- or paired-nutrient supplements to prevent cardiovascular disease or cancer.¹³ (The exceptions are beta-carotene and vitamin E, which the Task Force recommends against.) This statement is consistent with the lack of evidence the Task Force found regarding prevention of these conditions by vitamin D supplementation in those who are vitamin D deficient.

Prevention of fractures in men and in premenopausal and postmenopausal women. For men and premenopausal women, the Task Force concludes that evidence is insufficient to assess the benefits and harms of vitamin D and calcium supplementation, alone or in combination, to prevent fractures.¹⁴ For prevention of fractures in postmenopausal women, there are 2 recommendations. The first one advises against the use of ≤ 400 IU of vitamin D and ≤ 1000 mg of calcium because the evidence indicates ineffectiveness. The second one is another “I” statement for the use of doses > 400 IU of vitamin D and > 1000 mg of calcium. These 3 recommendations apply to adults who live in the community and not in nursing homes or other institutional care facilities; they do not apply to those who have osteoporosis.

What should the family physician do?

Encourage all patients to take the recommended dietary allowances (RDA) of vitamin D. The RDA is the average daily level of intake sufficient to meet the nutrient requirements of nearly all (97%-98%) healthy individuals. Most professional organizations recommend that adults ≥ 50 years consume 800 to 1000 IU of vitamin D daily. TABLE 2 lists the RDA for vitamin D by age and sex.¹⁵ The amount of vitamin D in selected food products is listed in TABLE 3.¹⁵ Some increase in levels of vitamin D can occur as a result of sun exposure, but current practices of sun avoidance make it difficult to achieve a significant contribution to vitamin D requirements.¹⁵

Continue to: Alternatives to universal screening

Alternatives to universal screening. Screening for vitamin D deficiency might benefit some patients, although there is no evidence to support it. Universal screening will likely lead to overdiagnosis and overtreatment based on what is essentially a poorly understood blood test. This was the concern expressed by the NAM.^4,16 An editorial accompanying publication of the recent USPSTF recommendation suggested not measuring vitamin D levels but instead advising patients to consume the age-based RDA of vitamin D.³ For those at increased risk for vitamin D deficiency, advise a higher dose of vitamin D (eg, 2000 IU/d, which is still lower than the upper daily limit).³

Evidence is insufficient to recommend vitamin D and calcium supplementation, alone or in combination, to prevent fractures in men or premenopausal women.

Other options are to screen for vitamin D deficiency only in those at high risk for low vitamin D levels, and to test for vitamin D deficiency in those with symptoms associated with deficiency such as bone pain and muscle weakness. These options would be consistent with recommendations from the Endocrine Society.⁵ Some have recommended that if testing is ordered, it should be performed by a laboratory that uses liquid chromatography-mass spectrometry because it is the criterion standard.²

Treatment options. Vitamin D deficiency can be treated with either ergocalciferol (vitamin D₂) or cholecalciferol (vitamin D₃). These treatments can also be recommended for those whose diets may not provide the RDA for vitamin D. Both are readily available over the counter and by prescription. The Task Force found that the harms of treating vitamin D deficiency with vitamin D at recommended doses are small to none.¹ There is possibly a small increase in kidney stones with the combined use of 1000 mg/d calcium and 10 mcg (400 IU)/d vitamin D.¹⁷ Large doses of vitamin D can cause toxicity including marked hypercalcemia, nausea, vomiting, muscle weakness, neuropsychiatric disturbances, pain, loss of appetite, dehydration, polyuria, excessive thirst, and kidney stones.¹⁵A cautious evidence-based approach would be to selectively screen for vitamin D deficiency, conduct diagnostic testing when indicated, and advise vitamin D supplementation as needed.

In April 2021, the US Preventive Services Task Force (USPSTF) published an updated recommendation on screening for vitamin D deficiency in adults. It reaffirmed an “I” statement first made in 2014: evidence is insufficient to balance the benefits and harms of screening.¹ This recommendation applies to asymptomatic, community-dwelling, nonpregnant adults without conditions treatable with vitamin D. It’s important to remember that screening refers to testing asymptomatic individuals to detect a condition early before it causes illness. Testing performed to determine whether symptoms are evidence of an underlying condition is not screening but diagnostic testing.

The Task Force statement explains the problems they found with the current level of knowledge about screening for vitamin D deficiency. First, while 25-hydroxyvitamin D [25(OH)D] is considered the best test for vitamin D levels, it is hard to measure accurately and test results vary by the method used and laboratories doing the testing. There also is uncertainty about how best to measure vitamin D status in different racial and ethnic groups, especially those with dark skin pigmentation. In addition, 25(OH)D in the blood is predominantly the bound form, with only 10% to 15% being unbound and bioavailable. Current tests do not determine the amount of bound vs unbound 25(OH)D.^1-3

There is no consensus about the optimal blood level of vitamin D or the level that defines deficiency. The Institute of Medicine (now the National Academy of Medicine—NAM) stated that serum 25(OH)D levels ≥ 20 ng/mL are adequate to meet the ­metabolic needs of 97.5% of people, and that levels of 12 to 20 ng/mL pose a risk of deficiency, with levels < 12 considered to be very low.⁴ The Endocrine Society defines deficiency as < 20 ng/mL and insufficiency as 21 to 29 ng/mL.⁵

The rate of testing for vitamin D deficiency in primary care in unknown, but there is evidence that since 2000, it has increased 80 fold at least among those with Medicare.⁶ Data from the 2011-2014 National Health and Nutrition Examination Survey showed that 5% of the population had 25(OH)D levels < 12 ng/mL and 18% had levels between 12 and 19 ng/mL.⁷ Some have estimated that as many as half of all adults would be considered vitamin D deficient or insufficient using current less conservative definitions, with higher rates in racial/ethnic minorities.^2,8

There are no firm data on the frequency, or benefits, of screening for vitamin D levels in asymptomatic adults (and treating those found to have vitamin D deficiency). The Task Force looked for indirect evidence by examining the effect of treating vitamin D deficiency in a number of conditions and found that for some, there was adequate evidence of no benefit and for others there was inadequate evidence for possible benefits.⁹ No benefit was found for incidence of fractures, type 2 diabetes, and overall mortality.⁹ Inadequate evidence was found for incidence of cancer, cardiovascular disease, scores on measures of depression and physical functioning, and urinary tract infections in those with impaired fasting glucose.⁹

Known risk factors for low vitamin D levels include low vitamin D intake, older age, obesity, low UVB exposure or absorption due to long winter seasons in northern latitudes, sun avoidance, and dark skin pigmentation.¹ In addition, certain medical conditions contribute to, or are caused by, low vitamin D levels—eg, osteoporosis, chronic kidney disease, malabsorption syndromes, and medication use (ie, glucocorticoids).^1-3

The Task Force recommendation on screening for vitamin D deficiency differs from those of some other organizations. However, none recommend universal population-based screening. The Endocrine Society and the American Association of Clinical Endocrinologists recommend screening but only in those at risk for vitamin D deficiency.^5,10 The American Academy of Family Physicians endorses the USPSTF recommendation.¹¹

Continue to: Specific USPSTF topics related to vitamin D

The Task Force has specifically addressed 3 topics pertaining to vitamin D. In each instance, the recommendation relates not to providing vitamin D to those who have vitamin D deficiency, but to providing vitamin D supplementation universally to specific groups at risk of a particular condition (TABLE 1).

Prevention of falls in the elderly. In 2018 the Task Force recommended against the use of vitamin D to prevent falls in ­community-dwelling adults ≥ 65 years.¹² This reversed its 2012 recommendation advising vitamin D supplementation to prevent falls. The Task Force re-examined the old evidence and looked at newer studies and concluded that their previous conclusion was wrong and that the evidence showed no benefit from vitamin D in preventing falls in the elderly. The reversal of a prior recommendation is rare for the USPSTF because of the rigor of its evidence reviews and its policy of not making a recommendation unless solid evidence for or against exists.

Prevention of cardiovascular disease and cancer. The Task Force concludes that current evidence is insufficient to assess the balance of benefits and harms in the use of single- or paired-nutrient supplements to prevent cardiovascular disease or cancer.¹³ (The exceptions are beta-carotene and vitamin E, which the Task Force recommends against.) This statement is consistent with the lack of evidence the Task Force found regarding prevention of these conditions by vitamin D supplementation in those who are vitamin D deficient.

Prevention of fractures in men and in premenopausal and postmenopausal women. For men and premenopausal women, the Task Force concludes that evidence is insufficient to assess the benefits and harms of vitamin D and calcium supplementation, alone or in combination, to prevent fractures.¹⁴ For prevention of fractures in postmenopausal women, there are 2 recommendations. The first one advises against the use of ≤ 400 IU of vitamin D and ≤ 1000 mg of calcium because the evidence indicates ineffectiveness. The second one is another “I” statement for the use of doses > 400 IU of vitamin D and > 1000 mg of calcium. These 3 recommendations apply to adults who live in the community and not in nursing homes or other institutional care facilities; they do not apply to those who have osteoporosis.

What should the family physician do?

Encourage all patients to take the recommended dietary allowances (RDA) of vitamin D. The RDA is the average daily level of intake sufficient to meet the nutrient requirements of nearly all (97%-98%) healthy individuals. Most professional organizations recommend that adults ≥ 50 years consume 800 to 1000 IU of vitamin D daily. TABLE 2 lists the RDA for vitamin D by age and sex.¹⁵ The amount of vitamin D in selected food products is listed in TABLE 3.¹⁵ Some increase in levels of vitamin D can occur as a result of sun exposure, but current practices of sun avoidance make it difficult to achieve a significant contribution to vitamin D requirements.¹⁵

Continue to: Alternatives to universal screening

Alternatives to universal screening. Screening for vitamin D deficiency might benefit some patients, although there is no evidence to support it. Universal screening will likely lead to overdiagnosis and overtreatment based on what is essentially a poorly understood blood test. This was the concern expressed by the NAM.^4,16 An editorial accompanying publication of the recent USPSTF recommendation suggested not measuring vitamin D levels but instead advising patients to consume the age-based RDA of vitamin D.³ For those at increased risk for vitamin D deficiency, advise a higher dose of vitamin D (eg, 2000 IU/d, which is still lower than the upper daily limit).³

Evidence is insufficient to recommend vitamin D and calcium supplementation, alone or in combination, to prevent fractures in men or premenopausal women.

Other options are to screen for vitamin D deficiency only in those at high risk for low vitamin D levels, and to test for vitamin D deficiency in those with symptoms associated with deficiency such as bone pain and muscle weakness. These options would be consistent with recommendations from the Endocrine Society.⁵ Some have recommended that if testing is ordered, it should be performed by a laboratory that uses liquid chromatography-mass spectrometry because it is the criterion standard.²

Treatment options. Vitamin D deficiency can be treated with either ergocalciferol (vitamin D₂) or cholecalciferol (vitamin D₃). These treatments can also be recommended for those whose diets may not provide the RDA for vitamin D. Both are readily available over the counter and by prescription. The Task Force found that the harms of treating vitamin D deficiency with vitamin D at recommended doses are small to none.¹ There is possibly a small increase in kidney stones with the combined use of 1000 mg/d calcium and 10 mcg (400 IU)/d vitamin D.¹⁷ Large doses of vitamin D can cause toxicity including marked hypercalcemia, nausea, vomiting, muscle weakness, neuropsychiatric disturbances, pain, loss of appetite, dehydration, polyuria, excessive thirst, and kidney stones.¹⁵A cautious evidence-based approach would be to selectively screen for vitamin D deficiency, conduct diagnostic testing when indicated, and advise vitamin D supplementation as needed.

CASE

A 48-year-old man comes to your clinic with a dark nevus on his right upper arm that appeared 2 months earlier. He says that the lesion has continued to grow and has bled (he thought because he initially picked at it). On exam, there is a 7-mm brown papule with 2 black dots and slightly asymmetric borders.

How would you proceed with this patient?

Melanoma is the fifth leading cause of new cancer cases annually, with > 96,000 new cases in 2019.¹ Overall, melanoma is more common in men and in Whites, with 48% diagnosed in people ages 55 to 74.¹ The past 2 decades have seen numerous developments in the diagnosis, treatment, and surveillance of melanoma. This article covers recommendations, controversies, and issues that require future study. It does not cover uveal or mucosal melanoma.

Evaluating a patient with a new or changing nevus

Known risk factors for melanoma include a changing nevus, indoor tanning, older age, many melanocytic nevi, history of a dysplastic nevus or of blistering sunburns during teen years, red or blonde hair, large congenital nevus, Fitzpatrick skin type I or II, high socioeconomic status, personal or family history of melanoma, and intermittent high-intensity sun exposure.^2-3 Presence of 1 or more of these risk factors should lower the threshold for biopsy.

Worrisome physical exam features (FIGURE) are nevus asymmetry, irregular borders, variegated color, and a diameter > 6 mm (the size of a pencil eraser). Inquire as to whether the nevus’ appearance has evolved and if it has bled without trauma. In a patient with multiple nevi, 1 nevus that looks different than the rest (the so-called “ugly duckling”) is concerning. Accuracy of diagnosis is enhanced with dermoscopy. A Cochrane review showed that skilled use of dermoscopy, in addition to inspection with the naked eye, considerably increases the sensitivity and specificity of diagnosing melanoma.⁴ Yet a 2017 study of 705 US primary care practitioners showed that only 8.3% of them used dermoscopy to evaluate pigmented lesions.⁵

Several published algorithms and checklists can aid clinicians in identifying lesions suggestive of melanoma—eg, ABCDE, CASH, Menzies method, “chaos and clues,” and 2-step and 3- and 7-point checklists.^6-10 A simple 3-step algorithm, the TADA (triage amalgamated dermoscopic algorithm) method is available to novice dermoscopy users.¹¹ Experts in pigmented lesions prefer to use pattern analysis, which requires simultaneously assessing multiple lesion patterns that vary according to body site.^12,13

Dermoscopic features suggesting melanoma are atypical pigment networks, pseudopods, radial streaking, irregular dots or globules, blue-whitish veil, and granularity or peppering.¹⁴ Appropriate and effective use of dermoscopy requires training.^15,16 Available methods for learning dermoscopy include online and in-person courses, mentoring by experienced dermoscopists, books and articles, and free apps and online resources.¹⁷

Continue to: Perform a skin biopsy, but do this first

Skin biopsy is the definitive way to diagnose melanoma. Prior to biopsy, take photographs to document the exact location of the lesion and to ensure that the correct area is removed in wide excision (WE). A complete biopsy should include the full depth and breadth of the lesion to ensure there are clinically negative margins. This can be achieved with an elliptical excision (for larger lesions), punch excision (for small lesions), or saucerization (deep shave with 1- to 2-mm peripheral margins, used for intermediate-size lesions).¹⁸ Saucerization is distinctly different from a superficial shave biopsy, which is not recommended for lesions with features of melanoma.¹⁹

A decision to perform a biopsy on a part of the lesion (partial biopsy) depends on the size of the lesion and its anatomic location, and is best made in agreement with the patient. If the lesion cannot be removed in its entirety and a partial biopsy is necessary, take the sample from the most atypical appearing area and communicate this decision to the pathologist on the biopsy order. There is no evidence that performing a partial biopsy increases the risk of spreading melanoma.^20,21

If you are untrained or uncomfortable performing the biopsy, contact a dermatologist immediately. In many communities, such referrals are subject to long delays, which further supports the advisability of family physicians doing their own biopsies after photographing the suspicious lesion. Many resources are available to help family physicians learn to do biopsies proficiently (www.mdedge.com/familymedicine/article/164358/oncology/biopsies-skin-cancer-detection-dispelling-myths).¹⁹

What to communicate to the pathologist. At a minimum, the biopsy request form should include patient age, sex, biopsy type (punch, excisional, or scoop shave), intention (complete or partial sample), exact site of the biopsy with laterality, and clinical details. These details should include the lesion size and clinical description, the suspected diagnosis, and clinical information, such as whether there is a history of bleeding or changing color, size, or symmetry. In standard biopsy specimens, the pathologist is only examining a portion of the lesion. Communicating clearly to the pathologist may lead to a request for deeper or additional sections or special stains.

Complete biopsy of a concerning pigmented lesion is the standard. If, however, a partial biopsy is necessary, sample the most atypical area.

If the biopsy results do not match the clinical impression, a phone call to the pathologist is warranted. In addition, evaluation by a dermatopathologist may be merited as pathologic diagnosis of melanoma can be quite challenging. Newer molecular tests, such as fluorescence in situ hybridization (FISH) and comparative genomic hybridization (CGH), can assist in the histologic evaluation of complex pigmented lesions.

Continue to: CASE

CASE

You perform an elliptical excisional biopsy on your patient. The biopsy report comes back as a nodular malignant melanoma, Breslow depth 2.5 mm without ulceration, and no evidence of lymphovascular invasion or microsatellitosis. The report states that the biopsy margins appear clear of tumor involvement.

Further evaluation when the biopsy result is positive

Key steps in initial patient care include relaying pathology results to the patient, conducting (as needed) a more extensive evaluation, and obtaining appropriate consultation.

Clearly explain the diagnosis and convey an accurate reading of the pathology report. The vital pieces of information in the biopsy report are the Breslow depth and presence of ulceration, as evidence shows these 2 factors to be important independent predictors of outcome.^22,23 Also important are the presence of microsatellitosis (essential for staging purposes), pathologic stage, and the status of the peripheral and deep biopsy margins. Review Breslow depth with the patient as this largely dictates treatment options and prognosis.

Evaluate for possible metastatic disease. Obtain a complete history from every patient with cutaneous melanoma, looking for any positive review of systems as a harbinger of metastatic disease. A full-body skin and lymph node exam is vital, given that melanoma can arise anywhere including on the scalp, in the gluteal cleft, and beneath nails. If the lymph node exam is worrisome, conduct an ultrasound exam, even while referring to specialty care. Treating a patient with melanoma requires a multidisciplinary approach that may include dermatologists, surgeons, and oncologists based on the stage of disease. A challenge for family physicians is knowing which consultation to prioritize and how to counsel the patient to schedule these for the most cost-effective and timely evaluation.

Expedite a dermatology consultation. If the melanoma is deep or appears advanced based on size or palpable lymph nodes, contact the dermatologist immediately by phone to set up a rapid referral. Delays in the definitive management of thick melanomas can negatively affect outcome. Paper, facsimile, or electronic referrals can get lost in the system and are not reliable methods for referring patients for a melanoma consultation. One benefit of the family physician performing the initial biopsy is that a confirmed melanoma diagnosis will almost certainly get an expedited dermatology appointment.

Continue to: Wide excision and sentinel node biopsy

Wide excision and sentinel node biopsy

Wide excision of a primary melanoma is standard practice, with evidence favoring the following surgical margins: 0.5 to 1 cm for melanoma in situ, 1 cm for tumors up to 1 mm in thickness, 1 to 2 cm for tumors > 1 to 2 mm thick, and 2 cm for tumors > 2 mm thick.¹⁸ WE is often performed by dermatologists for nonulcerated tumors < 0.8 mm thick (T1a) without adverse features. If trained in cutaneous surgery, you can also choose to excise these thin melanomas in your office. Otherwise refer all patients with biopsy-proven melanoma to dermatologists to perform an adequate WE.

Refer patients who have tumors ≥ 0.8 mm thick to the appropriate surgical specialty (surgical oncology, if available) for consultation on sentinel lymph node biopsy. SLNB, when indicated, should be performed prior to WE of the primary tumor, and whenever possible in the same surgical setting, to maximize lymphatic drainage mapping techniques.¹⁸ Medical oncology referral, if needed, is usually made after WE.

SLNB remains the standard for lymph node staging. It is controversial mainly in its use for very thin or very thick lesions. Randomized controlled trials, including the Multicenter Selective Lymphadenectomy Trial,²⁴ have shown no difference in melanoma-­specific survival for patients with intermediate-thickness melanomas who had undergone SLNB.²⁴However, a subgroup analysis did find a significant increase in melanoma survival among patients with positive early SLNB results and immediate lymphadenectomy compared with patients who were observed and subsequently underwent lymphadenectomy only as metastases developed.²⁴

Many professional organizations consider SLNB to be the most significant prognostic indicator of disease recurrence. With a negative SLNB result, the risk of regional node recurrence is 5% or lower.^18,25 In addition, sentinel lymph node status is a critical determinant for systemic adjuvant therapy consideration and clinical trial eligibility. For patients who have primary cutaneous melanoma without clinical lymphadenopathy, an online tool is available for patients to use with their physician in predicting the likelihood of SLNB positivity.²⁶

Recommendations for SLNB, supported by multidisciplinary consensus:¹⁸

• Do not pursue SLNB for melanoma in situ or most cutaneous melanomas < 0.8 mm without ulceration (T1a). (See TABLE 1²⁷)
• Discuss SLNB with patients who have T1a melanoma and additional adverse features: young age, high mitotic rate, lymphovascular invasion, and nevus depth close to 0.8 mm with positive deep biopsy margins.
• Discuss SLNB with patients who have T1b disease (< 0.8 mm with ulceration, or 0.8-1 mm), although rates of SLNB positivity are low.
• Offer SLNB to patients with T2a and higher disease (> 1 mm).¹⁸

Continue to: Patients who have...

Patients who have clinical Stage I or II disease (TABLE 1²⁷) and a negative review of systems and lymph node exam do not require baseline laboratory or radiology tests.^18,28-31 Ultrasonography of the lymph node basin is recommended for any Stage I or II patient with an equivocal lymph node exam.^27,32 Stage III disease warrants computed tomography of the chest, abdomen, and pelvis, and possibly magnetic resonance imaging (MRI) of the brain. For Stage IV, brain MRI is recommended in all patients. Patients with higher risk disease (IIB - IV) will need consultation with Medical Oncology. The surgery and oncology team will make decisions regarding SLNB, genetic testing, and chemotherapy.

Melanoma in women: Considerations to keep in mind

Hormonal influences of pregnancy, lactation, contraception, and menopause introduce special considerations regarding melanoma, which is the most common cancer occurring during pregnancy, accounting for 31% of new malignancies.³³ Risk of melanoma lessens, however, for women who first give birth at a younger age or who have had > 5 live births.^18,34,35 There is no evidence that nevi darken during pregnancy, although nevi on the breast and abdomen may seem to enlarge due to skin stretching.¹⁸ All changing nevi in pregnancy warrant an examination, preferably with dermoscopy, and patients should be offered biopsy if there are any nevus characteristics associated with melanoma.¹⁸

One benefit of the family physician performing the initial biopsy is that a confirmed melanoma diagnosis will almost certainly get an expedited dermatology appointment.

The effect of pregnancy on an existing melanoma is not fully understood, but evidence from controlled studies shows no negative effect. Recent working group guidelines advise WE with local anesthesia without delay in pregnant patients.¹⁸ Definitive treatment after melanoma diagnosis should take a multidisciplinary approach involving obstetric care coordinated with Dermatology, Surgery, and Medical Oncology.¹⁸

Most recommendations on the timing of pregnancy following a melanoma diagnosis have limited evidence. One meta-­analysis concluded that pregnancy occurring after successful treatment of melanoma did not change a woman’s prognosis.³⁶ Current guidelines do not recommend delaying future pregnancy if a woman had an early-stage melanoma. For melanomas deemed higher risk, a woman could consider a 2- to 3-year delay in the next planned pregnancy, owing to current data on recurrence rates.¹⁸

A systematic review of women who used hormonal contraception or postmenopausal hormone replacement therapy (HRT) showed no associated increased risk of melanoma.³⁵ An additional randomized trial showed no effect of HRT on melanoma risk.³⁷

Continue to: Systemic melanoma treatment and common adverse effects

Multiple systemic therapies have been approved for the treatment of advanced or unresectable cutaneous melanomas. While these treatments are managed primarily by Oncology in concert with Dermatology, an awareness of the medications’ common dermatologic toxicities is important for the primary care provider. The 2 broad categories of FDA-approved systemic medications for advanced melanoma are mitogen-activated protein kinase (MAPK) inhibitors and immune checkpoint inhibitors, each having its own set of adverse cutaneous effects.

MAPK pathway–targeting drugs include the B-Raf proto-oncogene serine/threonine-kinase inhibitors (BRAFIs) vemurafenib and dabrafenib, and the MAPK inhibitors (MEKIs) trametinib and cobimetinib. The most common adverse skin effects in MAPK pathway–targeting drugs are severe ultraviolet photosensitivity, cutaneous epidermal neoplasms (particularly squamous cell carcinoma, keratoacanthoma-type), thick actinic keratosis, wart-like keratosis, painful palmoplantar keratosis, and dry skin.³⁸ These effects are most commonly seen with BRAFI monotherapy and can be abated with the addition of a MEKI. MEKI therapy can cause acneiform eruptions and paronychia.³⁹ Additional adverse effects include diarrhea, pyrexia, arthralgias, and fatigue for BRAFIs and diarrhea, fatigue, and peripheral edema for MEKIs.⁴⁰

Immune checkpoint inhibitors include anti-CTLA-4 (ipilimumab), anti-PD-1 (pembrolizumab and nivolumab), and anti-PDL-1 (atezolizumab). Adverse skin effects include morbilliform rash with or without an associated itch, itch with or without an associated rash, vitiligo, and lichenoid skin rashes. PD-1 and PDL-1 inhibitors have been associated with flares or unmasking of atopic dermatitis, psoriasis, sarcoidosis, and autoimmune bullous disease.¹⁸ Diarrhea, colitis, hepatitis, elevated liver enzymes, hypophysitis, and thyroiditis are some of the more common noncutaneous adverse effects reported with CTLA-4 inhibitors, while fatigue, diarrhea, nausea, pneumonitis, and thyroid disease are seen with anti-PD-1/PDL-1 therapy.³

A look at the prognosis

For patients diagnosed with primary cutaneous melanoma between 2011 and 2017, the 5-year survival rate for localized disease (Stages I-II) was 99%.¹ For regional (Stage III) and distant (Stage IV) disease, the 5-year survival rates were 68% and 30%, respectively.¹ With the advent of adjuvant systemic therapy, 5-year overall survival rates for metastatic melanoma have markedly improved from < 10% to up to 40% to 50%.⁴¹ The 3-year survival rate for patients with high tumor burden, brain metastasis, and elevated lactate dehydrogenase remains at < 10%.⁴² Relative survival decreases with increased age, although survival is higher in women than in men.⁴³ Risk of melanoma recurrence after surgical excision is high in patients with stage IIB, IIC, III and IV (resectable) disease. The most important risk factor for recurrence is primary tumor thickness.⁴⁴ The most common site of first recurrence in stage I-II disease is regional lymph node metastasis (42.8%), closely followed by distant metastasis (37.6%).⁴⁴

Long-term follow-up and surveillance

Recommendations for long-term care of patients with melanoma have evolved with advances in treatment, prognostication, and imaging. Caring for these patients requires a multidisciplinary approach wherein the family physician provides frontline care and team coordination. Since most recurrences are discovered by the patient or the patient’s family, patient education and self-examination are the cost-effective foundation for recurrence screening. In a trial of patients and partners, a 30-minute structured session on skin examination followed by physician reminders every 4 months increased the detection of melanoma recurrence without significant increases in patient visits.⁴⁵

Continue to: Patient education should include sun safety...

Patient education should include sun safety (wearing sun-protective clothing, using broad-spectrum sunscreen, and avoiding sun exposure during peak times of the day). The US Preventive Services Task Force (USPSTF) says the level of evidence is insufficient to support routine skin cancer screening in adults.⁴⁶ However, the USPSTF recommends discussing efforts to minimize UV radiation exposure to prevent skin cancer in fair-skinned individuals 10 to 24 years of age.

Current National Comprehensive Cancer Network (NCCN) guidelines have outlined the follow-up frequency for all melanoma patients. TABLE 2³² outlines those recommendations in addition to self-examination and patient education.

Melanoma epidemic or overdiagnosis?

Over the past 2 decades, a marked rise in the incidence of melanoma has been reported in developed countries worldwide, although melanoma mortality rates have not increased as rapidly, with melanoma-specific survival stable in most groups.^47-50 Due to conflicting evidence, significant disagreement exists as to whether this is an actual epidemic caused by a true rise in disease burden or is merely an artifact stemming from overdiagnosis.⁴⁷

Evidence supporting a true melanoma epidemic includes population-based studies demonstrating greater UV radiation–induced carcinogenesis (from the sun and tanning bed use), a larger aging population, and increased incidence regardless of socioeconomic status.⁴⁷ Those challenging the validity of an epidemic instead attribute the rising incidence to early-detection public awareness campaigns, expanded screenings, improved diagnostic modalities, and increased biopsies. They also credit lower pathologic thresholds that help identify thinner tumors with little to no metastatic potential.⁴⁸ Additionally, multiple studies report an increased incidence in melanomas of all histologic subtypes and thicknesses, not just thinner, more curable tumors.^49,51,52 Although increased screening and biopsies are effective, they alone cannot account for the sharp rise in melanoma cases.⁴⁷ This “melanoma paradox” of increasing incidence without a parallel increase in mortality remains unsettled.⁴⁷

CASE

Your patient had Stage IIA disease and a WE was performed with 1-cm margins. Ultrasound of the axilla identified an enlarged node, which was removed and found not to be diseased. He has now returned to have you look at another lesion identified by his spouse. His review of symptoms is negative. His initial melanoma was removed 2 years earlier, and his last dermatology skin exam was 5 months prior. You look at the lesion using a dermatoscope and do not note any worrisome features. You recommend that the patient photograph the area for reexamination and follow-up with his dermatologist next month for a 6-month follow-up.

CORRESPONDENCE
Jessica Servey, MD, 4301 Jones Bridge Road, Bethesda, MD 20814; [email protected]

CASE

A 48-year-old man comes to your clinic with a dark nevus on his right upper arm that appeared 2 months earlier. He says that the lesion has continued to grow and has bled (he thought because he initially picked at it). On exam, there is a 7-mm brown papule with 2 black dots and slightly asymmetric borders.

How would you proceed with this patient?

Melanoma is the fifth leading cause of new cancer cases annually, with > 96,000 new cases in 2019.¹ Overall, melanoma is more common in men and in Whites, with 48% diagnosed in people ages 55 to 74.¹ The past 2 decades have seen numerous developments in the diagnosis, treatment, and surveillance of melanoma. This article covers recommendations, controversies, and issues that require future study. It does not cover uveal or mucosal melanoma.

Evaluating a patient with a new or changing nevus

Known risk factors for melanoma include a changing nevus, indoor tanning, older age, many melanocytic nevi, history of a dysplastic nevus or of blistering sunburns during teen years, red or blonde hair, large congenital nevus, Fitzpatrick skin type I or II, high socioeconomic status, personal or family history of melanoma, and intermittent high-intensity sun exposure.^2-3 Presence of 1 or more of these risk factors should lower the threshold for biopsy.

Worrisome physical exam features (FIGURE) are nevus asymmetry, irregular borders, variegated color, and a diameter > 6 mm (the size of a pencil eraser). Inquire as to whether the nevus’ appearance has evolved and if it has bled without trauma. In a patient with multiple nevi, 1 nevus that looks different than the rest (the so-called “ugly duckling”) is concerning. Accuracy of diagnosis is enhanced with dermoscopy. A Cochrane review showed that skilled use of dermoscopy, in addition to inspection with the naked eye, considerably increases the sensitivity and specificity of diagnosing melanoma.⁴ Yet a 2017 study of 705 US primary care practitioners showed that only 8.3% of them used dermoscopy to evaluate pigmented lesions.⁵

Several published algorithms and checklists can aid clinicians in identifying lesions suggestive of melanoma—eg, ABCDE, CASH, Menzies method, “chaos and clues,” and 2-step and 3- and 7-point checklists.^6-10 A simple 3-step algorithm, the TADA (triage amalgamated dermoscopic algorithm) method is available to novice dermoscopy users.¹¹ Experts in pigmented lesions prefer to use pattern analysis, which requires simultaneously assessing multiple lesion patterns that vary according to body site.^12,13

Dermoscopic features suggesting melanoma are atypical pigment networks, pseudopods, radial streaking, irregular dots or globules, blue-whitish veil, and granularity or peppering.¹⁴ Appropriate and effective use of dermoscopy requires training.^15,16 Available methods for learning dermoscopy include online and in-person courses, mentoring by experienced dermoscopists, books and articles, and free apps and online resources.¹⁷

Continue to: Perform a skin biopsy, but do this first

Skin biopsy is the definitive way to diagnose melanoma. Prior to biopsy, take photographs to document the exact location of the lesion and to ensure that the correct area is removed in wide excision (WE). A complete biopsy should include the full depth and breadth of the lesion to ensure there are clinically negative margins. This can be achieved with an elliptical excision (for larger lesions), punch excision (for small lesions), or saucerization (deep shave with 1- to 2-mm peripheral margins, used for intermediate-size lesions).¹⁸ Saucerization is distinctly different from a superficial shave biopsy, which is not recommended for lesions with features of melanoma.¹⁹

A decision to perform a biopsy on a part of the lesion (partial biopsy) depends on the size of the lesion and its anatomic location, and is best made in agreement with the patient. If the lesion cannot be removed in its entirety and a partial biopsy is necessary, take the sample from the most atypical appearing area and communicate this decision to the pathologist on the biopsy order. There is no evidence that performing a partial biopsy increases the risk of spreading melanoma.^20,21

If you are untrained or uncomfortable performing the biopsy, contact a dermatologist immediately. In many communities, such referrals are subject to long delays, which further supports the advisability of family physicians doing their own biopsies after photographing the suspicious lesion. Many resources are available to help family physicians learn to do biopsies proficiently (www.mdedge.com/familymedicine/article/164358/oncology/biopsies-skin-cancer-detection-dispelling-myths).¹⁹

What to communicate to the pathologist. At a minimum, the biopsy request form should include patient age, sex, biopsy type (punch, excisional, or scoop shave), intention (complete or partial sample), exact site of the biopsy with laterality, and clinical details. These details should include the lesion size and clinical description, the suspected diagnosis, and clinical information, such as whether there is a history of bleeding or changing color, size, or symmetry. In standard biopsy specimens, the pathologist is only examining a portion of the lesion. Communicating clearly to the pathologist may lead to a request for deeper or additional sections or special stains.

Complete biopsy of a concerning pigmented lesion is the standard. If, however, a partial biopsy is necessary, sample the most atypical area.

If the biopsy results do not match the clinical impression, a phone call to the pathologist is warranted. In addition, evaluation by a dermatopathologist may be merited as pathologic diagnosis of melanoma can be quite challenging. Newer molecular tests, such as fluorescence in situ hybridization (FISH) and comparative genomic hybridization (CGH), can assist in the histologic evaluation of complex pigmented lesions.

Continue to: CASE

CASE

You perform an elliptical excisional biopsy on your patient. The biopsy report comes back as a nodular malignant melanoma, Breslow depth 2.5 mm without ulceration, and no evidence of lymphovascular invasion or microsatellitosis. The report states that the biopsy margins appear clear of tumor involvement.

Further evaluation when the biopsy result is positive

Key steps in initial patient care include relaying pathology results to the patient, conducting (as needed) a more extensive evaluation, and obtaining appropriate consultation.

Clearly explain the diagnosis and convey an accurate reading of the pathology report. The vital pieces of information in the biopsy report are the Breslow depth and presence of ulceration, as evidence shows these 2 factors to be important independent predictors of outcome.^22,23 Also important are the presence of microsatellitosis (essential for staging purposes), pathologic stage, and the status of the peripheral and deep biopsy margins. Review Breslow depth with the patient as this largely dictates treatment options and prognosis.

Evaluate for possible metastatic disease. Obtain a complete history from every patient with cutaneous melanoma, looking for any positive review of systems as a harbinger of metastatic disease. A full-body skin and lymph node exam is vital, given that melanoma can arise anywhere including on the scalp, in the gluteal cleft, and beneath nails. If the lymph node exam is worrisome, conduct an ultrasound exam, even while referring to specialty care. Treating a patient with melanoma requires a multidisciplinary approach that may include dermatologists, surgeons, and oncologists based on the stage of disease. A challenge for family physicians is knowing which consultation to prioritize and how to counsel the patient to schedule these for the most cost-effective and timely evaluation.

Expedite a dermatology consultation. If the melanoma is deep or appears advanced based on size or palpable lymph nodes, contact the dermatologist immediately by phone to set up a rapid referral. Delays in the definitive management of thick melanomas can negatively affect outcome. Paper, facsimile, or electronic referrals can get lost in the system and are not reliable methods for referring patients for a melanoma consultation. One benefit of the family physician performing the initial biopsy is that a confirmed melanoma diagnosis will almost certainly get an expedited dermatology appointment.

Continue to: Wide excision and sentinel node biopsy

Wide excision and sentinel node biopsy

Wide excision of a primary melanoma is standard practice, with evidence favoring the following surgical margins: 0.5 to 1 cm for melanoma in situ, 1 cm for tumors up to 1 mm in thickness, 1 to 2 cm for tumors > 1 to 2 mm thick, and 2 cm for tumors > 2 mm thick.¹⁸ WE is often performed by dermatologists for nonulcerated tumors < 0.8 mm thick (T1a) without adverse features. If trained in cutaneous surgery, you can also choose to excise these thin melanomas in your office. Otherwise refer all patients with biopsy-proven melanoma to dermatologists to perform an adequate WE.

Refer patients who have tumors ≥ 0.8 mm thick to the appropriate surgical specialty (surgical oncology, if available) for consultation on sentinel lymph node biopsy. SLNB, when indicated, should be performed prior to WE of the primary tumor, and whenever possible in the same surgical setting, to maximize lymphatic drainage mapping techniques.¹⁸ Medical oncology referral, if needed, is usually made after WE.

SLNB remains the standard for lymph node staging. It is controversial mainly in its use for very thin or very thick lesions. Randomized controlled trials, including the Multicenter Selective Lymphadenectomy Trial,²⁴ have shown no difference in melanoma-­specific survival for patients with intermediate-thickness melanomas who had undergone SLNB.²⁴However, a subgroup analysis did find a significant increase in melanoma survival among patients with positive early SLNB results and immediate lymphadenectomy compared with patients who were observed and subsequently underwent lymphadenectomy only as metastases developed.²⁴

Many professional organizations consider SLNB to be the most significant prognostic indicator of disease recurrence. With a negative SLNB result, the risk of regional node recurrence is 5% or lower.^18,25 In addition, sentinel lymph node status is a critical determinant for systemic adjuvant therapy consideration and clinical trial eligibility. For patients who have primary cutaneous melanoma without clinical lymphadenopathy, an online tool is available for patients to use with their physician in predicting the likelihood of SLNB positivity.²⁶

Recommendations for SLNB, supported by multidisciplinary consensus:¹⁸

• Do not pursue SLNB for melanoma in situ or most cutaneous melanomas < 0.8 mm without ulceration (T1a). (See TABLE 1²⁷)
• Discuss SLNB with patients who have T1a melanoma and additional adverse features: young age, high mitotic rate, lymphovascular invasion, and nevus depth close to 0.8 mm with positive deep biopsy margins.
• Discuss SLNB with patients who have T1b disease (< 0.8 mm with ulceration, or 0.8-1 mm), although rates of SLNB positivity are low.
• Offer SLNB to patients with T2a and higher disease (> 1 mm).¹⁸

Continue to: Patients who have...

Patients who have clinical Stage I or II disease (TABLE 1²⁷) and a negative review of systems and lymph node exam do not require baseline laboratory or radiology tests.^18,28-31 Ultrasonography of the lymph node basin is recommended for any Stage I or II patient with an equivocal lymph node exam.^27,32 Stage III disease warrants computed tomography of the chest, abdomen, and pelvis, and possibly magnetic resonance imaging (MRI) of the brain. For Stage IV, brain MRI is recommended in all patients. Patients with higher risk disease (IIB - IV) will need consultation with Medical Oncology. The surgery and oncology team will make decisions regarding SLNB, genetic testing, and chemotherapy.

Melanoma in women: Considerations to keep in mind

Hormonal influences of pregnancy, lactation, contraception, and menopause introduce special considerations regarding melanoma, which is the most common cancer occurring during pregnancy, accounting for 31% of new malignancies.³³ Risk of melanoma lessens, however, for women who first give birth at a younger age or who have had > 5 live births.^18,34,35 There is no evidence that nevi darken during pregnancy, although nevi on the breast and abdomen may seem to enlarge due to skin stretching.¹⁸ All changing nevi in pregnancy warrant an examination, preferably with dermoscopy, and patients should be offered biopsy if there are any nevus characteristics associated with melanoma.¹⁸

One benefit of the family physician performing the initial biopsy is that a confirmed melanoma diagnosis will almost certainly get an expedited dermatology appointment.

The effect of pregnancy on an existing melanoma is not fully understood, but evidence from controlled studies shows no negative effect. Recent working group guidelines advise WE with local anesthesia without delay in pregnant patients.¹⁸ Definitive treatment after melanoma diagnosis should take a multidisciplinary approach involving obstetric care coordinated with Dermatology, Surgery, and Medical Oncology.¹⁸

Most recommendations on the timing of pregnancy following a melanoma diagnosis have limited evidence. One meta-­analysis concluded that pregnancy occurring after successful treatment of melanoma did not change a woman’s prognosis.³⁶ Current guidelines do not recommend delaying future pregnancy if a woman had an early-stage melanoma. For melanomas deemed higher risk, a woman could consider a 2- to 3-year delay in the next planned pregnancy, owing to current data on recurrence rates.¹⁸

A systematic review of women who used hormonal contraception or postmenopausal hormone replacement therapy (HRT) showed no associated increased risk of melanoma.³⁵ An additional randomized trial showed no effect of HRT on melanoma risk.³⁷

Continue to: Systemic melanoma treatment and common adverse effects

Multiple systemic therapies have been approved for the treatment of advanced or unresectable cutaneous melanomas. While these treatments are managed primarily by Oncology in concert with Dermatology, an awareness of the medications’ common dermatologic toxicities is important for the primary care provider. The 2 broad categories of FDA-approved systemic medications for advanced melanoma are mitogen-activated protein kinase (MAPK) inhibitors and immune checkpoint inhibitors, each having its own set of adverse cutaneous effects.

MAPK pathway–targeting drugs include the B-Raf proto-oncogene serine/threonine-kinase inhibitors (BRAFIs) vemurafenib and dabrafenib, and the MAPK inhibitors (MEKIs) trametinib and cobimetinib. The most common adverse skin effects in MAPK pathway–targeting drugs are severe ultraviolet photosensitivity, cutaneous epidermal neoplasms (particularly squamous cell carcinoma, keratoacanthoma-type), thick actinic keratosis, wart-like keratosis, painful palmoplantar keratosis, and dry skin.³⁸ These effects are most commonly seen with BRAFI monotherapy and can be abated with the addition of a MEKI. MEKI therapy can cause acneiform eruptions and paronychia.³⁹ Additional adverse effects include diarrhea, pyrexia, arthralgias, and fatigue for BRAFIs and diarrhea, fatigue, and peripheral edema for MEKIs.⁴⁰

Immune checkpoint inhibitors include anti-CTLA-4 (ipilimumab), anti-PD-1 (pembrolizumab and nivolumab), and anti-PDL-1 (atezolizumab). Adverse skin effects include morbilliform rash with or without an associated itch, itch with or without an associated rash, vitiligo, and lichenoid skin rashes. PD-1 and PDL-1 inhibitors have been associated with flares or unmasking of atopic dermatitis, psoriasis, sarcoidosis, and autoimmune bullous disease.¹⁸ Diarrhea, colitis, hepatitis, elevated liver enzymes, hypophysitis, and thyroiditis are some of the more common noncutaneous adverse effects reported with CTLA-4 inhibitors, while fatigue, diarrhea, nausea, pneumonitis, and thyroid disease are seen with anti-PD-1/PDL-1 therapy.³

A look at the prognosis

For patients diagnosed with primary cutaneous melanoma between 2011 and 2017, the 5-year survival rate for localized disease (Stages I-II) was 99%.¹ For regional (Stage III) and distant (Stage IV) disease, the 5-year survival rates were 68% and 30%, respectively.¹ With the advent of adjuvant systemic therapy, 5-year overall survival rates for metastatic melanoma have markedly improved from < 10% to up to 40% to 50%.⁴¹ The 3-year survival rate for patients with high tumor burden, brain metastasis, and elevated lactate dehydrogenase remains at < 10%.⁴² Relative survival decreases with increased age, although survival is higher in women than in men.⁴³ Risk of melanoma recurrence after surgical excision is high in patients with stage IIB, IIC, III and IV (resectable) disease. The most important risk factor for recurrence is primary tumor thickness.⁴⁴ The most common site of first recurrence in stage I-II disease is regional lymph node metastasis (42.8%), closely followed by distant metastasis (37.6%).⁴⁴

Long-term follow-up and surveillance

Recommendations for long-term care of patients with melanoma have evolved with advances in treatment, prognostication, and imaging. Caring for these patients requires a multidisciplinary approach wherein the family physician provides frontline care and team coordination. Since most recurrences are discovered by the patient or the patient’s family, patient education and self-examination are the cost-effective foundation for recurrence screening. In a trial of patients and partners, a 30-minute structured session on skin examination followed by physician reminders every 4 months increased the detection of melanoma recurrence without significant increases in patient visits.⁴⁵

Continue to: Patient education should include sun safety...

Patient education should include sun safety (wearing sun-protective clothing, using broad-spectrum sunscreen, and avoiding sun exposure during peak times of the day). The US Preventive Services Task Force (USPSTF) says the level of evidence is insufficient to support routine skin cancer screening in adults.⁴⁶ However, the USPSTF recommends discussing efforts to minimize UV radiation exposure to prevent skin cancer in fair-skinned individuals 10 to 24 years of age.

Current National Comprehensive Cancer Network (NCCN) guidelines have outlined the follow-up frequency for all melanoma patients. TABLE 2³² outlines those recommendations in addition to self-examination and patient education.

Melanoma epidemic or overdiagnosis?

Over the past 2 decades, a marked rise in the incidence of melanoma has been reported in developed countries worldwide, although melanoma mortality rates have not increased as rapidly, with melanoma-specific survival stable in most groups.^47-50 Due to conflicting evidence, significant disagreement exists as to whether this is an actual epidemic caused by a true rise in disease burden or is merely an artifact stemming from overdiagnosis.⁴⁷

Evidence supporting a true melanoma epidemic includes population-based studies demonstrating greater UV radiation–induced carcinogenesis (from the sun and tanning bed use), a larger aging population, and increased incidence regardless of socioeconomic status.⁴⁷ Those challenging the validity of an epidemic instead attribute the rising incidence to early-detection public awareness campaigns, expanded screenings, improved diagnostic modalities, and increased biopsies. They also credit lower pathologic thresholds that help identify thinner tumors with little to no metastatic potential.⁴⁸ Additionally, multiple studies report an increased incidence in melanomas of all histologic subtypes and thicknesses, not just thinner, more curable tumors.^49,51,52 Although increased screening and biopsies are effective, they alone cannot account for the sharp rise in melanoma cases.⁴⁷ This “melanoma paradox” of increasing incidence without a parallel increase in mortality remains unsettled.⁴⁷

CASE

Your patient had Stage IIA disease and a WE was performed with 1-cm margins. Ultrasound of the axilla identified an enlarged node, which was removed and found not to be diseased. He has now returned to have you look at another lesion identified by his spouse. His review of symptoms is negative. His initial melanoma was removed 2 years earlier, and his last dermatology skin exam was 5 months prior. You look at the lesion using a dermatoscope and do not note any worrisome features. You recommend that the patient photograph the area for reexamination and follow-up with his dermatologist next month for a 6-month follow-up.

CORRESPONDENCE
Jessica Servey, MD, 4301 Jones Bridge Road, Bethesda, MD 20814; [email protected]

CASE

A 48-year-old man comes to your clinic with a dark nevus on his right upper arm that appeared 2 months earlier. He says that the lesion has continued to grow and has bled (he thought because he initially picked at it). On exam, there is a 7-mm brown papule with 2 black dots and slightly asymmetric borders.

How would you proceed with this patient?

Melanoma is the fifth leading cause of new cancer cases annually, with > 96,000 new cases in 2019.¹ Overall, melanoma is more common in men and in Whites, with 48% diagnosed in people ages 55 to 74.¹ The past 2 decades have seen numerous developments in the diagnosis, treatment, and surveillance of melanoma. This article covers recommendations, controversies, and issues that require future study. It does not cover uveal or mucosal melanoma.

Evaluating a patient with a new or changing nevus

Known risk factors for melanoma include a changing nevus, indoor tanning, older age, many melanocytic nevi, history of a dysplastic nevus or of blistering sunburns during teen years, red or blonde hair, large congenital nevus, Fitzpatrick skin type I or II, high socioeconomic status, personal or family history of melanoma, and intermittent high-intensity sun exposure.^2-3 Presence of 1 or more of these risk factors should lower the threshold for biopsy.

Worrisome physical exam features (FIGURE) are nevus asymmetry, irregular borders, variegated color, and a diameter > 6 mm (the size of a pencil eraser). Inquire as to whether the nevus’ appearance has evolved and if it has bled without trauma. In a patient with multiple nevi, 1 nevus that looks different than the rest (the so-called “ugly duckling”) is concerning. Accuracy of diagnosis is enhanced with dermoscopy. A Cochrane review showed that skilled use of dermoscopy, in addition to inspection with the naked eye, considerably increases the sensitivity and specificity of diagnosing melanoma.⁴ Yet a 2017 study of 705 US primary care practitioners showed that only 8.3% of them used dermoscopy to evaluate pigmented lesions.⁵

Several published algorithms and checklists can aid clinicians in identifying lesions suggestive of melanoma—eg, ABCDE, CASH, Menzies method, “chaos and clues,” and 2-step and 3- and 7-point checklists.^6-10 A simple 3-step algorithm, the TADA (triage amalgamated dermoscopic algorithm) method is available to novice dermoscopy users.¹¹ Experts in pigmented lesions prefer to use pattern analysis, which requires simultaneously assessing multiple lesion patterns that vary according to body site.^12,13

Dermoscopic features suggesting melanoma are atypical pigment networks, pseudopods, radial streaking, irregular dots or globules, blue-whitish veil, and granularity or peppering.¹⁴ Appropriate and effective use of dermoscopy requires training.^15,16 Available methods for learning dermoscopy include online and in-person courses, mentoring by experienced dermoscopists, books and articles, and free apps and online resources.¹⁷

Continue to: Perform a skin biopsy, but do this first

Skin biopsy is the definitive way to diagnose melanoma. Prior to biopsy, take photographs to document the exact location of the lesion and to ensure that the correct area is removed in wide excision (WE). A complete biopsy should include the full depth and breadth of the lesion to ensure there are clinically negative margins. This can be achieved with an elliptical excision (for larger lesions), punch excision (for small lesions), or saucerization (deep shave with 1- to 2-mm peripheral margins, used for intermediate-size lesions).¹⁸ Saucerization is distinctly different from a superficial shave biopsy, which is not recommended for lesions with features of melanoma.¹⁹

A decision to perform a biopsy on a part of the lesion (partial biopsy) depends on the size of the lesion and its anatomic location, and is best made in agreement with the patient. If the lesion cannot be removed in its entirety and a partial biopsy is necessary, take the sample from the most atypical appearing area and communicate this decision to the pathologist on the biopsy order. There is no evidence that performing a partial biopsy increases the risk of spreading melanoma.^20,21

If you are untrained or uncomfortable performing the biopsy, contact a dermatologist immediately. In many communities, such referrals are subject to long delays, which further supports the advisability of family physicians doing their own biopsies after photographing the suspicious lesion. Many resources are available to help family physicians learn to do biopsies proficiently (www.mdedge.com/familymedicine/article/164358/oncology/biopsies-skin-cancer-detection-dispelling-myths).¹⁹

What to communicate to the pathologist. At a minimum, the biopsy request form should include patient age, sex, biopsy type (punch, excisional, or scoop shave), intention (complete or partial sample), exact site of the biopsy with laterality, and clinical details. These details should include the lesion size and clinical description, the suspected diagnosis, and clinical information, such as whether there is a history of bleeding or changing color, size, or symmetry. In standard biopsy specimens, the pathologist is only examining a portion of the lesion. Communicating clearly to the pathologist may lead to a request for deeper or additional sections or special stains.

Complete biopsy of a concerning pigmented lesion is the standard. If, however, a partial biopsy is necessary, sample the most atypical area.

If the biopsy results do not match the clinical impression, a phone call to the pathologist is warranted. In addition, evaluation by a dermatopathologist may be merited as pathologic diagnosis of melanoma can be quite challenging. Newer molecular tests, such as fluorescence in situ hybridization (FISH) and comparative genomic hybridization (CGH), can assist in the histologic evaluation of complex pigmented lesions.

Continue to: CASE

CASE

You perform an elliptical excisional biopsy on your patient. The biopsy report comes back as a nodular malignant melanoma, Breslow depth 2.5 mm without ulceration, and no evidence of lymphovascular invasion or microsatellitosis. The report states that the biopsy margins appear clear of tumor involvement.

Further evaluation when the biopsy result is positive

Key steps in initial patient care include relaying pathology results to the patient, conducting (as needed) a more extensive evaluation, and obtaining appropriate consultation.

Clearly explain the diagnosis and convey an accurate reading of the pathology report. The vital pieces of information in the biopsy report are the Breslow depth and presence of ulceration, as evidence shows these 2 factors to be important independent predictors of outcome.^22,23 Also important are the presence of microsatellitosis (essential for staging purposes), pathologic stage, and the status of the peripheral and deep biopsy margins. Review Breslow depth with the patient as this largely dictates treatment options and prognosis.

Evaluate for possible metastatic disease. Obtain a complete history from every patient with cutaneous melanoma, looking for any positive review of systems as a harbinger of metastatic disease. A full-body skin and lymph node exam is vital, given that melanoma can arise anywhere including on the scalp, in the gluteal cleft, and beneath nails. If the lymph node exam is worrisome, conduct an ultrasound exam, even while referring to specialty care. Treating a patient with melanoma requires a multidisciplinary approach that may include dermatologists, surgeons, and oncologists based on the stage of disease. A challenge for family physicians is knowing which consultation to prioritize and how to counsel the patient to schedule these for the most cost-effective and timely evaluation.

Expedite a dermatology consultation. If the melanoma is deep or appears advanced based on size or palpable lymph nodes, contact the dermatologist immediately by phone to set up a rapid referral. Delays in the definitive management of thick melanomas can negatively affect outcome. Paper, facsimile, or electronic referrals can get lost in the system and are not reliable methods for referring patients for a melanoma consultation. One benefit of the family physician performing the initial biopsy is that a confirmed melanoma diagnosis will almost certainly get an expedited dermatology appointment.

Continue to: Wide excision and sentinel node biopsy

Wide excision and sentinel node biopsy

Wide excision of a primary melanoma is standard practice, with evidence favoring the following surgical margins: 0.5 to 1 cm for melanoma in situ, 1 cm for tumors up to 1 mm in thickness, 1 to 2 cm for tumors > 1 to 2 mm thick, and 2 cm for tumors > 2 mm thick.¹⁸ WE is often performed by dermatologists for nonulcerated tumors < 0.8 mm thick (T1a) without adverse features. If trained in cutaneous surgery, you can also choose to excise these thin melanomas in your office. Otherwise refer all patients with biopsy-proven melanoma to dermatologists to perform an adequate WE.

Refer patients who have tumors ≥ 0.8 mm thick to the appropriate surgical specialty (surgical oncology, if available) for consultation on sentinel lymph node biopsy. SLNB, when indicated, should be performed prior to WE of the primary tumor, and whenever possible in the same surgical setting, to maximize lymphatic drainage mapping techniques.¹⁸ Medical oncology referral, if needed, is usually made after WE.

SLNB remains the standard for lymph node staging. It is controversial mainly in its use for very thin or very thick lesions. Randomized controlled trials, including the Multicenter Selective Lymphadenectomy Trial,²⁴ have shown no difference in melanoma-­specific survival for patients with intermediate-thickness melanomas who had undergone SLNB.²⁴However, a subgroup analysis did find a significant increase in melanoma survival among patients with positive early SLNB results and immediate lymphadenectomy compared with patients who were observed and subsequently underwent lymphadenectomy only as metastases developed.²⁴

Many professional organizations consider SLNB to be the most significant prognostic indicator of disease recurrence. With a negative SLNB result, the risk of regional node recurrence is 5% or lower.^18,25 In addition, sentinel lymph node status is a critical determinant for systemic adjuvant therapy consideration and clinical trial eligibility. For patients who have primary cutaneous melanoma without clinical lymphadenopathy, an online tool is available for patients to use with their physician in predicting the likelihood of SLNB positivity.²⁶

Recommendations for SLNB, supported by multidisciplinary consensus:¹⁸

• Do not pursue SLNB for melanoma in situ or most cutaneous melanomas < 0.8 mm without ulceration (T1a). (See TABLE 1²⁷)
• Discuss SLNB with patients who have T1a melanoma and additional adverse features: young age, high mitotic rate, lymphovascular invasion, and nevus depth close to 0.8 mm with positive deep biopsy margins.
• Discuss SLNB with patients who have T1b disease (< 0.8 mm with ulceration, or 0.8-1 mm), although rates of SLNB positivity are low.
• Offer SLNB to patients with T2a and higher disease (> 1 mm).¹⁸

Continue to: Patients who have...

Patients who have clinical Stage I or II disease (TABLE 1²⁷) and a negative review of systems and lymph node exam do not require baseline laboratory or radiology tests.^18,28-31 Ultrasonography of the lymph node basin is recommended for any Stage I or II patient with an equivocal lymph node exam.^27,32 Stage III disease warrants computed tomography of the chest, abdomen, and pelvis, and possibly magnetic resonance imaging (MRI) of the brain. For Stage IV, brain MRI is recommended in all patients. Patients with higher risk disease (IIB - IV) will need consultation with Medical Oncology. The surgery and oncology team will make decisions regarding SLNB, genetic testing, and chemotherapy.

Melanoma in women: Considerations to keep in mind

Hormonal influences of pregnancy, lactation, contraception, and menopause introduce special considerations regarding melanoma, which is the most common cancer occurring during pregnancy, accounting for 31% of new malignancies.³³ Risk of melanoma lessens, however, for women who first give birth at a younger age or who have had > 5 live births.^18,34,35 There is no evidence that nevi darken during pregnancy, although nevi on the breast and abdomen may seem to enlarge due to skin stretching.¹⁸ All changing nevi in pregnancy warrant an examination, preferably with dermoscopy, and patients should be offered biopsy if there are any nevus characteristics associated with melanoma.¹⁸

One benefit of the family physician performing the initial biopsy is that a confirmed melanoma diagnosis will almost certainly get an expedited dermatology appointment.

The effect of pregnancy on an existing melanoma is not fully understood, but evidence from controlled studies shows no negative effect. Recent working group guidelines advise WE with local anesthesia without delay in pregnant patients.¹⁸ Definitive treatment after melanoma diagnosis should take a multidisciplinary approach involving obstetric care coordinated with Dermatology, Surgery, and Medical Oncology.¹⁸

Most recommendations on the timing of pregnancy following a melanoma diagnosis have limited evidence. One meta-­analysis concluded that pregnancy occurring after successful treatment of melanoma did not change a woman’s prognosis.³⁶ Current guidelines do not recommend delaying future pregnancy if a woman had an early-stage melanoma. For melanomas deemed higher risk, a woman could consider a 2- to 3-year delay in the next planned pregnancy, owing to current data on recurrence rates.¹⁸

A systematic review of women who used hormonal contraception or postmenopausal hormone replacement therapy (HRT) showed no associated increased risk of melanoma.³⁵ An additional randomized trial showed no effect of HRT on melanoma risk.³⁷

Continue to: Systemic melanoma treatment and common adverse effects

Multiple systemic therapies have been approved for the treatment of advanced or unresectable cutaneous melanomas. While these treatments are managed primarily by Oncology in concert with Dermatology, an awareness of the medications’ common dermatologic toxicities is important for the primary care provider. The 2 broad categories of FDA-approved systemic medications for advanced melanoma are mitogen-activated protein kinase (MAPK) inhibitors and immune checkpoint inhibitors, each having its own set of adverse cutaneous effects.

MAPK pathway–targeting drugs include the B-Raf proto-oncogene serine/threonine-kinase inhibitors (BRAFIs) vemurafenib and dabrafenib, and the MAPK inhibitors (MEKIs) trametinib and cobimetinib. The most common adverse skin effects in MAPK pathway–targeting drugs are severe ultraviolet photosensitivity, cutaneous epidermal neoplasms (particularly squamous cell carcinoma, keratoacanthoma-type), thick actinic keratosis, wart-like keratosis, painful palmoplantar keratosis, and dry skin.³⁸ These effects are most commonly seen with BRAFI monotherapy and can be abated with the addition of a MEKI. MEKI therapy can cause acneiform eruptions and paronychia.³⁹ Additional adverse effects include diarrhea, pyrexia, arthralgias, and fatigue for BRAFIs and diarrhea, fatigue, and peripheral edema for MEKIs.⁴⁰

Immune checkpoint inhibitors include anti-CTLA-4 (ipilimumab), anti-PD-1 (pembrolizumab and nivolumab), and anti-PDL-1 (atezolizumab). Adverse skin effects include morbilliform rash with or without an associated itch, itch with or without an associated rash, vitiligo, and lichenoid skin rashes. PD-1 and PDL-1 inhibitors have been associated with flares or unmasking of atopic dermatitis, psoriasis, sarcoidosis, and autoimmune bullous disease.¹⁸ Diarrhea, colitis, hepatitis, elevated liver enzymes, hypophysitis, and thyroiditis are some of the more common noncutaneous adverse effects reported with CTLA-4 inhibitors, while fatigue, diarrhea, nausea, pneumonitis, and thyroid disease are seen with anti-PD-1/PDL-1 therapy.³

A look at the prognosis

For patients diagnosed with primary cutaneous melanoma between 2011 and 2017, the 5-year survival rate for localized disease (Stages I-II) was 99%.¹ For regional (Stage III) and distant (Stage IV) disease, the 5-year survival rates were 68% and 30%, respectively.¹ With the advent of adjuvant systemic therapy, 5-year overall survival rates for metastatic melanoma have markedly improved from < 10% to up to 40% to 50%.⁴¹ The 3-year survival rate for patients with high tumor burden, brain metastasis, and elevated lactate dehydrogenase remains at < 10%.⁴² Relative survival decreases with increased age, although survival is higher in women than in men.⁴³ Risk of melanoma recurrence after surgical excision is high in patients with stage IIB, IIC, III and IV (resectable) disease. The most important risk factor for recurrence is primary tumor thickness.⁴⁴ The most common site of first recurrence in stage I-II disease is regional lymph node metastasis (42.8%), closely followed by distant metastasis (37.6%).⁴⁴

Long-term follow-up and surveillance

Recommendations for long-term care of patients with melanoma have evolved with advances in treatment, prognostication, and imaging. Caring for these patients requires a multidisciplinary approach wherein the family physician provides frontline care and team coordination. Since most recurrences are discovered by the patient or the patient’s family, patient education and self-examination are the cost-effective foundation for recurrence screening. In a trial of patients and partners, a 30-minute structured session on skin examination followed by physician reminders every 4 months increased the detection of melanoma recurrence without significant increases in patient visits.⁴⁵

Continue to: Patient education should include sun safety...

Patient education should include sun safety (wearing sun-protective clothing, using broad-spectrum sunscreen, and avoiding sun exposure during peak times of the day). The US Preventive Services Task Force (USPSTF) says the level of evidence is insufficient to support routine skin cancer screening in adults.⁴⁶ However, the USPSTF recommends discussing efforts to minimize UV radiation exposure to prevent skin cancer in fair-skinned individuals 10 to 24 years of age.

Current National Comprehensive Cancer Network (NCCN) guidelines have outlined the follow-up frequency for all melanoma patients. TABLE 2³² outlines those recommendations in addition to self-examination and patient education.

Melanoma epidemic or overdiagnosis?

Over the past 2 decades, a marked rise in the incidence of melanoma has been reported in developed countries worldwide, although melanoma mortality rates have not increased as rapidly, with melanoma-specific survival stable in most groups.^47-50 Due to conflicting evidence, significant disagreement exists as to whether this is an actual epidemic caused by a true rise in disease burden or is merely an artifact stemming from overdiagnosis.⁴⁷

Evidence supporting a true melanoma epidemic includes population-based studies demonstrating greater UV radiation–induced carcinogenesis (from the sun and tanning bed use), a larger aging population, and increased incidence regardless of socioeconomic status.⁴⁷ Those challenging the validity of an epidemic instead attribute the rising incidence to early-detection public awareness campaigns, expanded screenings, improved diagnostic modalities, and increased biopsies. They also credit lower pathologic thresholds that help identify thinner tumors with little to no metastatic potential.⁴⁸ Additionally, multiple studies report an increased incidence in melanomas of all histologic subtypes and thicknesses, not just thinner, more curable tumors.^49,51,52 Although increased screening and biopsies are effective, they alone cannot account for the sharp rise in melanoma cases.⁴⁷ This “melanoma paradox” of increasing incidence without a parallel increase in mortality remains unsettled.⁴⁷

CASE

Your patient had Stage IIA disease and a WE was performed with 1-cm margins. Ultrasound of the axilla identified an enlarged node, which was removed and found not to be diseased. He has now returned to have you look at another lesion identified by his spouse. His review of symptoms is negative. His initial melanoma was removed 2 years earlier, and his last dermatology skin exam was 5 months prior. You look at the lesion using a dermatoscope and do not note any worrisome features. You recommend that the patient photograph the area for reexamination and follow-up with his dermatologist next month for a 6-month follow-up.

CORRESPONDENCE
Jessica Servey, MD, 4301 Jones Bridge Road, Bethesda, MD 20814; [email protected]