Rapid response teams (RRT) have been promoted by numerous patient safety organizations to reduce preventable in‐hospital deaths.14 Initial studies of RRTs were promising,57 but recent literature,811 including systematic reviews and meta‐analyses, has called these findings into question. Nevertheless, RRTs remain popular in academic and community hospitals worldwide, and many have expanded their roles beyond solely responding to the deteriorating patient.12

Some RRTs, for example, proactively round on seriously ill ward patients and patients recently discharged from the intensive care unit (ICU) in an effort to prevent transitions to higher levels of care. Priestley and colleagues demonstrated that institution of such a team, referred to as a critical care outreach team (CCOT), decreased in‐hospital mortality while possibly increasing hospital length of stay (LOS).13 Three additional single‐center studies from the United Kingdom, where CCOTs are common, specifically examined proactive rounding by CCOTs on the ICU readmission rate: 2 observed no improvement,14, 15 while the third, limited by a small sample size, demonstrated a modest reduction in ICU readmissions.16

We sought to determine the impact of proactive rounding by an RRT on patients discharged from intensive care on the ICU readmission rate, ICU LOS, and in‐hospital mortality of patients discharged from the ICU. We hypothesized that proactive rounding by an RRT would decrease the ICU readmission rate, ICU LOS, and the in‐hospital mortality of patients discharged from the ICU.

MATERIALS AND METHODS

RESULTS

ICU Readmission Rate

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in the ICU readmission rate (6.7% preintervention vs 7.3% postintervention, P = 0.24; Table 1). In the adjusted ITS model, the intervention had no net effect on the odds of ICU readmission (adjusted odds ratio [AOR] for net intervention effect 0.98, 95% confidence interval [CI] 0.42, 2.28), with similar secular trends both preintervention (AOR 1.00 per year, 95% CI 0.97, 1.03), and afterwards (AOR 0.99 per year, 95% CI 0.98, 01.00), and a nonsignificant increase at implementation (Table 2). Figure 1 uses solid lines to show the fitted readmission rates, a hatched line to show the projection of the preintervention secular trend into the postintervention period, and circles to represent adjusted monthly means. The lack of a net intervention effect is indicated by the convergence of the solid and hatched lines 24 months postintervention.

Figure 1

Table 2.Adjusted Impact of Proactive Rounding by an RRT on Clinical Outcomes

Outcome: Summary Effect Measure	Value (95% CI)	P Value
NOTE: ICU readmission model adjusted for attending service, age, race/ethnicity, comorbidities (chronic pulmonary disease, weight loss, anemia, neurological disorders, rheumatoid arthritis, and solid tumors without metastasis), and clustering at the attending physician level. Length of stay model adjusted for attending service, age, race/ethnicity, comorbidities (drug abuse, rheumatoid arthritis, anemia, weight loss, paralysis, pulmonary circulation disease, neurological disorders, hypothyroidism, peptic ulcer disease, and solid tumors without metastasis), and clustering at the attending physician level. Mortality model adjusted for attending service, age, race/ethnicity, comorbidities (weight loss, lymphoma, metastatic cancer, chronic pulmonary and pulmonary circulation disease, and paralysis), and clustering at the attending physician level. Abbreviations: CI, confidence interval; ICU, intensive care unit; RRT, rapid response teams.
ICU readmission rateadjusted odds ratio
Pre‐RRT trend	1.00 (0.97, 1.03)	0.98
Change at RRT implementation	1.24 (0.94, 1.63)	0.13
Post‐RRT trend	0.98 (0.97, 1.00)	0.06
Change in trend	0.98 (0.96, 1.02)	0.39
Net intervention effect	0.92 (0.40, 2.12)	0.85
ICU average length of stayadjusted ratio of means
Trend at 9 mo pre‐RRT	0.98 (0.96, 1.00)	0.05
Trend at 3 mo pre‐RRT	1.02 (0.99, 1.04)	0.19
Change in trend at 3 mo pre‐RRT	1.03 (1.00, 1.07)	0.07
Change at RRT implementation	0.92 (0.80, 1.06)	0.27
Post‐RRT trend	1.00 (0.99, 1.00)	0.35
Change in trend at RRT implementation	0.98 (0.96, 1.01)	0.14
Net intervention effect	0.60 (0.31, 1.18)	0.14
In‐hospital mortality of patients discharged from the ICUadjusted odds ratio
Pre‐RRT trend	1.02 (0.99, 1.06)	0.15
Change at RRT implementation	0.74 (0.51, 1.08)	0.12
Post‐RRT trend	1.00 (0.98, 1.01)	0.68
Change in trend	0.97 (0.94, 1.01)	0.14
Net intervention effect	0.39 (0.14, 1.10)	0.08

ICU Average LOS

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in ICU average LOS (5.1 days preintervention vs 4.9 days postintervention, P = 0.24; Table 1). Trends in ICU LOS may have changed in October 2006 (P = 0.07), decreasing in the first half of the study period (adjusted rate ratio [ARR] 0.98 per year, 95% CI 0.961.00), but did not change significantly thereafter. As with the ICU readmission rate, neither the change in estimated secular trend after implementation (ARR 0.98, 95% CI 0.961.01), nor the net effect of the intervention (ARR 0.62, 95% CI 0.321.22) was statistically significant (Table 2); these results are depicted graphically in Figure 2.

Figure 2

In‐Hospital Mortality of Patients Discharged From the ICU

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in the mortality of patients discharged from the ICU (6.0% preintervention vs 5.5% postintervention, P = 0.24; Table 1). Similarly, in the adjusted ITS model, the intervention had no statistically significant net effect on the mortality outcome (Table 2 and Figure 3).

Figure 3

DISCUSSION

In this single center study, introduction of an RRT that proactively rounded on patients discharged from the ICU did not reduce the ICU readmission rate, ICU LOS, or mortality of patients discharged from the ICU, after accounting for secular trends using robust ITS methods and adjusting for patient level factors.

Our study is consistent with 2 smaller studies that assessed the impact of proactive rounding by a CCOT on ICU readmission rate. Leary and Ridley14 found that proactively rounding by a CCOT did not reduce ICU readmissions or shorten the ICU LOS, although this study was limited by a surprisingly low ICU readmission rate and short ICU LOS prior to the intervention. Another study15 also observed no change in the ICU readmission rate following introduction of a proactively rounding CCOT but noted small reductions in both ICU and hospital mortality. The sole study showing an effect16 observed a lower ICU readmission rate and increased survival to hospital discharge (after excluding do not resuscitate [DNR] patients) with implementation of a CCOT, although some of their findings may be explained by their CCOT's use of palliative care services, a function not featured in our model.

Our study adds to the meta‐analyses and systematic reviews810 that have questioned the hypothesis that a trained and proactive team of caregivers should be able to prevent patients from returning to the ICU. Perhaps one reason why this is not true is that proactive rounding by RRTs may have minimal effect in systems where step‐down beds are readily available. At UCSF, nearly every patient transferred out of the ICU is triaged to a step‐down unit, where telemetry and pulse oximetry are continuously monitored. Despite this, however, our institution's 2 step‐down units generate more calls to our RRT than any other units in the hospital.

We were surprised to see that proactive rounding failed to shorten ICU LOS, hypothesizing that clinicians would be more comfortable discharging patients from the ICU knowing that the RRT would be closely monitoring them afterwards. Although we have no data to support this hypothesis, increased use of the RRT may have also increased step‐down bed use, as patients on the general medicalsurgical floors were transferred to a higher level of care upon recommendation of the RRT, thereby delaying transfers out of the ICU. Moreover, the opening of an additional 16‐bed ICU in October 2008 might have encouraged clinicians to transfer patients back to the ICU simply because beds were more easily accessible than before.

Introduction of proactive rounding by the RRT was also not associated with differences in the mortality rate of patients discharged from the ICU. This finding conflicts with the results of Garcea et al,15 Ball et al,16 and Priestley et al13, all of which found that implementation of a CCOT led to small but statistically significant reductions in in‐hospital mortality. All 3 of these studies, however, examined smaller patient populations (1380, 470, and 2903 patients, respectively), and both the Priestley and Ball studies13, 16 had significantly shorter periods of data collection (24 months and 32 weeks, respectively). Our results are based on models with confidence intervals and P values that account for variability in all 3 underlying effect estimates but assume a linear extrapolation of the preintervention trend. This approach allowed us to flexibly deal with changes related to the intervention, while relying on our large sample size to define time trends not dealt with adequately (or at all) in previous research.

The lack of improvement in outcomes cannot be attributed to immaturity of the RRT or failure of the clinical staff to use the RRT adequately. A prespecified secondary data analysis midway through the postintervention time period demonstrated that the RRT failed to gain efficacy with time with respect to all 3 outcomes. The postintervention RRT was also utilized far more frequently than its predecessor (110.6 vs 2.7 calls per 1000 admissions, respectively), and this degree of RRT utilization far surpasses the dose considered to be indicative of a mature RRT system.12

Our study has several limitations. First, we relied on administrative rather than chart‐collected data to determine the reason for ICU admission, and the APR severity of illness and risk of mortality scores. It seems unlikely, however, that coding deficiencies or biases affected the preintervention and postintervention patient populations differently. Even though we adjusted for all available measures, it is possible that we were not able to account for time trends in all potential confounders. Second, we did not have detailed clinical information on reasons for ICU readmission and whether readmissions occurred before or after the RRT proactively rounded on the patient. Therefore, potential readmissions to the ICU that might have been planned or which would have happened regardless of the presence of the RRT, such as for antibiotic desensitization, could not be accounted for. Third, introduction of proactive rounding by the RRT in June 2007 was accompanied by a change in the RRT's composition, from a physician‐led model to a nurse‐led model. Therefore, inherent differences in the way that physicians and nurses might assess and triage patients could not have been adjusted for. Lastly, this was a retrospective study conducted at a single academic medical center with a specific RRT model, and our results may not be directly applicable to nonteaching settings or to different RRT models.

Our findings raise further questions about the benefits of RRTs as they assume additional roles, such as proactive rounding on patients recently discharged from the ICU. The failure of our RRT to reduce the ICU readmission rate, the ICU average LOS, and the mortality of patients discharged from the ICU raises concerns that the benefits of our RRT are not commensurate with its cost. While defining the degree of impact and underlying mechanisms are worthy of prospective study, hospitals seeking to improve their RRT models should consider how to develop systems that achieve the RRT's promise in measurable ways.

Rapid response teams (RRT) have been promoted by numerous patient safety organizations to reduce preventable in‐hospital deaths.14 Initial studies of RRTs were promising,57 but recent literature,811 including systematic reviews and meta‐analyses, has called these findings into question. Nevertheless, RRTs remain popular in academic and community hospitals worldwide, and many have expanded their roles beyond solely responding to the deteriorating patient.12

Some RRTs, for example, proactively round on seriously ill ward patients and patients recently discharged from the intensive care unit (ICU) in an effort to prevent transitions to higher levels of care. Priestley and colleagues demonstrated that institution of such a team, referred to as a critical care outreach team (CCOT), decreased in‐hospital mortality while possibly increasing hospital length of stay (LOS).13 Three additional single‐center studies from the United Kingdom, where CCOTs are common, specifically examined proactive rounding by CCOTs on the ICU readmission rate: 2 observed no improvement,14, 15 while the third, limited by a small sample size, demonstrated a modest reduction in ICU readmissions.16

We sought to determine the impact of proactive rounding by an RRT on patients discharged from intensive care on the ICU readmission rate, ICU LOS, and in‐hospital mortality of patients discharged from the ICU. We hypothesized that proactive rounding by an RRT would decrease the ICU readmission rate, ICU LOS, and the in‐hospital mortality of patients discharged from the ICU.

MATERIALS AND METHODS

RESULTS

ICU Readmission Rate

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in the ICU readmission rate (6.7% preintervention vs 7.3% postintervention, P = 0.24; Table 1). In the adjusted ITS model, the intervention had no net effect on the odds of ICU readmission (adjusted odds ratio [AOR] for net intervention effect 0.98, 95% confidence interval [CI] 0.42, 2.28), with similar secular trends both preintervention (AOR 1.00 per year, 95% CI 0.97, 1.03), and afterwards (AOR 0.99 per year, 95% CI 0.98, 01.00), and a nonsignificant increase at implementation (Table 2). Figure 1 uses solid lines to show the fitted readmission rates, a hatched line to show the projection of the preintervention secular trend into the postintervention period, and circles to represent adjusted monthly means. The lack of a net intervention effect is indicated by the convergence of the solid and hatched lines 24 months postintervention.

Figure 1

Table 2.Adjusted Impact of Proactive Rounding by an RRT on Clinical Outcomes

Outcome: Summary Effect Measure	Value (95% CI)	P Value
NOTE: ICU readmission model adjusted for attending service, age, race/ethnicity, comorbidities (chronic pulmonary disease, weight loss, anemia, neurological disorders, rheumatoid arthritis, and solid tumors without metastasis), and clustering at the attending physician level. Length of stay model adjusted for attending service, age, race/ethnicity, comorbidities (drug abuse, rheumatoid arthritis, anemia, weight loss, paralysis, pulmonary circulation disease, neurological disorders, hypothyroidism, peptic ulcer disease, and solid tumors without metastasis), and clustering at the attending physician level. Mortality model adjusted for attending service, age, race/ethnicity, comorbidities (weight loss, lymphoma, metastatic cancer, chronic pulmonary and pulmonary circulation disease, and paralysis), and clustering at the attending physician level. Abbreviations: CI, confidence interval; ICU, intensive care unit; RRT, rapid response teams.
ICU readmission rateadjusted odds ratio
Pre‐RRT trend	1.00 (0.97, 1.03)	0.98
Change at RRT implementation	1.24 (0.94, 1.63)	0.13
Post‐RRT trend	0.98 (0.97, 1.00)	0.06
Change in trend	0.98 (0.96, 1.02)	0.39
Net intervention effect	0.92 (0.40, 2.12)	0.85
ICU average length of stayadjusted ratio of means
Trend at 9 mo pre‐RRT	0.98 (0.96, 1.00)	0.05
Trend at 3 mo pre‐RRT	1.02 (0.99, 1.04)	0.19
Change in trend at 3 mo pre‐RRT	1.03 (1.00, 1.07)	0.07
Change at RRT implementation	0.92 (0.80, 1.06)	0.27
Post‐RRT trend	1.00 (0.99, 1.00)	0.35
Change in trend at RRT implementation	0.98 (0.96, 1.01)	0.14
Net intervention effect	0.60 (0.31, 1.18)	0.14
In‐hospital mortality of patients discharged from the ICUadjusted odds ratio
Pre‐RRT trend	1.02 (0.99, 1.06)	0.15
Change at RRT implementation	0.74 (0.51, 1.08)	0.12
Post‐RRT trend	1.00 (0.98, 1.01)	0.68
Change in trend	0.97 (0.94, 1.01)	0.14
Net intervention effect	0.39 (0.14, 1.10)	0.08

ICU Average LOS

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in ICU average LOS (5.1 days preintervention vs 4.9 days postintervention, P = 0.24; Table 1). Trends in ICU LOS may have changed in October 2006 (P = 0.07), decreasing in the first half of the study period (adjusted rate ratio [ARR] 0.98 per year, 95% CI 0.961.00), but did not change significantly thereafter. As with the ICU readmission rate, neither the change in estimated secular trend after implementation (ARR 0.98, 95% CI 0.961.01), nor the net effect of the intervention (ARR 0.62, 95% CI 0.321.22) was statistically significant (Table 2); these results are depicted graphically in Figure 2.

Figure 2

In‐Hospital Mortality of Patients Discharged From the ICU

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in the mortality of patients discharged from the ICU (6.0% preintervention vs 5.5% postintervention, P = 0.24; Table 1). Similarly, in the adjusted ITS model, the intervention had no statistically significant net effect on the mortality outcome (Table 2 and Figure 3).

Figure 3

DISCUSSION

In this single center study, introduction of an RRT that proactively rounded on patients discharged from the ICU did not reduce the ICU readmission rate, ICU LOS, or mortality of patients discharged from the ICU, after accounting for secular trends using robust ITS methods and adjusting for patient level factors.

Our study is consistent with 2 smaller studies that assessed the impact of proactive rounding by a CCOT on ICU readmission rate. Leary and Ridley14 found that proactively rounding by a CCOT did not reduce ICU readmissions or shorten the ICU LOS, although this study was limited by a surprisingly low ICU readmission rate and short ICU LOS prior to the intervention. Another study15 also observed no change in the ICU readmission rate following introduction of a proactively rounding CCOT but noted small reductions in both ICU and hospital mortality. The sole study showing an effect16 observed a lower ICU readmission rate and increased survival to hospital discharge (after excluding do not resuscitate [DNR] patients) with implementation of a CCOT, although some of their findings may be explained by their CCOT's use of palliative care services, a function not featured in our model.

Our study adds to the meta‐analyses and systematic reviews810 that have questioned the hypothesis that a trained and proactive team of caregivers should be able to prevent patients from returning to the ICU. Perhaps one reason why this is not true is that proactive rounding by RRTs may have minimal effect in systems where step‐down beds are readily available. At UCSF, nearly every patient transferred out of the ICU is triaged to a step‐down unit, where telemetry and pulse oximetry are continuously monitored. Despite this, however, our institution's 2 step‐down units generate more calls to our RRT than any other units in the hospital.

We were surprised to see that proactive rounding failed to shorten ICU LOS, hypothesizing that clinicians would be more comfortable discharging patients from the ICU knowing that the RRT would be closely monitoring them afterwards. Although we have no data to support this hypothesis, increased use of the RRT may have also increased step‐down bed use, as patients on the general medicalsurgical floors were transferred to a higher level of care upon recommendation of the RRT, thereby delaying transfers out of the ICU. Moreover, the opening of an additional 16‐bed ICU in October 2008 might have encouraged clinicians to transfer patients back to the ICU simply because beds were more easily accessible than before.

Introduction of proactive rounding by the RRT was also not associated with differences in the mortality rate of patients discharged from the ICU. This finding conflicts with the results of Garcea et al,15 Ball et al,16 and Priestley et al13, all of which found that implementation of a CCOT led to small but statistically significant reductions in in‐hospital mortality. All 3 of these studies, however, examined smaller patient populations (1380, 470, and 2903 patients, respectively), and both the Priestley and Ball studies13, 16 had significantly shorter periods of data collection (24 months and 32 weeks, respectively). Our results are based on models with confidence intervals and P values that account for variability in all 3 underlying effect estimates but assume a linear extrapolation of the preintervention trend. This approach allowed us to flexibly deal with changes related to the intervention, while relying on our large sample size to define time trends not dealt with adequately (or at all) in previous research.

The lack of improvement in outcomes cannot be attributed to immaturity of the RRT or failure of the clinical staff to use the RRT adequately. A prespecified secondary data analysis midway through the postintervention time period demonstrated that the RRT failed to gain efficacy with time with respect to all 3 outcomes. The postintervention RRT was also utilized far more frequently than its predecessor (110.6 vs 2.7 calls per 1000 admissions, respectively), and this degree of RRT utilization far surpasses the dose considered to be indicative of a mature RRT system.12

Our study has several limitations. First, we relied on administrative rather than chart‐collected data to determine the reason for ICU admission, and the APR severity of illness and risk of mortality scores. It seems unlikely, however, that coding deficiencies or biases affected the preintervention and postintervention patient populations differently. Even though we adjusted for all available measures, it is possible that we were not able to account for time trends in all potential confounders. Second, we did not have detailed clinical information on reasons for ICU readmission and whether readmissions occurred before or after the RRT proactively rounded on the patient. Therefore, potential readmissions to the ICU that might have been planned or which would have happened regardless of the presence of the RRT, such as for antibiotic desensitization, could not be accounted for. Third, introduction of proactive rounding by the RRT in June 2007 was accompanied by a change in the RRT's composition, from a physician‐led model to a nurse‐led model. Therefore, inherent differences in the way that physicians and nurses might assess and triage patients could not have been adjusted for. Lastly, this was a retrospective study conducted at a single academic medical center with a specific RRT model, and our results may not be directly applicable to nonteaching settings or to different RRT models.

Our findings raise further questions about the benefits of RRTs as they assume additional roles, such as proactive rounding on patients recently discharged from the ICU. The failure of our RRT to reduce the ICU readmission rate, the ICU average LOS, and the mortality of patients discharged from the ICU raises concerns that the benefits of our RRT are not commensurate with its cost. While defining the degree of impact and underlying mechanisms are worthy of prospective study, hospitals seeking to improve their RRT models should consider how to develop systems that achieve the RRT's promise in measurable ways.

Rapid response teams (RRT) have been promoted by numerous patient safety organizations to reduce preventable in‐hospital deaths.14 Initial studies of RRTs were promising,57 but recent literature,811 including systematic reviews and meta‐analyses, has called these findings into question. Nevertheless, RRTs remain popular in academic and community hospitals worldwide, and many have expanded their roles beyond solely responding to the deteriorating patient.12

Some RRTs, for example, proactively round on seriously ill ward patients and patients recently discharged from the intensive care unit (ICU) in an effort to prevent transitions to higher levels of care. Priestley and colleagues demonstrated that institution of such a team, referred to as a critical care outreach team (CCOT), decreased in‐hospital mortality while possibly increasing hospital length of stay (LOS).13 Three additional single‐center studies from the United Kingdom, where CCOTs are common, specifically examined proactive rounding by CCOTs on the ICU readmission rate: 2 observed no improvement,14, 15 while the third, limited by a small sample size, demonstrated a modest reduction in ICU readmissions.16

We sought to determine the impact of proactive rounding by an RRT on patients discharged from intensive care on the ICU readmission rate, ICU LOS, and in‐hospital mortality of patients discharged from the ICU. We hypothesized that proactive rounding by an RRT would decrease the ICU readmission rate, ICU LOS, and the in‐hospital mortality of patients discharged from the ICU.

MATERIALS AND METHODS

RESULTS

ICU Readmission Rate

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in the ICU readmission rate (6.7% preintervention vs 7.3% postintervention, P = 0.24; Table 1). In the adjusted ITS model, the intervention had no net effect on the odds of ICU readmission (adjusted odds ratio [AOR] for net intervention effect 0.98, 95% confidence interval [CI] 0.42, 2.28), with similar secular trends both preintervention (AOR 1.00 per year, 95% CI 0.97, 1.03), and afterwards (AOR 0.99 per year, 95% CI 0.98, 01.00), and a nonsignificant increase at implementation (Table 2). Figure 1 uses solid lines to show the fitted readmission rates, a hatched line to show the projection of the preintervention secular trend into the postintervention period, and circles to represent adjusted monthly means. The lack of a net intervention effect is indicated by the convergence of the solid and hatched lines 24 months postintervention.

Figure 1

Table 2.Adjusted Impact of Proactive Rounding by an RRT on Clinical Outcomes

Outcome: Summary Effect Measure	Value (95% CI)	P Value
NOTE: ICU readmission model adjusted for attending service, age, race/ethnicity, comorbidities (chronic pulmonary disease, weight loss, anemia, neurological disorders, rheumatoid arthritis, and solid tumors without metastasis), and clustering at the attending physician level. Length of stay model adjusted for attending service, age, race/ethnicity, comorbidities (drug abuse, rheumatoid arthritis, anemia, weight loss, paralysis, pulmonary circulation disease, neurological disorders, hypothyroidism, peptic ulcer disease, and solid tumors without metastasis), and clustering at the attending physician level. Mortality model adjusted for attending service, age, race/ethnicity, comorbidities (weight loss, lymphoma, metastatic cancer, chronic pulmonary and pulmonary circulation disease, and paralysis), and clustering at the attending physician level. Abbreviations: CI, confidence interval; ICU, intensive care unit; RRT, rapid response teams.
ICU readmission rateadjusted odds ratio
Pre‐RRT trend	1.00 (0.97, 1.03)	0.98
Change at RRT implementation	1.24 (0.94, 1.63)	0.13
Post‐RRT trend	0.98 (0.97, 1.00)	0.06
Change in trend	0.98 (0.96, 1.02)	0.39
Net intervention effect	0.92 (0.40, 2.12)	0.85
ICU average length of stayadjusted ratio of means
Trend at 9 mo pre‐RRT	0.98 (0.96, 1.00)	0.05
Trend at 3 mo pre‐RRT	1.02 (0.99, 1.04)	0.19
Change in trend at 3 mo pre‐RRT	1.03 (1.00, 1.07)	0.07
Change at RRT implementation	0.92 (0.80, 1.06)	0.27
Post‐RRT trend	1.00 (0.99, 1.00)	0.35
Change in trend at RRT implementation	0.98 (0.96, 1.01)	0.14
Net intervention effect	0.60 (0.31, 1.18)	0.14
In‐hospital mortality of patients discharged from the ICUadjusted odds ratio
Pre‐RRT trend	1.02 (0.99, 1.06)	0.15
Change at RRT implementation	0.74 (0.51, 1.08)	0.12
Post‐RRT trend	1.00 (0.98, 1.01)	0.68
Change in trend	0.97 (0.94, 1.01)	0.14
Net intervention effect	0.39 (0.14, 1.10)	0.08

ICU Average LOS

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in ICU average LOS (5.1 days preintervention vs 4.9 days postintervention, P = 0.24; Table 1). Trends in ICU LOS may have changed in October 2006 (P = 0.07), decreasing in the first half of the study period (adjusted rate ratio [ARR] 0.98 per year, 95% CI 0.961.00), but did not change significantly thereafter. As with the ICU readmission rate, neither the change in estimated secular trend after implementation (ARR 0.98, 95% CI 0.961.01), nor the net effect of the intervention (ARR 0.62, 95% CI 0.321.22) was statistically significant (Table 2); these results are depicted graphically in Figure 2.

Figure 2

In‐Hospital Mortality of Patients Discharged From the ICU

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in the mortality of patients discharged from the ICU (6.0% preintervention vs 5.5% postintervention, P = 0.24; Table 1). Similarly, in the adjusted ITS model, the intervention had no statistically significant net effect on the mortality outcome (Table 2 and Figure 3).

Figure 3

DISCUSSION

In this single center study, introduction of an RRT that proactively rounded on patients discharged from the ICU did not reduce the ICU readmission rate, ICU LOS, or mortality of patients discharged from the ICU, after accounting for secular trends using robust ITS methods and adjusting for patient level factors.

Our study is consistent with 2 smaller studies that assessed the impact of proactive rounding by a CCOT on ICU readmission rate. Leary and Ridley14 found that proactively rounding by a CCOT did not reduce ICU readmissions or shorten the ICU LOS, although this study was limited by a surprisingly low ICU readmission rate and short ICU LOS prior to the intervention. Another study15 also observed no change in the ICU readmission rate following introduction of a proactively rounding CCOT but noted small reductions in both ICU and hospital mortality. The sole study showing an effect16 observed a lower ICU readmission rate and increased survival to hospital discharge (after excluding do not resuscitate [DNR] patients) with implementation of a CCOT, although some of their findings may be explained by their CCOT's use of palliative care services, a function not featured in our model.

Our study adds to the meta‐analyses and systematic reviews810 that have questioned the hypothesis that a trained and proactive team of caregivers should be able to prevent patients from returning to the ICU. Perhaps one reason why this is not true is that proactive rounding by RRTs may have minimal effect in systems where step‐down beds are readily available. At UCSF, nearly every patient transferred out of the ICU is triaged to a step‐down unit, where telemetry and pulse oximetry are continuously monitored. Despite this, however, our institution's 2 step‐down units generate more calls to our RRT than any other units in the hospital.

We were surprised to see that proactive rounding failed to shorten ICU LOS, hypothesizing that clinicians would be more comfortable discharging patients from the ICU knowing that the RRT would be closely monitoring them afterwards. Although we have no data to support this hypothesis, increased use of the RRT may have also increased step‐down bed use, as patients on the general medicalsurgical floors were transferred to a higher level of care upon recommendation of the RRT, thereby delaying transfers out of the ICU. Moreover, the opening of an additional 16‐bed ICU in October 2008 might have encouraged clinicians to transfer patients back to the ICU simply because beds were more easily accessible than before.

Introduction of proactive rounding by the RRT was also not associated with differences in the mortality rate of patients discharged from the ICU. This finding conflicts with the results of Garcea et al,15 Ball et al,16 and Priestley et al13, all of which found that implementation of a CCOT led to small but statistically significant reductions in in‐hospital mortality. All 3 of these studies, however, examined smaller patient populations (1380, 470, and 2903 patients, respectively), and both the Priestley and Ball studies13, 16 had significantly shorter periods of data collection (24 months and 32 weeks, respectively). Our results are based on models with confidence intervals and P values that account for variability in all 3 underlying effect estimates but assume a linear extrapolation of the preintervention trend. This approach allowed us to flexibly deal with changes related to the intervention, while relying on our large sample size to define time trends not dealt with adequately (or at all) in previous research.

The lack of improvement in outcomes cannot be attributed to immaturity of the RRT or failure of the clinical staff to use the RRT adequately. A prespecified secondary data analysis midway through the postintervention time period demonstrated that the RRT failed to gain efficacy with time with respect to all 3 outcomes. The postintervention RRT was also utilized far more frequently than its predecessor (110.6 vs 2.7 calls per 1000 admissions, respectively), and this degree of RRT utilization far surpasses the dose considered to be indicative of a mature RRT system.12

Our study has several limitations. First, we relied on administrative rather than chart‐collected data to determine the reason for ICU admission, and the APR severity of illness and risk of mortality scores. It seems unlikely, however, that coding deficiencies or biases affected the preintervention and postintervention patient populations differently. Even though we adjusted for all available measures, it is possible that we were not able to account for time trends in all potential confounders. Second, we did not have detailed clinical information on reasons for ICU readmission and whether readmissions occurred before or after the RRT proactively rounded on the patient. Therefore, potential readmissions to the ICU that might have been planned or which would have happened regardless of the presence of the RRT, such as for antibiotic desensitization, could not be accounted for. Third, introduction of proactive rounding by the RRT in June 2007 was accompanied by a change in the RRT's composition, from a physician‐led model to a nurse‐led model. Therefore, inherent differences in the way that physicians and nurses might assess and triage patients could not have been adjusted for. Lastly, this was a retrospective study conducted at a single academic medical center with a specific RRT model, and our results may not be directly applicable to nonteaching settings or to different RRT models.

Our findings raise further questions about the benefits of RRTs as they assume additional roles, such as proactive rounding on patients recently discharged from the ICU. The failure of our RRT to reduce the ICU readmission rate, the ICU average LOS, and the mortality of patients discharged from the ICU raises concerns that the benefits of our RRT are not commensurate with its cost. While defining the degree of impact and underlying mechanisms are worthy of prospective study, hospitals seeking to improve their RRT models should consider how to develop systems that achieve the RRT's promise in measurable ways.

Emergency Department (ED) patients who are hospitalized and require unplanned transfer to the intensive care unit (ICU) within 24 hours of arrival on the ward have higher mortality than direct ICU admissions.1, 2 Previous research found that 5% of ED admissions experienced unplanned ICU transfer during their hospitalization, yet these patients account for 25% of in‐hospital deaths and have a longer length of stay than direct ICU admissions.1, 3 For these reasons, inpatient rapid‐response teams and early warning systems have been studied to reduce the mortality of patients who rapidly deteriorate on the hospital ward.410 However, there is little conclusive evidence that these interventions decrease mortality.710 It is possible that with better recognition and intervention in the ED, a portion of these unplanned ICU transfers and their subsequent adverse outcomes could be prevented.11

Previous research on risk factors for unplanned ICU transfers among ED admissions is limited. While 2 previous studies from non‐US hospitals used administrative data to identify some general populations at risk for unplanned ICU transfer,12, 13 these studies did not differentiate between transfers shortly after admission and those that occurred during a prolonged hospital staya critical distinction since the outcomes between these groups differs substantially.1 Another limitation of these studies is the absence of physiologic measures at ED presentation, which have been shown to be highly predictive of mortality.14

In this study, we describe risk factors for unplanned transfer to the ICU within 24 hours of arrival on the ward, among a large cohort of ED hospitalizations across 13 community hospitals. Focusing on admitting diagnoses most at risk, our goal was to inform efforts to improve the triage of ED admissions and determine which patients may benefit from additional interventions, such as improved resuscitation, closer monitoring, or risk stratification tools. We also hypothesized that higher volume hospitals would have lower rates of unplanned ICU transfers, as these hospitals are more likely have more patient care resources on the hospital ward and a higher threshold to transfer to the ICU.

METHODS

RESULTS

Of 178,315 ED non‐ICU hospitalizations meeting inclusion criteria, 4,252 (2.4%) were admitted to the ward and were transferred to the ICU within 24 hours of leaving the ED. There were 122,251 unique patients in our study population. Table 1 compares the characteristics of ED hospitalizations in which an unplanned transfer occurred to those that did not experience an unplanned transfer. Unplanned transfers were more likely to have a higher comorbidity burden, more deranged physiology, and more likely to arrive on the floor during the overnight shift.

Unplanned ICU transfers were more frequent in lower volume hospitals (Table 1). Figure 1 displays the inverse relationship between hospital annual ED inpatient admission volume and unplanned ICU transfers rates. The lowest volume hospital had a crude rate twice as high as the 2 highest volume hospitals (39 vs 20, per 1,000 admissions).

Figure 1

Pneumonia/respiratory infection was the most frequent admitting condition associated with unplanned transfer (16.3%) (Table 1). There was also wide variation in crude rates for unplanned ICU transfer by admitting condition (Figure 2). Patients admitted with sepsis had the highest rate (59 per 1,000 admissions), while patients admitted with renal conditions other than acute renal failure had the lowest rates (14.3 per 1,000 admissions).

Figure 2

We confirmed that almost all diagnoses found to account for a disproportionately high share of unplanned ICU transfers in Table 1 were indeed independently associated with this phenomenon after adjustment for patient and hospital differences (Figure 2). Pneumonia remained the most frequent condition associated with unplanned ICU transfer (odds ratio [OR] 1.50; 95% CI 1.201.86). Although less frequent, sepsis had the strongest association of any condition with unplanned transfer (OR 2.51; 95% CI 1.903.31). However, metabolic, endocrine, and electrolyte conditions were no longer associated with unplanned transfer after adjustment, while arrhythmias and pulmonary embolism were. Other conditions confirmed to be associated with increased risk of unplanned transfer included: myocardial infarction (MI), chronic obstructive pulmonary disease (COPD), stroke, diabetic emergencies, catastrophic conditions (includes aortic catastrophes, all forms of shock except septic shock, and intracranial hemorrhage), and acute renal failure. After taking into account the frequency of admitting diagnoses, respiratory conditions (COPD, pneumonia/acute respiratory infection) comprised nearly half (47%) of all conditions associated with increased risk of unplanned ICU transfer.

Other factors confirmed to be independently associated with unplanned ICU transfer included: male sex (OR 1.20; 95% CI 1.131.28), high comorbidity burden as measured by COPS >145 (OR 1.13; 95% CI 1.031.24), increasingly abnormal physiology compared to a LAPS <7, and arrival on ward during the overnight shift (OR 1.10; 95% CI 1.011.21). After adjustment, we did find that admission to the TCU rather than a medicalsurgical unit was associated with decreased risk of unplanned ICU transfer (OR 0.83; 95% CI 0.770.90). Age 85 was associated with decreased risk of unplanned ICU transfer relative to the youngest age group of 1834‐year‐old patients (OR 0.64; 95% CI 0.530.77).

ED admissions to higher volume hospitals were 6% less likely to experience an unplanned transfer for each additional 1,000 annual ED hospitalizations over a lower volume hospital (OR 0.94; 95% CI 0.910.98). In other words, a patient admitted to a hospital with 8,000 annual ED hospitalizations had 30% decreased odds of unplanned ICU transfer compared to a hospital with only 3,000 annual ED hospitalizations.

DISCUSSION

Patients admitted with respiratory conditions accounted for half of all admitting diagnoses associated with increased risk of unplanned transfer to the ICU within 24 hours of arrival to the ward. We found that 1 in 30 ED ward admissions for pneumonia, and 1 in 33 for COPD, were transferred to the ICU within 24 hours. These findings indicate that there is some room for improvement in early care of respiratory conditions, given the average unplanned transfer rate of 1 in 42, and previous research showing that patients with pneumonia and patients with COPD, who experience unplanned ICU transfer, have substantially worse mortality than those directly admitted to the ICU.1

Although less frequent than hospitalizations for respiratory conditions, patients admitted with sepsis were at the highest risk of unplanned ICU transfer (1 in 17 ED non‐ICU hospitalizations). We also found that MI and stroke ward admissions had a higher risk of unplanned ICU transfer. However, we previously found that unplanned ICU transfers for sepsis, MI, and stroke did not have worse mortality than direct ICU admits for these conditions.1 Therefore, quality improvement efforts to reduce excess mortality related to early decompensation in the hospital and unplanned ICU transfer would be most effective if targeted towards respiratory conditions such as pneumonia and COPD.

This is the only in‐depth study, to our knowledge, to explore the association between a set of mutually exclusive diagnostic categories and risk of unplanned ICU transfer within 24 hours, and it is the first study to identify risk factors for unplanned ICU transfer in a multi‐hospital cohort adjusted for patient‐ and hospital‐level characteristics. We also identified a novel hospital volumeoutcome relationship: Unplanned ICU transfers are up to twice as likely to occur in the smallest volume hospitals compared with highest volume hospitals. Hospital volume has long been proposed as a proxy for hospital resources; there are several studies showing a relationship between low‐volume hospitals and worse outcomes for a number of conditions.19, 20 Possible mechanisms may include decreased ICU capacity, decreased on‐call intensivists in the hospital after hours, and less experience with certain critical care conditions seen more frequently in high‐volume hospitals.21

Patients at risk of unplanned ICU transfer were also more likely to have physiologic derangement identified on laboratory testing, high comorbidity burden, and arrive on the ward between 11 PM and 7 AM. Given the strong correlation between comorbidity burden and physiologic derangement and mortality,14 it is not surprising that the COPS and LAPS were independent predictors of unplanned transfer. It is unclear, however, why arriving on the ward on the overnight shift is associated with higher risk. One possibility is that patients who arrive on the wards during 11 PM to 7 AM are also likely to have been in the ED during evening peak hours most associated with ED crowding.22 High levels of ED crowding have been associated with delays in care, worse quality care, lapses in patient safety, and even increased in‐hospital mortality.22, 23 Other possible reasons include decreased in‐hospital staffing and longer delays in critical diagnostic tests and interventions.2428

Admission to TCUs was associated with decreased risk of unplanned ICU transfer in the first 24 hours of hospitalization. This may be due to the continuous monitoring, decreased nursing‐to‐patient ratios, or the availability to provide some critical care interventions. In our study, age 85 was associated with lower likelihood of unplanned transfer. Unfortunately, we did not have access to data on advanced directives or patient preferences. Data on advanced directives would help to distinguish whether this phenomenon was related to end‐of‐life care goals versus other explanations.

Our study confirms some risk factors identified in previous studies. These include specific diagnoses such as pneumonia and COPD,12, 13, 29 heavy comorbidity burden,12, 13, 29 abnormal labs,29 and male sex.13 Pneumonia has consistently been shown to be a risk factor for unplanned ICU transfer. This may stem from the dynamic nature of this condition and its ability to rapidly progress, and the fact that some ICUs may not accept pneumonia patients unless they demonstrate a need for mechanical ventilation.30 Recently, a prediction rule has been developed to determine which patients with pneumonia are likely to have an unplanned ICU transfer.30 It is possible that with validation and application of this rule, unplanned transfer rates for pneumonia could be reduced. It is unclear whether males have unmeasured factors associated with increased risk of unplanned transfer or whether a true gender disparity exists.

Our findings should be interpreted within the context of this study's limitations. First, this study was not designed to distinguish the underlying cause of the unplanned transfer such as under‐recognition of illness severity in the ED, evolving clinical disease after leaving the ED, or delays in critical interventions on the ward. These are a focus of our ongoing research efforts. Second, while previous studies have demonstrated that our automated risk adjustment variables can accurately predict in‐hospital mortality (0.88 area under curve in external populations),17 additional data on vital signs and mental status could further improve risk adjustment. However, using automated data allowed us to study risk factors for unplanned transfer in a multi‐hospital cohort with a much larger population than has been previously studied. Serial data on vital signs and mental status both in the ED and during hospitalization could also be helpful in determining which unplanned transfers could be prevented with earlier recognition and intervention. Finally, all patient care occurred within an integrated healthcare delivery system. Thus, differences in case‐mix, hospital resources, ICU structure, and geographic location should be considered when applying our results to other healthcare systems.

This study raises several new areas for future research. With access to richer data becoming available in electronic medical records, prediction rules should be developed to enable better triage to appropriate levels of care for ED admissions. Future research should also analyze the comparative effectiveness of intermediate monitored units versus non‐monitored wards for preventing clinical deterioration by admitting diagnosis. Diagnoses that have been shown to have an increased risk of death after unplanned ICU transfer, such as pneumonia/respiratory infection and COPD,1 should be prioritized in this research. Better understanding is needed on the diagnosis‐specific differences and the differences in ED triage process and ICU structure that may explain why high‐volume hospitals have significantly lower rates of early unplanned ICU transfers compared with low‐volume hospitals. In particular, determining the effect of TCU and ICU capacities and census at the time of admission, and comparing patient risk characteristics across hospital‐volume strata would be very useful. Finally, more work is needed to determine whether the higher rate of unplanned transfers during overnight nursing shifts is related to decreased resource availability, preceding ED crowding, or other organizational causes.

In conclusion, patients admitted with respiratory conditions, sepsis, MI, high comorbidity, and abnormal labs are at modestly increased risk of unplanned ICU transfer within 24 hours of admission from the ED. Patients admitted with respiratory conditions (pneumonia/respiratory infections and COPD) accounted for half of the admitting diagnoses that are at increased risk for unplanned ICU transfer. These patients may benefit from better inpatient triage from the ED, earlier intervention, or closer monitoring. More research is needed to determine the specific aspects of care associated with admission to intermediate care units and high‐volume hospitals that reduce the risk of unplanned ICU transfer.

Emergency Department (ED) patients who are hospitalized and require unplanned transfer to the intensive care unit (ICU) within 24 hours of arrival on the ward have higher mortality than direct ICU admissions.1, 2 Previous research found that 5% of ED admissions experienced unplanned ICU transfer during their hospitalization, yet these patients account for 25% of in‐hospital deaths and have a longer length of stay than direct ICU admissions.1, 3 For these reasons, inpatient rapid‐response teams and early warning systems have been studied to reduce the mortality of patients who rapidly deteriorate on the hospital ward.410 However, there is little conclusive evidence that these interventions decrease mortality.710 It is possible that with better recognition and intervention in the ED, a portion of these unplanned ICU transfers and their subsequent adverse outcomes could be prevented.11

Previous research on risk factors for unplanned ICU transfers among ED admissions is limited. While 2 previous studies from non‐US hospitals used administrative data to identify some general populations at risk for unplanned ICU transfer,12, 13 these studies did not differentiate between transfers shortly after admission and those that occurred during a prolonged hospital staya critical distinction since the outcomes between these groups differs substantially.1 Another limitation of these studies is the absence of physiologic measures at ED presentation, which have been shown to be highly predictive of mortality.14

In this study, we describe risk factors for unplanned transfer to the ICU within 24 hours of arrival on the ward, among a large cohort of ED hospitalizations across 13 community hospitals. Focusing on admitting diagnoses most at risk, our goal was to inform efforts to improve the triage of ED admissions and determine which patients may benefit from additional interventions, such as improved resuscitation, closer monitoring, or risk stratification tools. We also hypothesized that higher volume hospitals would have lower rates of unplanned ICU transfers, as these hospitals are more likely have more patient care resources on the hospital ward and a higher threshold to transfer to the ICU.

METHODS

RESULTS

Of 178,315 ED non‐ICU hospitalizations meeting inclusion criteria, 4,252 (2.4%) were admitted to the ward and were transferred to the ICU within 24 hours of leaving the ED. There were 122,251 unique patients in our study population. Table 1 compares the characteristics of ED hospitalizations in which an unplanned transfer occurred to those that did not experience an unplanned transfer. Unplanned transfers were more likely to have a higher comorbidity burden, more deranged physiology, and more likely to arrive on the floor during the overnight shift.

Unplanned ICU transfers were more frequent in lower volume hospitals (Table 1). Figure 1 displays the inverse relationship between hospital annual ED inpatient admission volume and unplanned ICU transfers rates. The lowest volume hospital had a crude rate twice as high as the 2 highest volume hospitals (39 vs 20, per 1,000 admissions).

Figure 1

Pneumonia/respiratory infection was the most frequent admitting condition associated with unplanned transfer (16.3%) (Table 1). There was also wide variation in crude rates for unplanned ICU transfer by admitting condition (Figure 2). Patients admitted with sepsis had the highest rate (59 per 1,000 admissions), while patients admitted with renal conditions other than acute renal failure had the lowest rates (14.3 per 1,000 admissions).

Figure 2

We confirmed that almost all diagnoses found to account for a disproportionately high share of unplanned ICU transfers in Table 1 were indeed independently associated with this phenomenon after adjustment for patient and hospital differences (Figure 2). Pneumonia remained the most frequent condition associated with unplanned ICU transfer (odds ratio [OR] 1.50; 95% CI 1.201.86). Although less frequent, sepsis had the strongest association of any condition with unplanned transfer (OR 2.51; 95% CI 1.903.31). However, metabolic, endocrine, and electrolyte conditions were no longer associated with unplanned transfer after adjustment, while arrhythmias and pulmonary embolism were. Other conditions confirmed to be associated with increased risk of unplanned transfer included: myocardial infarction (MI), chronic obstructive pulmonary disease (COPD), stroke, diabetic emergencies, catastrophic conditions (includes aortic catastrophes, all forms of shock except septic shock, and intracranial hemorrhage), and acute renal failure. After taking into account the frequency of admitting diagnoses, respiratory conditions (COPD, pneumonia/acute respiratory infection) comprised nearly half (47%) of all conditions associated with increased risk of unplanned ICU transfer.

Other factors confirmed to be independently associated with unplanned ICU transfer included: male sex (OR 1.20; 95% CI 1.131.28), high comorbidity burden as measured by COPS >145 (OR 1.13; 95% CI 1.031.24), increasingly abnormal physiology compared to a LAPS <7, and arrival on ward during the overnight shift (OR 1.10; 95% CI 1.011.21). After adjustment, we did find that admission to the TCU rather than a medicalsurgical unit was associated with decreased risk of unplanned ICU transfer (OR 0.83; 95% CI 0.770.90). Age 85 was associated with decreased risk of unplanned ICU transfer relative to the youngest age group of 1834‐year‐old patients (OR 0.64; 95% CI 0.530.77).

ED admissions to higher volume hospitals were 6% less likely to experience an unplanned transfer for each additional 1,000 annual ED hospitalizations over a lower volume hospital (OR 0.94; 95% CI 0.910.98). In other words, a patient admitted to a hospital with 8,000 annual ED hospitalizations had 30% decreased odds of unplanned ICU transfer compared to a hospital with only 3,000 annual ED hospitalizations.

DISCUSSION

Patients admitted with respiratory conditions accounted for half of all admitting diagnoses associated with increased risk of unplanned transfer to the ICU within 24 hours of arrival to the ward. We found that 1 in 30 ED ward admissions for pneumonia, and 1 in 33 for COPD, were transferred to the ICU within 24 hours. These findings indicate that there is some room for improvement in early care of respiratory conditions, given the average unplanned transfer rate of 1 in 42, and previous research showing that patients with pneumonia and patients with COPD, who experience unplanned ICU transfer, have substantially worse mortality than those directly admitted to the ICU.1

Although less frequent than hospitalizations for respiratory conditions, patients admitted with sepsis were at the highest risk of unplanned ICU transfer (1 in 17 ED non‐ICU hospitalizations). We also found that MI and stroke ward admissions had a higher risk of unplanned ICU transfer. However, we previously found that unplanned ICU transfers for sepsis, MI, and stroke did not have worse mortality than direct ICU admits for these conditions.1 Therefore, quality improvement efforts to reduce excess mortality related to early decompensation in the hospital and unplanned ICU transfer would be most effective if targeted towards respiratory conditions such as pneumonia and COPD.

This is the only in‐depth study, to our knowledge, to explore the association between a set of mutually exclusive diagnostic categories and risk of unplanned ICU transfer within 24 hours, and it is the first study to identify risk factors for unplanned ICU transfer in a multi‐hospital cohort adjusted for patient‐ and hospital‐level characteristics. We also identified a novel hospital volumeoutcome relationship: Unplanned ICU transfers are up to twice as likely to occur in the smallest volume hospitals compared with highest volume hospitals. Hospital volume has long been proposed as a proxy for hospital resources; there are several studies showing a relationship between low‐volume hospitals and worse outcomes for a number of conditions.19, 20 Possible mechanisms may include decreased ICU capacity, decreased on‐call intensivists in the hospital after hours, and less experience with certain critical care conditions seen more frequently in high‐volume hospitals.21

Patients at risk of unplanned ICU transfer were also more likely to have physiologic derangement identified on laboratory testing, high comorbidity burden, and arrive on the ward between 11 PM and 7 AM. Given the strong correlation between comorbidity burden and physiologic derangement and mortality,14 it is not surprising that the COPS and LAPS were independent predictors of unplanned transfer. It is unclear, however, why arriving on the ward on the overnight shift is associated with higher risk. One possibility is that patients who arrive on the wards during 11 PM to 7 AM are also likely to have been in the ED during evening peak hours most associated with ED crowding.22 High levels of ED crowding have been associated with delays in care, worse quality care, lapses in patient safety, and even increased in‐hospital mortality.22, 23 Other possible reasons include decreased in‐hospital staffing and longer delays in critical diagnostic tests and interventions.2428

Admission to TCUs was associated with decreased risk of unplanned ICU transfer in the first 24 hours of hospitalization. This may be due to the continuous monitoring, decreased nursing‐to‐patient ratios, or the availability to provide some critical care interventions. In our study, age 85 was associated with lower likelihood of unplanned transfer. Unfortunately, we did not have access to data on advanced directives or patient preferences. Data on advanced directives would help to distinguish whether this phenomenon was related to end‐of‐life care goals versus other explanations.

Our study confirms some risk factors identified in previous studies. These include specific diagnoses such as pneumonia and COPD,12, 13, 29 heavy comorbidity burden,12, 13, 29 abnormal labs,29 and male sex.13 Pneumonia has consistently been shown to be a risk factor for unplanned ICU transfer. This may stem from the dynamic nature of this condition and its ability to rapidly progress, and the fact that some ICUs may not accept pneumonia patients unless they demonstrate a need for mechanical ventilation.30 Recently, a prediction rule has been developed to determine which patients with pneumonia are likely to have an unplanned ICU transfer.30 It is possible that with validation and application of this rule, unplanned transfer rates for pneumonia could be reduced. It is unclear whether males have unmeasured factors associated with increased risk of unplanned transfer or whether a true gender disparity exists.

Our findings should be interpreted within the context of this study's limitations. First, this study was not designed to distinguish the underlying cause of the unplanned transfer such as under‐recognition of illness severity in the ED, evolving clinical disease after leaving the ED, or delays in critical interventions on the ward. These are a focus of our ongoing research efforts. Second, while previous studies have demonstrated that our automated risk adjustment variables can accurately predict in‐hospital mortality (0.88 area under curve in external populations),17 additional data on vital signs and mental status could further improve risk adjustment. However, using automated data allowed us to study risk factors for unplanned transfer in a multi‐hospital cohort with a much larger population than has been previously studied. Serial data on vital signs and mental status both in the ED and during hospitalization could also be helpful in determining which unplanned transfers could be prevented with earlier recognition and intervention. Finally, all patient care occurred within an integrated healthcare delivery system. Thus, differences in case‐mix, hospital resources, ICU structure, and geographic location should be considered when applying our results to other healthcare systems.

This study raises several new areas for future research. With access to richer data becoming available in electronic medical records, prediction rules should be developed to enable better triage to appropriate levels of care for ED admissions. Future research should also analyze the comparative effectiveness of intermediate monitored units versus non‐monitored wards for preventing clinical deterioration by admitting diagnosis. Diagnoses that have been shown to have an increased risk of death after unplanned ICU transfer, such as pneumonia/respiratory infection and COPD,1 should be prioritized in this research. Better understanding is needed on the diagnosis‐specific differences and the differences in ED triage process and ICU structure that may explain why high‐volume hospitals have significantly lower rates of early unplanned ICU transfers compared with low‐volume hospitals. In particular, determining the effect of TCU and ICU capacities and census at the time of admission, and comparing patient risk characteristics across hospital‐volume strata would be very useful. Finally, more work is needed to determine whether the higher rate of unplanned transfers during overnight nursing shifts is related to decreased resource availability, preceding ED crowding, or other organizational causes.

In conclusion, patients admitted with respiratory conditions, sepsis, MI, high comorbidity, and abnormal labs are at modestly increased risk of unplanned ICU transfer within 24 hours of admission from the ED. Patients admitted with respiratory conditions (pneumonia/respiratory infections and COPD) accounted for half of the admitting diagnoses that are at increased risk for unplanned ICU transfer. These patients may benefit from better inpatient triage from the ED, earlier intervention, or closer monitoring. More research is needed to determine the specific aspects of care associated with admission to intermediate care units and high‐volume hospitals that reduce the risk of unplanned ICU transfer.

Emergency Department (ED) patients who are hospitalized and require unplanned transfer to the intensive care unit (ICU) within 24 hours of arrival on the ward have higher mortality than direct ICU admissions.1, 2 Previous research found that 5% of ED admissions experienced unplanned ICU transfer during their hospitalization, yet these patients account for 25% of in‐hospital deaths and have a longer length of stay than direct ICU admissions.1, 3 For these reasons, inpatient rapid‐response teams and early warning systems have been studied to reduce the mortality of patients who rapidly deteriorate on the hospital ward.410 However, there is little conclusive evidence that these interventions decrease mortality.710 It is possible that with better recognition and intervention in the ED, a portion of these unplanned ICU transfers and their subsequent adverse outcomes could be prevented.11

Previous research on risk factors for unplanned ICU transfers among ED admissions is limited. While 2 previous studies from non‐US hospitals used administrative data to identify some general populations at risk for unplanned ICU transfer,12, 13 these studies did not differentiate between transfers shortly after admission and those that occurred during a prolonged hospital staya critical distinction since the outcomes between these groups differs substantially.1 Another limitation of these studies is the absence of physiologic measures at ED presentation, which have been shown to be highly predictive of mortality.14

In this study, we describe risk factors for unplanned transfer to the ICU within 24 hours of arrival on the ward, among a large cohort of ED hospitalizations across 13 community hospitals. Focusing on admitting diagnoses most at risk, our goal was to inform efforts to improve the triage of ED admissions and determine which patients may benefit from additional interventions, such as improved resuscitation, closer monitoring, or risk stratification tools. We also hypothesized that higher volume hospitals would have lower rates of unplanned ICU transfers, as these hospitals are more likely have more patient care resources on the hospital ward and a higher threshold to transfer to the ICU.

METHODS

RESULTS

Of 178,315 ED non‐ICU hospitalizations meeting inclusion criteria, 4,252 (2.4%) were admitted to the ward and were transferred to the ICU within 24 hours of leaving the ED. There were 122,251 unique patients in our study population. Table 1 compares the characteristics of ED hospitalizations in which an unplanned transfer occurred to those that did not experience an unplanned transfer. Unplanned transfers were more likely to have a higher comorbidity burden, more deranged physiology, and more likely to arrive on the floor during the overnight shift.

Unplanned ICU transfers were more frequent in lower volume hospitals (Table 1). Figure 1 displays the inverse relationship between hospital annual ED inpatient admission volume and unplanned ICU transfers rates. The lowest volume hospital had a crude rate twice as high as the 2 highest volume hospitals (39 vs 20, per 1,000 admissions).

Figure 1

Pneumonia/respiratory infection was the most frequent admitting condition associated with unplanned transfer (16.3%) (Table 1). There was also wide variation in crude rates for unplanned ICU transfer by admitting condition (Figure 2). Patients admitted with sepsis had the highest rate (59 per 1,000 admissions), while patients admitted with renal conditions other than acute renal failure had the lowest rates (14.3 per 1,000 admissions).

Figure 2

We confirmed that almost all diagnoses found to account for a disproportionately high share of unplanned ICU transfers in Table 1 were indeed independently associated with this phenomenon after adjustment for patient and hospital differences (Figure 2). Pneumonia remained the most frequent condition associated with unplanned ICU transfer (odds ratio [OR] 1.50; 95% CI 1.201.86). Although less frequent, sepsis had the strongest association of any condition with unplanned transfer (OR 2.51; 95% CI 1.903.31). However, metabolic, endocrine, and electrolyte conditions were no longer associated with unplanned transfer after adjustment, while arrhythmias and pulmonary embolism were. Other conditions confirmed to be associated with increased risk of unplanned transfer included: myocardial infarction (MI), chronic obstructive pulmonary disease (COPD), stroke, diabetic emergencies, catastrophic conditions (includes aortic catastrophes, all forms of shock except septic shock, and intracranial hemorrhage), and acute renal failure. After taking into account the frequency of admitting diagnoses, respiratory conditions (COPD, pneumonia/acute respiratory infection) comprised nearly half (47%) of all conditions associated with increased risk of unplanned ICU transfer.

Other factors confirmed to be independently associated with unplanned ICU transfer included: male sex (OR 1.20; 95% CI 1.131.28), high comorbidity burden as measured by COPS >145 (OR 1.13; 95% CI 1.031.24), increasingly abnormal physiology compared to a LAPS <7, and arrival on ward during the overnight shift (OR 1.10; 95% CI 1.011.21). After adjustment, we did find that admission to the TCU rather than a medicalsurgical unit was associated with decreased risk of unplanned ICU transfer (OR 0.83; 95% CI 0.770.90). Age 85 was associated with decreased risk of unplanned ICU transfer relative to the youngest age group of 1834‐year‐old patients (OR 0.64; 95% CI 0.530.77).

ED admissions to higher volume hospitals were 6% less likely to experience an unplanned transfer for each additional 1,000 annual ED hospitalizations over a lower volume hospital (OR 0.94; 95% CI 0.910.98). In other words, a patient admitted to a hospital with 8,000 annual ED hospitalizations had 30% decreased odds of unplanned ICU transfer compared to a hospital with only 3,000 annual ED hospitalizations.

DISCUSSION

Patients admitted with respiratory conditions accounted for half of all admitting diagnoses associated with increased risk of unplanned transfer to the ICU within 24 hours of arrival to the ward. We found that 1 in 30 ED ward admissions for pneumonia, and 1 in 33 for COPD, were transferred to the ICU within 24 hours. These findings indicate that there is some room for improvement in early care of respiratory conditions, given the average unplanned transfer rate of 1 in 42, and previous research showing that patients with pneumonia and patients with COPD, who experience unplanned ICU transfer, have substantially worse mortality than those directly admitted to the ICU.1

Although less frequent than hospitalizations for respiratory conditions, patients admitted with sepsis were at the highest risk of unplanned ICU transfer (1 in 17 ED non‐ICU hospitalizations). We also found that MI and stroke ward admissions had a higher risk of unplanned ICU transfer. However, we previously found that unplanned ICU transfers for sepsis, MI, and stroke did not have worse mortality than direct ICU admits for these conditions.1 Therefore, quality improvement efforts to reduce excess mortality related to early decompensation in the hospital and unplanned ICU transfer would be most effective if targeted towards respiratory conditions such as pneumonia and COPD.

This is the only in‐depth study, to our knowledge, to explore the association between a set of mutually exclusive diagnostic categories and risk of unplanned ICU transfer within 24 hours, and it is the first study to identify risk factors for unplanned ICU transfer in a multi‐hospital cohort adjusted for patient‐ and hospital‐level characteristics. We also identified a novel hospital volumeoutcome relationship: Unplanned ICU transfers are up to twice as likely to occur in the smallest volume hospitals compared with highest volume hospitals. Hospital volume has long been proposed as a proxy for hospital resources; there are several studies showing a relationship between low‐volume hospitals and worse outcomes for a number of conditions.19, 20 Possible mechanisms may include decreased ICU capacity, decreased on‐call intensivists in the hospital after hours, and less experience with certain critical care conditions seen more frequently in high‐volume hospitals.21

Patients at risk of unplanned ICU transfer were also more likely to have physiologic derangement identified on laboratory testing, high comorbidity burden, and arrive on the ward between 11 PM and 7 AM. Given the strong correlation between comorbidity burden and physiologic derangement and mortality,14 it is not surprising that the COPS and LAPS were independent predictors of unplanned transfer. It is unclear, however, why arriving on the ward on the overnight shift is associated with higher risk. One possibility is that patients who arrive on the wards during 11 PM to 7 AM are also likely to have been in the ED during evening peak hours most associated with ED crowding.22 High levels of ED crowding have been associated with delays in care, worse quality care, lapses in patient safety, and even increased in‐hospital mortality.22, 23 Other possible reasons include decreased in‐hospital staffing and longer delays in critical diagnostic tests and interventions.2428

Admission to TCUs was associated with decreased risk of unplanned ICU transfer in the first 24 hours of hospitalization. This may be due to the continuous monitoring, decreased nursing‐to‐patient ratios, or the availability to provide some critical care interventions. In our study, age 85 was associated with lower likelihood of unplanned transfer. Unfortunately, we did not have access to data on advanced directives or patient preferences. Data on advanced directives would help to distinguish whether this phenomenon was related to end‐of‐life care goals versus other explanations.

Our study confirms some risk factors identified in previous studies. These include specific diagnoses such as pneumonia and COPD,12, 13, 29 heavy comorbidity burden,12, 13, 29 abnormal labs,29 and male sex.13 Pneumonia has consistently been shown to be a risk factor for unplanned ICU transfer. This may stem from the dynamic nature of this condition and its ability to rapidly progress, and the fact that some ICUs may not accept pneumonia patients unless they demonstrate a need for mechanical ventilation.30 Recently, a prediction rule has been developed to determine which patients with pneumonia are likely to have an unplanned ICU transfer.30 It is possible that with validation and application of this rule, unplanned transfer rates for pneumonia could be reduced. It is unclear whether males have unmeasured factors associated with increased risk of unplanned transfer or whether a true gender disparity exists.

Our findings should be interpreted within the context of this study's limitations. First, this study was not designed to distinguish the underlying cause of the unplanned transfer such as under‐recognition of illness severity in the ED, evolving clinical disease after leaving the ED, or delays in critical interventions on the ward. These are a focus of our ongoing research efforts. Second, while previous studies have demonstrated that our automated risk adjustment variables can accurately predict in‐hospital mortality (0.88 area under curve in external populations),17 additional data on vital signs and mental status could further improve risk adjustment. However, using automated data allowed us to study risk factors for unplanned transfer in a multi‐hospital cohort with a much larger population than has been previously studied. Serial data on vital signs and mental status both in the ED and during hospitalization could also be helpful in determining which unplanned transfers could be prevented with earlier recognition and intervention. Finally, all patient care occurred within an integrated healthcare delivery system. Thus, differences in case‐mix, hospital resources, ICU structure, and geographic location should be considered when applying our results to other healthcare systems.

This study raises several new areas for future research. With access to richer data becoming available in electronic medical records, prediction rules should be developed to enable better triage to appropriate levels of care for ED admissions. Future research should also analyze the comparative effectiveness of intermediate monitored units versus non‐monitored wards for preventing clinical deterioration by admitting diagnosis. Diagnoses that have been shown to have an increased risk of death after unplanned ICU transfer, such as pneumonia/respiratory infection and COPD,1 should be prioritized in this research. Better understanding is needed on the diagnosis‐specific differences and the differences in ED triage process and ICU structure that may explain why high‐volume hospitals have significantly lower rates of early unplanned ICU transfers compared with low‐volume hospitals. In particular, determining the effect of TCU and ICU capacities and census at the time of admission, and comparing patient risk characteristics across hospital‐volume strata would be very useful. Finally, more work is needed to determine whether the higher rate of unplanned transfers during overnight nursing shifts is related to decreased resource availability, preceding ED crowding, or other organizational causes.

In conclusion, patients admitted with respiratory conditions, sepsis, MI, high comorbidity, and abnormal labs are at modestly increased risk of unplanned ICU transfer within 24 hours of admission from the ED. Patients admitted with respiratory conditions (pneumonia/respiratory infections and COPD) accounted for half of the admitting diagnoses that are at increased risk for unplanned ICU transfer. These patients may benefit from better inpatient triage from the ED, earlier intervention, or closer monitoring. More research is needed to determine the specific aspects of care associated with admission to intermediate care units and high‐volume hospitals that reduce the risk of unplanned ICU transfer.

The Centers for Disease Control and Prevention (CDC) established a case definition during the 2009 influenza season for influenza‐like illness (ILI): a temperature of 100.0F (37.8C), oral or equivalent, and cough or sore throat, in the absence of a known cause other than influenza.1 Even though this definition is currently being revised, fever is still listed as a predominant symptom for influenza on the CDC Web site.2, 3

During the spring of 2009 in New York City, 95% of patients with pandemic hemagglutinin type 1 and neuraminidase type 1 (H1N1) influenza A met the case definition for ILI,4 whereas a report from Mexico5 and another from China6 reported that about one‐third of patients presented without fever. A recent study found that the clinical features of H1N1/09 influenza and seasonal strains were similar in hospitalized patients.7 Until recently, the CDC case definition for ILI included fever as a requisite symptom. Most clinicians still consider influenza a fever‐based illness.8, 9 Some studies have reported that seeking early medical care and early treatment for influenza resulted in reduced severity and duration of illness.1013 However, individuals without fever may not have sought early care because their illnesses did not meet all the criteria for ILI.

We hypothesized that significant number of our patients hospitalized during the H1N1/09 influenza pandemic did not have fever as part of the influenza‐like illness prior to hospital presentation. We further hypothesized that this absence of fever was associated with delayed presentation to the hospital and delayed treatment. No prior studies have assessed the effect of fever on hospital presentation, diagnosis, and treatment of H1N1/09 patients. We performed a retrospective cohort analysis comparing H1N1/09 patients with fever as part of their illness prior to hospital presentation to those without fever, to assess the influence of fever on hospital presentation and on diagnosis and treatment in hospitalized patients with laboratory‐confirmed H1N1/09 infection.

METHODS

RESULTS

We identified 135 hospitalized patients with laboratory‐confirmed pandemic H1N1 virus infection during the study period. A substantial number of patients (n = 56, 42%) had no fever as part of their illness prior to hospital presentation. Thirty‐one patients (23%) required treatment in an ICU during hospitalization, and 9 (7%) died. While we observed no differences between patients with fever and those without fever prior to hospital presentation in gender, race, or age (Table 1), patients without fever had higher Charlson index compared to those with fever (P = 0.01, Wilcoxon rank‐sum test). Patients without fever also had significantly longer time to presentation to the hospital since the onset of illness, compared to those with fever (median of 4 vs 2 days, P < 0.001). Patients without fever also had significantly longer time to initiation of proper treatment since the onset of illness, compared to those with fever (median of 5 vs 2 days, P = 0.001).

Patients were rapidly diagnosed and treated after presentation to the hospital with no significant difference by fever status at presentation (P = 0.17 and 0.07 for differences in time from hospital presentation to diagnosis and treatment, respectively). While 82% (65/79) of patients with fever received empiric antibiotic treatment, only 59% (33/56) of those with no fever received empiric antibiotic treatment (P = 0.003). Prevalence of bacterial coinfection was similar between the 2 groups (5% in the fever group vs 9% in the group without fever; P = 0.067). Patients without fever were more frequently in an ICU than those with fever (P = 0.01), but we observed no significant differences in mortality by fever status at presentation in the small number of deaths during the study period (P = 0.61). Compared to patients with no fever, after adjustment for age (<40 vs 40) and Charlson index (0, 12, 3), patients with fever had significantly decreased likelihood of late hospital presentation (>2 days from the onset of illness) (adjusted odds ratio [OR]: 0.27, P = 0.001). Patients with fever also had decreased likelihood of ICU stay compared to patients with no fever, after adjustment for age and Charlson index (adjusted OR: 0.42, P = 0.05) (Table 2).

Patients who presented to the hospital more than 2 days after the onset of illness were significantly more likely to be treated in an ICU during their hospitalization (P < 0.001). Among 71 patients who presented to the hospital more than 2 days after the onset of illness, 27 patients were treated in an ICU and 44 patients did not require ICU care. Among 64 patients who presented to the hospital within 2 days of the onset of illness, 4 patients were treated in an ICU and 60 patients did not require ICU care. Those who presented to the hospital more than 2 days after the onset of illness also had a nonsignificantly higher death rate: 7 of 71 (10%) versus 2 of 64 (3%). Among the comorbidities tested, patients with no fever were significantly more likely to have diabetes mellitus (P = 0.03).

ICU patients had a slightly higher median Charlson index than those who did not receive ICU care (median score 1 vs score 0 for ICU patients vs non‐ICU patients, respectively; P = 0.052). We observed no difference in median Charlson index between patients with late hospital presentation (>2 days from onset of illness) and those with early hospital presentation (2 days from onset of illness) (P = 0.51).

DISCUSSION

We present the first study, to our knowledge, to assess the influence of presence of fever, as part of the illness prior to hospital presentation, on time to presentation and treatment for H1N1/09 patients. By comparing patients who had fever with those who did not, we showed that patients with fever tended to seek medical care sooner and thus received treatment sooner. Those without fever may not have realized they needed to seek medical care until much later in the course of disease compared to those with fever. Given that ICU stay was significantly more likely in patients with late presentation and no fever, these findings have important medical and economic consequences for individual patients and healthcare systems. Our findings, in conjunction with previous studies indicating reduced disease severity with early treatment of influenza,1013, can be used to educate the public to seek medical care for influenza early, even in the absence of fever.

Our study confirms previous observations that a significant number of patients with H1N1/09 did not have fever.5, 6 Nevertheless, fever appeared to trigger initiation of empiric antibiotic treatment. Empiric antibiotic use was significantly higher in the fever group. There was no difference in the median Charlson index among those who received antibiotic treatment and those who did not, suggesting that comorbidities are unlikely to explain the observed association. Empiric antibiotic treatment appeared not to affect whether or not a patient required ICU care during the hospitalization, though this association is difficult to measure because the majority of ICU patients (84%) were admitted to an ICU on the same day as hospital admission. We saw no difference in laboratory‐confirmed bacterial coinfection between patients presenting with and without fever. We did not observe a significant effect of late presentation or fever on mortality, though the small number of deaths in the study likely limited our ability to detect any association.

Recent studies have shown that clinical signs cannot reliably differentiate H1N1/09‐positive and H1N1/09‐negative patients.18, 19 In our study, patients in the fever and non‐fever groups received prompt diagnosis and treatment once they presented to the hospital. This may be because clinicians were encouraged to test patients based on any suspicion for influenza infection and to treat for influenza while waiting for the result of testing. This may explain the similar timeframes for diagnosis and treatment between the fever and non‐fever groups.

The Centers for Disease Control and Prevention (CDC) established a case definition during the 2009 influenza season for influenza‐like illness (ILI): a temperature of 100.0F (37.8C), oral or equivalent, and cough or sore throat, in the absence of a known cause other than influenza.1 Even though this definition is currently being revised, fever is still listed as a predominant symptom for influenza on the CDC Web site.2, 3

During the spring of 2009 in New York City, 95% of patients with pandemic hemagglutinin type 1 and neuraminidase type 1 (H1N1) influenza A met the case definition for ILI,4 whereas a report from Mexico5 and another from China6 reported that about one‐third of patients presented without fever. A recent study found that the clinical features of H1N1/09 influenza and seasonal strains were similar in hospitalized patients.7 Until recently, the CDC case definition for ILI included fever as a requisite symptom. Most clinicians still consider influenza a fever‐based illness.8, 9 Some studies have reported that seeking early medical care and early treatment for influenza resulted in reduced severity and duration of illness.1013 However, individuals without fever may not have sought early care because their illnesses did not meet all the criteria for ILI.

We hypothesized that significant number of our patients hospitalized during the H1N1/09 influenza pandemic did not have fever as part of the influenza‐like illness prior to hospital presentation. We further hypothesized that this absence of fever was associated with delayed presentation to the hospital and delayed treatment. No prior studies have assessed the effect of fever on hospital presentation, diagnosis, and treatment of H1N1/09 patients. We performed a retrospective cohort analysis comparing H1N1/09 patients with fever as part of their illness prior to hospital presentation to those without fever, to assess the influence of fever on hospital presentation and on diagnosis and treatment in hospitalized patients with laboratory‐confirmed H1N1/09 infection.

METHODS

RESULTS

We identified 135 hospitalized patients with laboratory‐confirmed pandemic H1N1 virus infection during the study period. A substantial number of patients (n = 56, 42%) had no fever as part of their illness prior to hospital presentation. Thirty‐one patients (23%) required treatment in an ICU during hospitalization, and 9 (7%) died. While we observed no differences between patients with fever and those without fever prior to hospital presentation in gender, race, or age (Table 1), patients without fever had higher Charlson index compared to those with fever (P = 0.01, Wilcoxon rank‐sum test). Patients without fever also had significantly longer time to presentation to the hospital since the onset of illness, compared to those with fever (median of 4 vs 2 days, P < 0.001). Patients without fever also had significantly longer time to initiation of proper treatment since the onset of illness, compared to those with fever (median of 5 vs 2 days, P = 0.001).

Patients were rapidly diagnosed and treated after presentation to the hospital with no significant difference by fever status at presentation (P = 0.17 and 0.07 for differences in time from hospital presentation to diagnosis and treatment, respectively). While 82% (65/79) of patients with fever received empiric antibiotic treatment, only 59% (33/56) of those with no fever received empiric antibiotic treatment (P = 0.003). Prevalence of bacterial coinfection was similar between the 2 groups (5% in the fever group vs 9% in the group without fever; P = 0.067). Patients without fever were more frequently in an ICU than those with fever (P = 0.01), but we observed no significant differences in mortality by fever status at presentation in the small number of deaths during the study period (P = 0.61). Compared to patients with no fever, after adjustment for age (<40 vs 40) and Charlson index (0, 12, 3), patients with fever had significantly decreased likelihood of late hospital presentation (>2 days from the onset of illness) (adjusted odds ratio [OR]: 0.27, P = 0.001). Patients with fever also had decreased likelihood of ICU stay compared to patients with no fever, after adjustment for age and Charlson index (adjusted OR: 0.42, P = 0.05) (Table 2).

Patients who presented to the hospital more than 2 days after the onset of illness were significantly more likely to be treated in an ICU during their hospitalization (P < 0.001). Among 71 patients who presented to the hospital more than 2 days after the onset of illness, 27 patients were treated in an ICU and 44 patients did not require ICU care. Among 64 patients who presented to the hospital within 2 days of the onset of illness, 4 patients were treated in an ICU and 60 patients did not require ICU care. Those who presented to the hospital more than 2 days after the onset of illness also had a nonsignificantly higher death rate: 7 of 71 (10%) versus 2 of 64 (3%). Among the comorbidities tested, patients with no fever were significantly more likely to have diabetes mellitus (P = 0.03).

ICU patients had a slightly higher median Charlson index than those who did not receive ICU care (median score 1 vs score 0 for ICU patients vs non‐ICU patients, respectively; P = 0.052). We observed no difference in median Charlson index between patients with late hospital presentation (>2 days from onset of illness) and those with early hospital presentation (2 days from onset of illness) (P = 0.51).

DISCUSSION

We present the first study, to our knowledge, to assess the influence of presence of fever, as part of the illness prior to hospital presentation, on time to presentation and treatment for H1N1/09 patients. By comparing patients who had fever with those who did not, we showed that patients with fever tended to seek medical care sooner and thus received treatment sooner. Those without fever may not have realized they needed to seek medical care until much later in the course of disease compared to those with fever. Given that ICU stay was significantly more likely in patients with late presentation and no fever, these findings have important medical and economic consequences for individual patients and healthcare systems. Our findings, in conjunction with previous studies indicating reduced disease severity with early treatment of influenza,1013, can be used to educate the public to seek medical care for influenza early, even in the absence of fever.

Our study confirms previous observations that a significant number of patients with H1N1/09 did not have fever.5, 6 Nevertheless, fever appeared to trigger initiation of empiric antibiotic treatment. Empiric antibiotic use was significantly higher in the fever group. There was no difference in the median Charlson index among those who received antibiotic treatment and those who did not, suggesting that comorbidities are unlikely to explain the observed association. Empiric antibiotic treatment appeared not to affect whether or not a patient required ICU care during the hospitalization, though this association is difficult to measure because the majority of ICU patients (84%) were admitted to an ICU on the same day as hospital admission. We saw no difference in laboratory‐confirmed bacterial coinfection between patients presenting with and without fever. We did not observe a significant effect of late presentation or fever on mortality, though the small number of deaths in the study likely limited our ability to detect any association.

Recent studies have shown that clinical signs cannot reliably differentiate H1N1/09‐positive and H1N1/09‐negative patients.18, 19 In our study, patients in the fever and non‐fever groups received prompt diagnosis and treatment once they presented to the hospital. This may be because clinicians were encouraged to test patients based on any suspicion for influenza infection and to treat for influenza while waiting for the result of testing. This may explain the similar timeframes for diagnosis and treatment between the fever and non‐fever groups.

The Centers for Disease Control and Prevention (CDC) established a case definition during the 2009 influenza season for influenza‐like illness (ILI): a temperature of 100.0F (37.8C), oral or equivalent, and cough or sore throat, in the absence of a known cause other than influenza.1 Even though this definition is currently being revised, fever is still listed as a predominant symptom for influenza on the CDC Web site.2, 3

During the spring of 2009 in New York City, 95% of patients with pandemic hemagglutinin type 1 and neuraminidase type 1 (H1N1) influenza A met the case definition for ILI,4 whereas a report from Mexico5 and another from China6 reported that about one‐third of patients presented without fever. A recent study found that the clinical features of H1N1/09 influenza and seasonal strains were similar in hospitalized patients.7 Until recently, the CDC case definition for ILI included fever as a requisite symptom. Most clinicians still consider influenza a fever‐based illness.8, 9 Some studies have reported that seeking early medical care and early treatment for influenza resulted in reduced severity and duration of illness.1013 However, individuals without fever may not have sought early care because their illnesses did not meet all the criteria for ILI.

We hypothesized that significant number of our patients hospitalized during the H1N1/09 influenza pandemic did not have fever as part of the influenza‐like illness prior to hospital presentation. We further hypothesized that this absence of fever was associated with delayed presentation to the hospital and delayed treatment. No prior studies have assessed the effect of fever on hospital presentation, diagnosis, and treatment of H1N1/09 patients. We performed a retrospective cohort analysis comparing H1N1/09 patients with fever as part of their illness prior to hospital presentation to those without fever, to assess the influence of fever on hospital presentation and on diagnosis and treatment in hospitalized patients with laboratory‐confirmed H1N1/09 infection.

METHODS

RESULTS

We identified 135 hospitalized patients with laboratory‐confirmed pandemic H1N1 virus infection during the study period. A substantial number of patients (n = 56, 42%) had no fever as part of their illness prior to hospital presentation. Thirty‐one patients (23%) required treatment in an ICU during hospitalization, and 9 (7%) died. While we observed no differences between patients with fever and those without fever prior to hospital presentation in gender, race, or age (Table 1), patients without fever had higher Charlson index compared to those with fever (P = 0.01, Wilcoxon rank‐sum test). Patients without fever also had significantly longer time to presentation to the hospital since the onset of illness, compared to those with fever (median of 4 vs 2 days, P < 0.001). Patients without fever also had significantly longer time to initiation of proper treatment since the onset of illness, compared to those with fever (median of 5 vs 2 days, P = 0.001).

Patients were rapidly diagnosed and treated after presentation to the hospital with no significant difference by fever status at presentation (P = 0.17 and 0.07 for differences in time from hospital presentation to diagnosis and treatment, respectively). While 82% (65/79) of patients with fever received empiric antibiotic treatment, only 59% (33/56) of those with no fever received empiric antibiotic treatment (P = 0.003). Prevalence of bacterial coinfection was similar between the 2 groups (5% in the fever group vs 9% in the group without fever; P = 0.067). Patients without fever were more frequently in an ICU than those with fever (P = 0.01), but we observed no significant differences in mortality by fever status at presentation in the small number of deaths during the study period (P = 0.61). Compared to patients with no fever, after adjustment for age (<40 vs 40) and Charlson index (0, 12, 3), patients with fever had significantly decreased likelihood of late hospital presentation (>2 days from the onset of illness) (adjusted odds ratio [OR]: 0.27, P = 0.001). Patients with fever also had decreased likelihood of ICU stay compared to patients with no fever, after adjustment for age and Charlson index (adjusted OR: 0.42, P = 0.05) (Table 2).

Patients who presented to the hospital more than 2 days after the onset of illness were significantly more likely to be treated in an ICU during their hospitalization (P < 0.001). Among 71 patients who presented to the hospital more than 2 days after the onset of illness, 27 patients were treated in an ICU and 44 patients did not require ICU care. Among 64 patients who presented to the hospital within 2 days of the onset of illness, 4 patients were treated in an ICU and 60 patients did not require ICU care. Those who presented to the hospital more than 2 days after the onset of illness also had a nonsignificantly higher death rate: 7 of 71 (10%) versus 2 of 64 (3%). Among the comorbidities tested, patients with no fever were significantly more likely to have diabetes mellitus (P = 0.03).

ICU patients had a slightly higher median Charlson index than those who did not receive ICU care (median score 1 vs score 0 for ICU patients vs non‐ICU patients, respectively; P = 0.052). We observed no difference in median Charlson index between patients with late hospital presentation (>2 days from onset of illness) and those with early hospital presentation (2 days from onset of illness) (P = 0.51).

DISCUSSION

We present the first study, to our knowledge, to assess the influence of presence of fever, as part of the illness prior to hospital presentation, on time to presentation and treatment for H1N1/09 patients. By comparing patients who had fever with those who did not, we showed that patients with fever tended to seek medical care sooner and thus received treatment sooner. Those without fever may not have realized they needed to seek medical care until much later in the course of disease compared to those with fever. Given that ICU stay was significantly more likely in patients with late presentation and no fever, these findings have important medical and economic consequences for individual patients and healthcare systems. Our findings, in conjunction with previous studies indicating reduced disease severity with early treatment of influenza,1013, can be used to educate the public to seek medical care for influenza early, even in the absence of fever.

Our study confirms previous observations that a significant number of patients with H1N1/09 did not have fever.5, 6 Nevertheless, fever appeared to trigger initiation of empiric antibiotic treatment. Empiric antibiotic use was significantly higher in the fever group. There was no difference in the median Charlson index among those who received antibiotic treatment and those who did not, suggesting that comorbidities are unlikely to explain the observed association. Empiric antibiotic treatment appeared not to affect whether or not a patient required ICU care during the hospitalization, though this association is difficult to measure because the majority of ICU patients (84%) were admitted to an ICU on the same day as hospital admission. We saw no difference in laboratory‐confirmed bacterial coinfection between patients presenting with and without fever. We did not observe a significant effect of late presentation or fever on mortality, though the small number of deaths in the study likely limited our ability to detect any association.

Recent studies have shown that clinical signs cannot reliably differentiate H1N1/09‐positive and H1N1/09‐negative patients.18, 19 In our study, patients in the fever and non‐fever groups received prompt diagnosis and treatment once they presented to the hospital. This may be because clinicians were encouraged to test patients based on any suspicion for influenza infection and to treat for influenza while waiting for the result of testing. This may explain the similar timeframes for diagnosis and treatment between the fever and non‐fever groups.

There are over 350,000 cases of out‐of‐hospital cardiac arrest (OHCA) each year in the United States1, 2 and, with supportive therapy alone, only a fraction of victims survive to hospital discharge. Rapid intervention including cardiopulmonary resuscitation in the moments following arrest is critical to minimizing neurologic injury, morbidity, and mortality. In 2002, two small randomized controlled trials showed a survival benefit of therapeutic hypothermia (TH) when provided within 12 hours after return of circulation following an OHCA and, to date, TH remains one of the few interventions with proven mortality benefit after initial cardiopulmonary resuscitation.3, 4 Since 2003, TH has been incorporated into the American Heart Association practice guidelines57 and use of TH has steadily increased, but widespread clinical uptake remains low.8, 9

The initial studies that evaluated TH were small, with only 189 patients included in the TH arms of the 2 trials combined. To date, only a few studies have replicated this initial observation in real‐world settings, with little analysis of outcomes in US centers in particular.1013 Accordingly, we aimed to examine the real‐world experience with TH in the United States using a large administrative claims database of all California hospital admissions to describe utilization trends, hospital mortality, and volumeoutcome relationships associated with the intervention.

MATERIALS AND METHODS

RESULTS

Descriptive Data

Table 1 reports summary statistics for patients with cardiac arrest complicated by neurologic insult (anoxic brain injury, coma, or persistent vegetative state) between 1999 and 2008. Across all years, 204 patients were identified as undergoing TH. In comparison, 105 patients were identified as undergoing TH in 2008 alone. Patients who underwent TH were less likely to be male (30.7% vs 44.6% male, P < 0.01), were younger (63.9 15.0 years vs 67.3 15.7 years, P = 0.03), and had equivalent numbers of Charlson‐Deyo comorbidities (2.5 2.0 diagnoses vs 2.5 2.0 diagnoses, P = 0.89). Therapeutic hypothermia was more commonly employed at teaching hospitals (51/3709 [1.4%] vs 153/42,942 [0.4%], P < 0.01). There was a trend toward decreased unadjusted mortality among patients who underwent therapeutic hypothermia compared with those who did not (56.9% vs 62.8%, P = 0.08).

Table 1.Characteristics of Patients Suffering Cardiac Arrest Complicated by Neurologic Insult, Stratified by Therapeutic Hypothermia Use, California Hospitals 19992008

	Therapeutic Hypothermia	No Therapeutic Hypothermia	P Value
P values for age, hospital mortality, and number of Charlson‐Deyo comorbidities reflect 2‐sided t test of continuous variables; P values for all other variables reflect 2 test of categorical data.
No. observations	204	46,629
No. cases in teaching hospitals	51	3,658
No. cases in non‐teaching hospitals	153	42,789
Age, y	63.9 15.0	67.3 15.7	0.06
Male	30.7	44.6	<0.01
Hospital mortality, %	56.9	62.8	0.08
Comorbidities
No. Charlson‐Deyo comorbidities	2.5 2.0	2.5 2.0	0.89
Coronary artery disease, %	48.0	38.0	<0.01
Acute myocardial infarction, %	42.6	28.9	<0.01
Congestive heart failure, %	27.9	35.3	0.03
Hypertension, %	36.3	33.2	0.83
Acute renal failure, %	33.3	26.6	0.03
Diabetes mellitus, %	30.9	23.0	<0.01
Chronic obstructive pulmonary disease, %	10.3	19.3	<0.01

Figures 1 and 2 provide additional aggregate statistics on therapeutic hypothermia in California hospitals. Figure 1 plots the number of therapeutic hypothermia cases recorded in the administrative discharge registry between 1999 and 2008. Of the 204 total cases identified during this period, 178 (87.3%) were performed between 2006 and 2008. Figure 2 shows the distribution of TH cases across centers that performed therapeutic hypothermia (n = 47 hospitals, 11.3 % of all hospitals). Ten centers accounted for 124/204 (60.7%) of the total patients treated with TH after cardiac arrest; the top 3 centers accounted for 64 (31.4%) of the treated patients. Twenty‐seven hospitals were identified as performing therapeutic hypothermia on only 1 or 2 patients between 1999 and 2008.

Figure 1

Figure 2

Risk‐Adjusted Mortality

Table 2 presents the odds ratio of factors predicting in‐hospital mortality after cardiac arrest complicated by neurologic insult. Factors include use of TH after cardiac arrest, age, gender, year of admission, number of Charlson‐Deyo comorbidities, hospital teaching status, and volume tercile of hospitals that performed therapeutic hypothermia. Overall, patients who were older, male, and had greater comorbidities were statistically more likely to die after cardiac arrest complicated by neurologic insult. Regardless of whether they underwent TH, patients admitted to hospitals in the highest volume tercile of TH use were more likely to die after cardiac arrest. Adjusting for volume tercile, teaching hospital status was not independently associated with mortality after cardiac arrest. The adjusted odds ratio of mortality among patients undergoing therapeutic hypothermia was 0.80 (95% confidence interval [CI] 0.601.06, P = 0.11). The adjusted probability of inpatient mortality among patients undergoing therapeutic hypothermia was 57.5% (95% CI 50.764.3%) compared to those who did not 62.8% (95% CI 61.763.9%, P = 0.11).

Table 2.Adjusted Odds Ratio of Hospital Mortality After Cardiac Arrest Complicated by Neurologic Insult in a Multivariable Regression Model, California Hospitals 19992008

Variable	Odds Ratio of Hospital Mortality (95% CI)	P Value
Abbreviations: CI, confidence interval; TH, therapeutic hypothermia. *Odds ratios compared to individuals aged 6065. Odds ratios compared to hospitals not performing TH.
No. observations	46,651
Age*
6569	1.19 (1.121.28)	<0.001
7074	1.29 (1.201.39)	<0.001
7579	1.55 (1.441.67)	<0.001
8084	1.79 (1.651.93)	<0.001
85 and over	2.06 (1.892.25)	<0.001
Male	1.15 (1.101.21)	<0.001
Teaching hospital	1.13 (0.951.34)	0.17
No. Charlson‐Deyo comorbidities	1.09 (1.081.10)	<0.001
Year trend	0.98 (0.970.99)	<0.001
Volume tercile among hospitals performing TH
First tercile	0.94 (0.791.12)	0.48
Second tercile	1.03 (0.801.33)	0.82
Third tercile	1.20 (1.051.36)	0.006
Therapeutic hypothermia	0.80 (0.601.06)	0.11

Figure 3 presents adjusted mortality after cardiac arrest in hospitals that did not perform TH, as well as adjusted mortality associated with TH for each volume tercile of hospitals that performed the procedure. Hospital mortality rates among patients not receiving TH after cardiac arrest were slightly higher in hospitals in the high volume tercile of TH (66.3%, 95% CI 63.868.8%) compared to hospitals in low and moderate volume terciles and to hospitals not performing TH (P < 0.001). Hospital mortality rates among low and moderate TH volume centers and in centers not performing TH were similar (62.3%, 61.3%, and 63.4%, respectively). Among both the low volume and moderate volume terciles, however, patients who underwent TH after cardiac arrest were significantly less likely to die in‐hospital compared to those who did not. For patients admitted to hospitals in the low volume tercile, those undergoing therapeutic hypothermia had an adjusted hospital mortality rate of 25.5% (95% CI 3.047.9%) compared to those who did not undergo TH (adjusted mortality 61.3%, 95% CI 57.465.1%), P < 0.001. In the moderate volume tercile, patients receiving therapeutic hypothermia had an adjusted hospital mortality rate of 31.0% (95% CI 9.2%52.8%) compared to 63.4% (95% CI 57.769.1%), P < 0.001, among those not undergoing the procedure. There was no statistically significant difference in adjusted mortality between those who underwent TH and those who did not, in hospitals in the highest volume tercile (P = 0.211). In addition to examining how volume of therapeutic hypothermia performed by hospitals affected the association between TH and hospital mortality, we also examined whether year of admission and teaching hospital independently modified the association. Neither year of admission nor teaching hospital statistically significantly affected the association between therapeutic hypothermia and hospital mortality after cardiac arrest at the P < 0.10 level.

Figure 3

DISCUSSION

In an administrative database of all admissions to California hospitals, we demonstrated that use of TH increased steadily since the publication of the initial clinical trials in 2002. The absolute level of TH utilization in our study undoubtedly represents a significant underestimation of actual TH utilization, however, our study does provide an assessment of the utilization trends over time. The bulk of TH use appears to be performed in a small group of high volume centers, and 89% of California hospitals did not perform TH during the study period (as assessed by procedure billing codes). Additionally, within the limitations of a retrospective, administrative claims‐based study design, TH appears to be associated with a similar in‐hospital mortality rate to that seen in clinical trials.3, 4 In exploratory analyses, there appears to be a particular benefit of TH in low and moderate volume centers, though these findings should be considered hypothesis‐generating.

Despite the body of evidence in favor of TH, utilization in our study and others appears quite low. In a 2005 survey of physicians, 87% of respondents had never used therapeutic hypothermia, citing inadequate data, technical limitations, and lack of incorporation in the Advanced Cardiac Life Support (ACLS) protocol as principal justifications.8 Other surveys have shown similar results and noted that critical care physicians and those working in large medical centers were more likely to adopt the therapy.9 Advocates of the therapy have suggested that an explicit hospital‐based plan developed by key stakeholders can help facilitate implementation.21 Accordingly, there is growing interest in developing centers of expertise in highly intensive therapies such as TH. For instance, the New York City Emergency Medical Service has begun to explore a protocol to divert TH candidates to specialized centers.22, 23 Some favorable results have been reported in individual hospitals and local hospital systems.2428

Our data suggest that TH is associated with an in‐hospital mortality rate that is comparable to that reported in the clinical trials. For example, in a 2009 meta‐analysis of 4 clinical trials and 1 abstract (481 patients in total), TH was associated with a 35% relative mortality benefit as compared to standard post‐resuscitation care.29 It has been estimated that broad TH implementation could save thousands of lives30 and many authors have advocated for its use and outlined explicit protocols for implementation.17 Furthermore, TH appears to be cost‐effective in line with other accepted therapies. Assuming the Hypothermia After Cardiac Arrest (HACA) trial inclusion criteria, even at extreme estimates for costs, the cost‐effectiveness of hypothermia remains less than $100,000 per quality‐adjusted life year.31

There are important limitations of this study. Our use of administrative claims data certainly underestimates the level of TH utilization, since we could only identify cases in which TH was included in the billing codes for the hospitalization. Hospitals may vary in utilization of this particular billing code for TH in ways that bias our estimated associations. The ICD‐9 code 99.81 for therapeutic hypothermia was also not developed for post‐cardiac arrest TH specifically, so use of the code may actually lag clinical utilization. Although the observed trend in TH utilization is likely mainly due to a true increase in utilization, it is possible that some of the observed increase is due to an increase in utilization of TH procedure billing codes. Our TH utilization estimates should be construed as a lower bound of the actual rates. Additionally, although the estimated real‐world mortality benefit of TH may be comparable to that of clinical trials, the equivalence of patients in our sample to those in published randomized trials is uncertain. Similarly, even after adjusting for age, gender, year of admission, comorbidities, hospital teaching status, and TH volume, there are likely many unmeasured variables that influence mortality in both the TH and comparison groups. There are also likely patients included in our comparison group who had both cardiac arrest or ventricular tachycardia and anoxic brain injury, but who were not candidates for TH as the episode of cardiac arrest followed rather than preceded the anoxic brain injury. Since we lack detailed clinical data about the TH cases (initial rhythm, time before return of circulation, preexisting disease states, etc.), we are unable to match controls directly to cases. Additionally, we lack data to assess neurologic recovery or quality of life after arrest.

The observation that a mortality benefit in our study could be detected only in low and moderate volume centers requires further exploration. Indeed, one might expect that high volume centers may have better outcomes with TH as a result of more robust infrastructure, technical experience, and available resources. Our finding that mortality benefits of TH appear concentrated in centers with low to moderate volume of TH utilization suggest at least 1 of 2 possibilities. First, low and moderate volume centers may perform TH in a subset of patients who benefit most from the intervention or, alternatively, in the most viable cardiac arrest cases (those who may fare well with or without the therapy). Consequently, we may observe relatively favorable outcomes in this group due to this selection bias. Second, high volume centersdespite having more expertisemay also attract patients at higher mortality risk due to referral bias. This would lead us to estimate lower mortality benefits associated with TH in these high volume centers. Indeed, greater observed mortality at high volume centers regardless of TH status suggests that overall acuity is higher at high volume centers. While our inferences are greatly affected by issues of case selection and referral bias, it also important to consider the possibility that the estimated mortality benefit of TH in higher volume centers is lower because of the selection of patients who do not meet current guidelines for treatment with TH. Distinguishing whether the selection of patients undergoing TH at high volume centers is appropriate or inappropriate based on current guidelines is an important issue that merits further research with datasets with more refined patient clinical information.

In summary, therapeutic hypothermia utilization is low, but the rate of implementation has increased since the publication of the initial clinical trials in 2002. The bulk of TH utilization appears limited to a subset of high volume centers, and most centers in California appear to have not used the therapy. Real‐world in‐hospital mortality associated with TH is comparable to that reported in randomized clinical trials.

There are over 350,000 cases of out‐of‐hospital cardiac arrest (OHCA) each year in the United States1, 2 and, with supportive therapy alone, only a fraction of victims survive to hospital discharge. Rapid intervention including cardiopulmonary resuscitation in the moments following arrest is critical to minimizing neurologic injury, morbidity, and mortality. In 2002, two small randomized controlled trials showed a survival benefit of therapeutic hypothermia (TH) when provided within 12 hours after return of circulation following an OHCA and, to date, TH remains one of the few interventions with proven mortality benefit after initial cardiopulmonary resuscitation.3, 4 Since 2003, TH has been incorporated into the American Heart Association practice guidelines57 and use of TH has steadily increased, but widespread clinical uptake remains low.8, 9

The initial studies that evaluated TH were small, with only 189 patients included in the TH arms of the 2 trials combined. To date, only a few studies have replicated this initial observation in real‐world settings, with little analysis of outcomes in US centers in particular.1013 Accordingly, we aimed to examine the real‐world experience with TH in the United States using a large administrative claims database of all California hospital admissions to describe utilization trends, hospital mortality, and volumeoutcome relationships associated with the intervention.

MATERIALS AND METHODS

RESULTS

Descriptive Data

Table 1 reports summary statistics for patients with cardiac arrest complicated by neurologic insult (anoxic brain injury, coma, or persistent vegetative state) between 1999 and 2008. Across all years, 204 patients were identified as undergoing TH. In comparison, 105 patients were identified as undergoing TH in 2008 alone. Patients who underwent TH were less likely to be male (30.7% vs 44.6% male, P < 0.01), were younger (63.9 15.0 years vs 67.3 15.7 years, P = 0.03), and had equivalent numbers of Charlson‐Deyo comorbidities (2.5 2.0 diagnoses vs 2.5 2.0 diagnoses, P = 0.89). Therapeutic hypothermia was more commonly employed at teaching hospitals (51/3709 [1.4%] vs 153/42,942 [0.4%], P < 0.01). There was a trend toward decreased unadjusted mortality among patients who underwent therapeutic hypothermia compared with those who did not (56.9% vs 62.8%, P = 0.08).

Table 1.Characteristics of Patients Suffering Cardiac Arrest Complicated by Neurologic Insult, Stratified by Therapeutic Hypothermia Use, California Hospitals 19992008

	Therapeutic Hypothermia	No Therapeutic Hypothermia	P Value
P values for age, hospital mortality, and number of Charlson‐Deyo comorbidities reflect 2‐sided t test of continuous variables; P values for all other variables reflect 2 test of categorical data.
No. observations	204	46,629
No. cases in teaching hospitals	51	3,658
No. cases in non‐teaching hospitals	153	42,789
Age, y	63.9 15.0	67.3 15.7	0.06
Male	30.7	44.6	<0.01
Hospital mortality, %	56.9	62.8	0.08
Comorbidities
No. Charlson‐Deyo comorbidities	2.5 2.0	2.5 2.0	0.89
Coronary artery disease, %	48.0	38.0	<0.01
Acute myocardial infarction, %	42.6	28.9	<0.01
Congestive heart failure, %	27.9	35.3	0.03
Hypertension, %	36.3	33.2	0.83
Acute renal failure, %	33.3	26.6	0.03
Diabetes mellitus, %	30.9	23.0	<0.01
Chronic obstructive pulmonary disease, %	10.3	19.3	<0.01

Figures 1 and 2 provide additional aggregate statistics on therapeutic hypothermia in California hospitals. Figure 1 plots the number of therapeutic hypothermia cases recorded in the administrative discharge registry between 1999 and 2008. Of the 204 total cases identified during this period, 178 (87.3%) were performed between 2006 and 2008. Figure 2 shows the distribution of TH cases across centers that performed therapeutic hypothermia (n = 47 hospitals, 11.3 % of all hospitals). Ten centers accounted for 124/204 (60.7%) of the total patients treated with TH after cardiac arrest; the top 3 centers accounted for 64 (31.4%) of the treated patients. Twenty‐seven hospitals were identified as performing therapeutic hypothermia on only 1 or 2 patients between 1999 and 2008.

Figure 1

Figure 2