The hospitalist movement and increasingly stringent resident work hour restrictions have led to the utilization of shift work in many hospitals.1 Use of nocturnist and night float systems, while often necessary, results in increased patient hand‐offs. Research suggests that hand‐offs in the inpatient setting can adversely affect patient outcomes as lack of continuity may increase the possibility of medical error.2, 3 In 2001, Bell et al.4 found that mortality was higher among patients admitted on weekends as compared to weekdays. Uneven staffing, lack of supervision, and fragmented care were cited as potential contributing factors.4 Similarly, Peberdy et al.5 in 2008 revealed that patients were less likely to survive a cardiac arrest if it occurred at night or on weekends, again attributed in part to fragmented patient care and understaffing.

The results of these studies raise concerns as to whether increased reliance on shift work and resulting handoffs compromises patient care.6, 7 The aim of this study was to evaluate the potential association between night admission and hospitalization‐relevant outcomes (length of stay [LOS], hospital charges, intensive care unit [ICU] transfer during hospitalization, repeat emergency department [ED] visit within 30 days of discharge, readmission within 30 days of discharge, and poor outcome [transfer to the ICU, cardiac arrest, or death] within the first 24 hours of admission) at an institution that exclusively uses nocturnists (night‐shift based hospitalists) and a resident night float system for patients admitted at night to the general medicine service. A secondary aim was to determine the potential association between weekend admission and hospitalization‐relevant outcomes.

Methods

Results

We reviewed 857 records. After excluding 33 records lacking administrative data regarding gender, race and ethnicity, and other demographic variables, there were 824 medical records available for analysis. We reviewed a similar number of records from each time period: 274 from January 2008, 265 from April 2008, and 285 from July 2008. A total of 345 (42%) patients were admitted during the day, and 479 (58%) at night; 641 (78%) were admitted on weekdays, and 183 (22%) on weekends. The 33 excluded charts were similar to the included charts for both time of admission and outcomes. Results for parametric testing and nonparametric testing, as well as for log‐transformation and non‐log‐transformation of the continuous outcomes were similar in both magnitude and statistical significance, so we present the parametric and nonlog‐transformed results below for ease of interpretation.

Interrater reliability among the 3 reviewers was very high. There were no disagreements among the 20 multiple reviews for either poor outcomes within 24 hours of admission or admitting service; the interclass correlation coefficients for 30 day repeat ED visit and 30 day readmission were 0.97 and 0.87, respectively.

Patients admitted at night or on the weekend were similar to patients admitted during the day and week across age, gender, insurance class, MDC, and Charlson Comorbidity Index (Table 1). For unadjusted outcomes, patients admitted at night has a similar LOS, hospital charges, 30 day repeat ED visits, 30 day readmissions, and poor outcome within 24 hours of admission as those patients admitted during the day. They had a potentially lower chance of any ICU transfer during hospitalization though this did not reach statistical significance at P < 0.05 (night admission 6%, day admission 3%, P = 0.06) (Table 2).

Patients admitted to the hospital during the weekend were similar to patients admitted during the week for unadjusted LOS, 30 day repeat ED visit or readmission rate, and poor outcomes within 24 hours of admission as those admitted during the week; however, they had lower hospital charges (weekend admission $22,700, weekday admission $27,200; P = 0.02), and a lower chance of ICU transfer during hospitalization (weekend admission 1%, weekday admission 5%; P = 0.02) (Table 2).

In the multivariable linear and logistic regression models (Tables 3 and 4), we assessed the independent association between night admission or weekend admission and each hospitalization‐relevant outcome except for poor outcome within 24 hours of admission (poor outcome within 24 hours of admission was not modeled to avoid the risk of overfitting because there were only 13 total events). After adjustment for age, gender, race and ethnicity, admitting service (hospitalist or teaching), Medicaid or self‐pay insurance, MDC, and Charlson Comorbidity Index, there was no statistically significant association between night admission and worse outcomes for LOS, hospital charges, 30 day repeat ED visit, or 30 day readmission. Night admission was associated with a decreased chance of ICU transfer during hospitalization, but the difference was not statistically significant (odds ratio, 0.54; 95% confidence interval [CI], 0.26‐1.11, P = 0.09). Weekend admission was not associated with worse outcomes for LOS or 30 day repeat ED visit or readmission; however, weekend admission was associated with a decrease in overall charges ($4400; 95% CI, $8300 to $600) and a decreased chance of ICU transfer during hospitalization (odds ratio, 0.20; 95% CI, 0.050.88).

Our multivariate models explained very little of the variance in patient outcomes. For LOS and hospital charges, adjusted R² values were 0.06 and 0.05, respectively. For ICU transfer during hospitalization, 30 day repeat ED visit, and 30 day readmission, the areas under the receiver operator curves were 0.75, 0.51, and 0.61 respectively.

To assess the robustness of our conclusions regarding night admission, we redefined night to include only patients admitted between the hours of 8 pm and 5:59 am. This did not change our conclusions. We also tested for interaction between night admission and weekend admission for all outcomes to assess whether night admissions on the weekend were in fact at increased risk of worse outcomes; we found no evidence of interaction (P > 0.3 for the interaction terms in each model).

Discussion

Among patients admitted to the medicine services at our academic medical center, night or weekend admission was not associated with worse hospitalization‐relevant outcomes. In some cases, night or weekend admission was associated with better outcomes, particularly in terms of ICU transfer during hospitalization and hospital charges. Prior research indicates worse outcomes during off‐hours,5 but we did not replicate this finding in our study.

The finding that admission at night was not associated with worse outcomes, particularly proximal outcomes such as LOS or ICU transfer during hospitalization, was surprising, though reassuring in view of the fact that more than half of our patients are admitted at night. We believe a few factors may be responsible. First, our general medicine service is staffed during the night (7 pm to 7 am) by in‐house nocturnists and night float residents. Second, our staffing ratio, while lower at night than during the day, remains the same on weekends and may be higher than in other settings. In continuously well‐staffed settings such as the ED12 and ICU,13 night and weekend admissions are only inconsistently associated with worse outcomes, which may be the same phenomena we observed in the current study. Third, the hospital used as the site of this study has received Nursing Magnet recognition and numerous quality awards such as the National Research Corporation's Consumer Choice Award and recognition as a Distinguished Hospital for Clinical Excellence by HealthGrades. Fourth, our integrated electronic medical record, computerized physician order entry system, and automatically generated sign out serve as complements to the morning hand off. Fifth, hospitalists and teaching teams rotate on a weekly, biweekly, or every 4 week basis, which may protect against discontinuity associated with the weekend. We believe that all of these factors may facilitate alert, comprehensive care during the night and weekend as well as safe and efficient transfer of patients from the night to the day providers.

We were also surprised by the association between weekend admission and lower charges and a lower chance of ICU transfer during hospitalization. We believe many of the same factors noted above may have played a role in these findings. In terms of hospital charges, it is possible that some workups were completed outside of the hospital rather than during the hospitalization, and that some tests were not ordered at all due to unavailability on weekends. The decreased chance of ICU transfer is unexplained. We hypothesize that there may have been a more conservative admission strategy within the ED, such that patients with high baseline severity were admitted directly to the ICU on the weekend rather than being admitted first to the general medicine floor. This hypothesis requires further study.

Our study had important limitations. It was a retrospective study from a single academic hospital. The sample size lacked sufficient power to detect differences in the low frequency of certain outcomes such as poor outcomes within 24 hours of admission (2% vs. 1%), and also for more frequent outcomes such as 30 day readmission; it is possible that with a larger sample there would have been statistically significant differences. Further, we recognize that the Charlson Comorbidity Index, which was developed to predict 1‐year mortality for medicine service patients, does not adjust for severity of illness at presentation, particularly for outcomes such as readmission. If patients admitted at night and during the weekend were less acutely ill despite having similar comorbidities and MDCs at admission, true associations between time of admission and worse outcomes could have been masked. Furthermore, the multivariable modeling explained very little of the variance in patient outcomes such that significant unmeasured confounding may still be present, and consequently our results cannot be interpreted in a causal way. Data was collected from electronic records, so it is possible that some adverse events were not recorded. However, it seems unlikely that major events such as death and transfer to an ICU would have been missed.

Several aspects of the study strengthen our confidence in the findings, including a large sample size, relevance of the outcomes, the adjustment for confounders, and an assessment for robustness of the conclusions based on restricting the definition of night and also testing for interaction between night and weekend admission. Our patient demographics and insurance mix resemble that of other academic hospitals,10 and perhaps our results may be generalizable to these settings, if not to non‐urban or community hospitals. Furthermore, the Charlson Comorbidity Index was associated with all 5 of the modeled outcomes we chose for our study, reaffirming their utility in assessing the quality of hospital care. Future directions for investigation may include examining the association of night admission with hospitalization‐relevant outcomes in nonacademic, nonurban settings, and examining whether the lack of association between night and weekend admission and worse outcomes persists with adjustment for initial severity of illness.

In summary, at a large, well‐staffed urban academic hospital, day or time of admission were not associated with worse hospitalization‐relevant outcomes. The use of nocturnists and night float teams for night admissions and continuity across weekends appears to be a safe approach to handling the increased volume of patients admitted at night, and a viable alternative to overnight call in the era of work hour restrictions.

The hospitalist movement and increasingly stringent resident work hour restrictions have led to the utilization of shift work in many hospitals.1 Use of nocturnist and night float systems, while often necessary, results in increased patient hand‐offs. Research suggests that hand‐offs in the inpatient setting can adversely affect patient outcomes as lack of continuity may increase the possibility of medical error.2, 3 In 2001, Bell et al.4 found that mortality was higher among patients admitted on weekends as compared to weekdays. Uneven staffing, lack of supervision, and fragmented care were cited as potential contributing factors.4 Similarly, Peberdy et al.5 in 2008 revealed that patients were less likely to survive a cardiac arrest if it occurred at night or on weekends, again attributed in part to fragmented patient care and understaffing.

The results of these studies raise concerns as to whether increased reliance on shift work and resulting handoffs compromises patient care.6, 7 The aim of this study was to evaluate the potential association between night admission and hospitalization‐relevant outcomes (length of stay [LOS], hospital charges, intensive care unit [ICU] transfer during hospitalization, repeat emergency department [ED] visit within 30 days of discharge, readmission within 30 days of discharge, and poor outcome [transfer to the ICU, cardiac arrest, or death] within the first 24 hours of admission) at an institution that exclusively uses nocturnists (night‐shift based hospitalists) and a resident night float system for patients admitted at night to the general medicine service. A secondary aim was to determine the potential association between weekend admission and hospitalization‐relevant outcomes.

Methods

Results

We reviewed 857 records. After excluding 33 records lacking administrative data regarding gender, race and ethnicity, and other demographic variables, there were 824 medical records available for analysis. We reviewed a similar number of records from each time period: 274 from January 2008, 265 from April 2008, and 285 from July 2008. A total of 345 (42%) patients were admitted during the day, and 479 (58%) at night; 641 (78%) were admitted on weekdays, and 183 (22%) on weekends. The 33 excluded charts were similar to the included charts for both time of admission and outcomes. Results for parametric testing and nonparametric testing, as well as for log‐transformation and non‐log‐transformation of the continuous outcomes were similar in both magnitude and statistical significance, so we present the parametric and nonlog‐transformed results below for ease of interpretation.

Interrater reliability among the 3 reviewers was very high. There were no disagreements among the 20 multiple reviews for either poor outcomes within 24 hours of admission or admitting service; the interclass correlation coefficients for 30 day repeat ED visit and 30 day readmission were 0.97 and 0.87, respectively.

Patients admitted at night or on the weekend were similar to patients admitted during the day and week across age, gender, insurance class, MDC, and Charlson Comorbidity Index (Table 1). For unadjusted outcomes, patients admitted at night has a similar LOS, hospital charges, 30 day repeat ED visits, 30 day readmissions, and poor outcome within 24 hours of admission as those patients admitted during the day. They had a potentially lower chance of any ICU transfer during hospitalization though this did not reach statistical significance at P < 0.05 (night admission 6%, day admission 3%, P = 0.06) (Table 2).

Patients admitted to the hospital during the weekend were similar to patients admitted during the week for unadjusted LOS, 30 day repeat ED visit or readmission rate, and poor outcomes within 24 hours of admission as those admitted during the week; however, they had lower hospital charges (weekend admission $22,700, weekday admission $27,200; P = 0.02), and a lower chance of ICU transfer during hospitalization (weekend admission 1%, weekday admission 5%; P = 0.02) (Table 2).

In the multivariable linear and logistic regression models (Tables 3 and 4), we assessed the independent association between night admission or weekend admission and each hospitalization‐relevant outcome except for poor outcome within 24 hours of admission (poor outcome within 24 hours of admission was not modeled to avoid the risk of overfitting because there were only 13 total events). After adjustment for age, gender, race and ethnicity, admitting service (hospitalist or teaching), Medicaid or self‐pay insurance, MDC, and Charlson Comorbidity Index, there was no statistically significant association between night admission and worse outcomes for LOS, hospital charges, 30 day repeat ED visit, or 30 day readmission. Night admission was associated with a decreased chance of ICU transfer during hospitalization, but the difference was not statistically significant (odds ratio, 0.54; 95% confidence interval [CI], 0.26‐1.11, P = 0.09). Weekend admission was not associated with worse outcomes for LOS or 30 day repeat ED visit or readmission; however, weekend admission was associated with a decrease in overall charges ($4400; 95% CI, $8300 to $600) and a decreased chance of ICU transfer during hospitalization (odds ratio, 0.20; 95% CI, 0.050.88).

Our multivariate models explained very little of the variance in patient outcomes. For LOS and hospital charges, adjusted R² values were 0.06 and 0.05, respectively. For ICU transfer during hospitalization, 30 day repeat ED visit, and 30 day readmission, the areas under the receiver operator curves were 0.75, 0.51, and 0.61 respectively.

To assess the robustness of our conclusions regarding night admission, we redefined night to include only patients admitted between the hours of 8 pm and 5:59 am. This did not change our conclusions. We also tested for interaction between night admission and weekend admission for all outcomes to assess whether night admissions on the weekend were in fact at increased risk of worse outcomes; we found no evidence of interaction (P > 0.3 for the interaction terms in each model).

Discussion

Among patients admitted to the medicine services at our academic medical center, night or weekend admission was not associated with worse hospitalization‐relevant outcomes. In some cases, night or weekend admission was associated with better outcomes, particularly in terms of ICU transfer during hospitalization and hospital charges. Prior research indicates worse outcomes during off‐hours,5 but we did not replicate this finding in our study.

The finding that admission at night was not associated with worse outcomes, particularly proximal outcomes such as LOS or ICU transfer during hospitalization, was surprising, though reassuring in view of the fact that more than half of our patients are admitted at night. We believe a few factors may be responsible. First, our general medicine service is staffed during the night (7 pm to 7 am) by in‐house nocturnists and night float residents. Second, our staffing ratio, while lower at night than during the day, remains the same on weekends and may be higher than in other settings. In continuously well‐staffed settings such as the ED12 and ICU,13 night and weekend admissions are only inconsistently associated with worse outcomes, which may be the same phenomena we observed in the current study. Third, the hospital used as the site of this study has received Nursing Magnet recognition and numerous quality awards such as the National Research Corporation's Consumer Choice Award and recognition as a Distinguished Hospital for Clinical Excellence by HealthGrades. Fourth, our integrated electronic medical record, computerized physician order entry system, and automatically generated sign out serve as complements to the morning hand off. Fifth, hospitalists and teaching teams rotate on a weekly, biweekly, or every 4 week basis, which may protect against discontinuity associated with the weekend. We believe that all of these factors may facilitate alert, comprehensive care during the night and weekend as well as safe and efficient transfer of patients from the night to the day providers.

We were also surprised by the association between weekend admission and lower charges and a lower chance of ICU transfer during hospitalization. We believe many of the same factors noted above may have played a role in these findings. In terms of hospital charges, it is possible that some workups were completed outside of the hospital rather than during the hospitalization, and that some tests were not ordered at all due to unavailability on weekends. The decreased chance of ICU transfer is unexplained. We hypothesize that there may have been a more conservative admission strategy within the ED, such that patients with high baseline severity were admitted directly to the ICU on the weekend rather than being admitted first to the general medicine floor. This hypothesis requires further study.

Our study had important limitations. It was a retrospective study from a single academic hospital. The sample size lacked sufficient power to detect differences in the low frequency of certain outcomes such as poor outcomes within 24 hours of admission (2% vs. 1%), and also for more frequent outcomes such as 30 day readmission; it is possible that with a larger sample there would have been statistically significant differences. Further, we recognize that the Charlson Comorbidity Index, which was developed to predict 1‐year mortality for medicine service patients, does not adjust for severity of illness at presentation, particularly for outcomes such as readmission. If patients admitted at night and during the weekend were less acutely ill despite having similar comorbidities and MDCs at admission, true associations between time of admission and worse outcomes could have been masked. Furthermore, the multivariable modeling explained very little of the variance in patient outcomes such that significant unmeasured confounding may still be present, and consequently our results cannot be interpreted in a causal way. Data was collected from electronic records, so it is possible that some adverse events were not recorded. However, it seems unlikely that major events such as death and transfer to an ICU would have been missed.

Several aspects of the study strengthen our confidence in the findings, including a large sample size, relevance of the outcomes, the adjustment for confounders, and an assessment for robustness of the conclusions based on restricting the definition of night and also testing for interaction between night and weekend admission. Our patient demographics and insurance mix resemble that of other academic hospitals,10 and perhaps our results may be generalizable to these settings, if not to non‐urban or community hospitals. Furthermore, the Charlson Comorbidity Index was associated with all 5 of the modeled outcomes we chose for our study, reaffirming their utility in assessing the quality of hospital care. Future directions for investigation may include examining the association of night admission with hospitalization‐relevant outcomes in nonacademic, nonurban settings, and examining whether the lack of association between night and weekend admission and worse outcomes persists with adjustment for initial severity of illness.

In summary, at a large, well‐staffed urban academic hospital, day or time of admission were not associated with worse hospitalization‐relevant outcomes. The use of nocturnists and night float teams for night admissions and continuity across weekends appears to be a safe approach to handling the increased volume of patients admitted at night, and a viable alternative to overnight call in the era of work hour restrictions.

The hospitalist movement and increasingly stringent resident work hour restrictions have led to the utilization of shift work in many hospitals.1 Use of nocturnist and night float systems, while often necessary, results in increased patient hand‐offs. Research suggests that hand‐offs in the inpatient setting can adversely affect patient outcomes as lack of continuity may increase the possibility of medical error.2, 3 In 2001, Bell et al.4 found that mortality was higher among patients admitted on weekends as compared to weekdays. Uneven staffing, lack of supervision, and fragmented care were cited as potential contributing factors.4 Similarly, Peberdy et al.5 in 2008 revealed that patients were less likely to survive a cardiac arrest if it occurred at night or on weekends, again attributed in part to fragmented patient care and understaffing.

The results of these studies raise concerns as to whether increased reliance on shift work and resulting handoffs compromises patient care.6, 7 The aim of this study was to evaluate the potential association between night admission and hospitalization‐relevant outcomes (length of stay [LOS], hospital charges, intensive care unit [ICU] transfer during hospitalization, repeat emergency department [ED] visit within 30 days of discharge, readmission within 30 days of discharge, and poor outcome [transfer to the ICU, cardiac arrest, or death] within the first 24 hours of admission) at an institution that exclusively uses nocturnists (night‐shift based hospitalists) and a resident night float system for patients admitted at night to the general medicine service. A secondary aim was to determine the potential association between weekend admission and hospitalization‐relevant outcomes.

Methods

Results

We reviewed 857 records. After excluding 33 records lacking administrative data regarding gender, race and ethnicity, and other demographic variables, there were 824 medical records available for analysis. We reviewed a similar number of records from each time period: 274 from January 2008, 265 from April 2008, and 285 from July 2008. A total of 345 (42%) patients were admitted during the day, and 479 (58%) at night; 641 (78%) were admitted on weekdays, and 183 (22%) on weekends. The 33 excluded charts were similar to the included charts for both time of admission and outcomes. Results for parametric testing and nonparametric testing, as well as for log‐transformation and non‐log‐transformation of the continuous outcomes were similar in both magnitude and statistical significance, so we present the parametric and nonlog‐transformed results below for ease of interpretation.

Interrater reliability among the 3 reviewers was very high. There were no disagreements among the 20 multiple reviews for either poor outcomes within 24 hours of admission or admitting service; the interclass correlation coefficients for 30 day repeat ED visit and 30 day readmission were 0.97 and 0.87, respectively.

Patients admitted at night or on the weekend were similar to patients admitted during the day and week across age, gender, insurance class, MDC, and Charlson Comorbidity Index (Table 1). For unadjusted outcomes, patients admitted at night has a similar LOS, hospital charges, 30 day repeat ED visits, 30 day readmissions, and poor outcome within 24 hours of admission as those patients admitted during the day. They had a potentially lower chance of any ICU transfer during hospitalization though this did not reach statistical significance at P < 0.05 (night admission 6%, day admission 3%, P = 0.06) (Table 2).

Patients admitted to the hospital during the weekend were similar to patients admitted during the week for unadjusted LOS, 30 day repeat ED visit or readmission rate, and poor outcomes within 24 hours of admission as those admitted during the week; however, they had lower hospital charges (weekend admission $22,700, weekday admission $27,200; P = 0.02), and a lower chance of ICU transfer during hospitalization (weekend admission 1%, weekday admission 5%; P = 0.02) (Table 2).

In the multivariable linear and logistic regression models (Tables 3 and 4), we assessed the independent association between night admission or weekend admission and each hospitalization‐relevant outcome except for poor outcome within 24 hours of admission (poor outcome within 24 hours of admission was not modeled to avoid the risk of overfitting because there were only 13 total events). After adjustment for age, gender, race and ethnicity, admitting service (hospitalist or teaching), Medicaid or self‐pay insurance, MDC, and Charlson Comorbidity Index, there was no statistically significant association between night admission and worse outcomes for LOS, hospital charges, 30 day repeat ED visit, or 30 day readmission. Night admission was associated with a decreased chance of ICU transfer during hospitalization, but the difference was not statistically significant (odds ratio, 0.54; 95% confidence interval [CI], 0.26‐1.11, P = 0.09). Weekend admission was not associated with worse outcomes for LOS or 30 day repeat ED visit or readmission; however, weekend admission was associated with a decrease in overall charges ($4400; 95% CI, $8300 to $600) and a decreased chance of ICU transfer during hospitalization (odds ratio, 0.20; 95% CI, 0.050.88).

Our multivariate models explained very little of the variance in patient outcomes. For LOS and hospital charges, adjusted R² values were 0.06 and 0.05, respectively. For ICU transfer during hospitalization, 30 day repeat ED visit, and 30 day readmission, the areas under the receiver operator curves were 0.75, 0.51, and 0.61 respectively.

To assess the robustness of our conclusions regarding night admission, we redefined night to include only patients admitted between the hours of 8 pm and 5:59 am. This did not change our conclusions. We also tested for interaction between night admission and weekend admission for all outcomes to assess whether night admissions on the weekend were in fact at increased risk of worse outcomes; we found no evidence of interaction (P > 0.3 for the interaction terms in each model).

Discussion

Among patients admitted to the medicine services at our academic medical center, night or weekend admission was not associated with worse hospitalization‐relevant outcomes. In some cases, night or weekend admission was associated with better outcomes, particularly in terms of ICU transfer during hospitalization and hospital charges. Prior research indicates worse outcomes during off‐hours,5 but we did not replicate this finding in our study.

The finding that admission at night was not associated with worse outcomes, particularly proximal outcomes such as LOS or ICU transfer during hospitalization, was surprising, though reassuring in view of the fact that more than half of our patients are admitted at night. We believe a few factors may be responsible. First, our general medicine service is staffed during the night (7 pm to 7 am) by in‐house nocturnists and night float residents. Second, our staffing ratio, while lower at night than during the day, remains the same on weekends and may be higher than in other settings. In continuously well‐staffed settings such as the ED12 and ICU,13 night and weekend admissions are only inconsistently associated with worse outcomes, which may be the same phenomena we observed in the current study. Third, the hospital used as the site of this study has received Nursing Magnet recognition and numerous quality awards such as the National Research Corporation's Consumer Choice Award and recognition as a Distinguished Hospital for Clinical Excellence by HealthGrades. Fourth, our integrated electronic medical record, computerized physician order entry system, and automatically generated sign out serve as complements to the morning hand off. Fifth, hospitalists and teaching teams rotate on a weekly, biweekly, or every 4 week basis, which may protect against discontinuity associated with the weekend. We believe that all of these factors may facilitate alert, comprehensive care during the night and weekend as well as safe and efficient transfer of patients from the night to the day providers.

We were also surprised by the association between weekend admission and lower charges and a lower chance of ICU transfer during hospitalization. We believe many of the same factors noted above may have played a role in these findings. In terms of hospital charges, it is possible that some workups were completed outside of the hospital rather than during the hospitalization, and that some tests were not ordered at all due to unavailability on weekends. The decreased chance of ICU transfer is unexplained. We hypothesize that there may have been a more conservative admission strategy within the ED, such that patients with high baseline severity were admitted directly to the ICU on the weekend rather than being admitted first to the general medicine floor. This hypothesis requires further study.

Our study had important limitations. It was a retrospective study from a single academic hospital. The sample size lacked sufficient power to detect differences in the low frequency of certain outcomes such as poor outcomes within 24 hours of admission (2% vs. 1%), and also for more frequent outcomes such as 30 day readmission; it is possible that with a larger sample there would have been statistically significant differences. Further, we recognize that the Charlson Comorbidity Index, which was developed to predict 1‐year mortality for medicine service patients, does not adjust for severity of illness at presentation, particularly for outcomes such as readmission. If patients admitted at night and during the weekend were less acutely ill despite having similar comorbidities and MDCs at admission, true associations between time of admission and worse outcomes could have been masked. Furthermore, the multivariable modeling explained very little of the variance in patient outcomes such that significant unmeasured confounding may still be present, and consequently our results cannot be interpreted in a causal way. Data was collected from electronic records, so it is possible that some adverse events were not recorded. However, it seems unlikely that major events such as death and transfer to an ICU would have been missed.

Several aspects of the study strengthen our confidence in the findings, including a large sample size, relevance of the outcomes, the adjustment for confounders, and an assessment for robustness of the conclusions based on restricting the definition of night and also testing for interaction between night and weekend admission. Our patient demographics and insurance mix resemble that of other academic hospitals,10 and perhaps our results may be generalizable to these settings, if not to non‐urban or community hospitals. Furthermore, the Charlson Comorbidity Index was associated with all 5 of the modeled outcomes we chose for our study, reaffirming their utility in assessing the quality of hospital care. Future directions for investigation may include examining the association of night admission with hospitalization‐relevant outcomes in nonacademic, nonurban settings, and examining whether the lack of association between night and weekend admission and worse outcomes persists with adjustment for initial severity of illness.

In summary, at a large, well‐staffed urban academic hospital, day or time of admission were not associated with worse hospitalization‐relevant outcomes. The use of nocturnists and night float teams for night admissions and continuity across weekends appears to be a safe approach to handling the increased volume of patients admitted at night, and a viable alternative to overnight call in the era of work hour restrictions.

Hospitalization can be considered a teachable moment for smoking cessation13 for the 6.5 million adult smokers who are hospitalized in the United States each year.4 Smokers who receive tobacco treatment during hospitalization and outpatient follow‐up treatment for at least 1 month are more likely to quit than patients who receive no treatment.5, 6

Unless tobacco treatment is explicitly delegated to other providers, physicians shoulder the responsibility of encouraging smokers to quit and prescribing smoking cessation medications. This is problematic in that physicians sometimes fail to counsel their patients about quitting smoking7, 8 or recommend outpatient follow‐up.9 Few hospitals provide comprehensive treatment. In a review of 33 studies on the prevalence of smoking care delivery in hospitals, 3 hospitals reported they provided advice to quit alone, 29 provided advice plus counseling and assistance in quitting, and 8 provided advice or prescription for cessation pharmacotherapy.9 Although post‐discharge support is a key component of effective treatment for hospitalized smokers,6 only 11 reported providing follow‐up treatment, or referral for follow‐up treatment, after discharge. Among these 11 hospitals, respondents reported they provided referral or follow‐up to 1% to 74% of their smokers, with a median percentage of 24%. The 1 study that specified the type of outpatient treatment provided reported the hospital provided the state quitline number to smokers.

Instituting a dedicated smoking cessation program may enhance inpatient treatment, outpatient follow‐up, and treatment outcomes. Two studies have found that institutional smoking cessation programs increased the likelihood that patients would receive treatment and quit compared to hospitals without dedicated programs.10, 11

Although many US hospitals are developing programs to provide systematic treatment for tobacco dependence,9 little is known regarding how programs structure their staff, enroll patients, or provide treatment to patients that smoke. Instituting tobacco treatment services usually requires policy change and system‐wide approaches with quality improvement endpoint goals.8, 1214 In the United States, elements of these services include: 1) developing a cadre of trained tobacco treatment specialists, 2) implementing hospital systems for identifying smokers and referring them to the service, 3) providing inpatient treatment based on current treatment guidelines15 and 4) providing or facilitating follow‐up treatment after discharge, often via fax‐referral to tobacco quitlines. This systematic approach is still lacking in many hospitals.

To date, few evaluations of dedicated hospital‐based smoking cessation programs have been reported in the literature.8, 11 The purpose of this study is to describe patient characteristics and outcomes of a dedicated tobacco treatment service, with paid staff, in a large academic medical center. We describe treatment protocols, profile patients served, treatments provided, and summarization of 6‐month post‐discharge outcomes for smokers referred to the UKanQuit service over a 1‐year period. We close with lessons learned on how to improve the delivery of tobacco treatment to hospitalized patients.

Methods

Program Intervention

UKanQuit staff visit patients at the bedside to deliver tobacco treatment. This consists of: (a) assessing withdrawal; (b) working with the health care team to adjust nicotine replacement to keep the patient comfortable; (c) assessing patients' interest in quitting smoking; (d) providing brief motivational intervention to patients not interested in quitting; and (e) providing assistance in quitting (developing a quit plan, arranging for medications on discharge) to patients interested in quitting (Figure 1). UKanQuit staff recommend medications based on the patients' level of dependence, history of cessation, and cessation medication preferences. The recommendation is communicated in person and by chart documentation to the medical team, usually by the nursing staff. The patients' resident or attending physician makes the final determination regarding medication provided. The hospital has nicotine replacement therapy (NRT; patch, gum, and lozenge), bupropion, and varenicline in its formulary. Patients are then offered an option of fax referral to the state tobacco quitline for follow‐up counseling. UKanQuit staff documents the services provided in the EMR via SOAR (Subjective, Objective, Assessment and Referral) notes.

Figure 1

Results

Baseline

Within the study period (September 1, 2007 to August 31, 2008), 22,624 patients were admitted to the medical center (Figure 2). A total of 4150 were current smokers (ie, smoked within the past 30 days). UKanQuit staff met with 513 (68%) of 753 patients referred to the service. Some of the reasons why 32% of referred patients were not seen by the UKanQuit staff have been described in our previous paper.17 These include patient was asleep, doctor in the room, out of bed for procedure, and unable to speak. Table 1 displays the characteristics of 513 smokers treated by UKanQuit from September 1, 2007 to August 31, 2008. Patients were predominantly white (74%) with mean age of 50 years. Slightly more than half of smokers were male (57%). They had smoked an average of 18 cigarettes per day for a mean duration of 29 years, and over half (58%) smoked within 5 minutes of waking suggesting a high level of dependence. On a scale of 1 to 10 the mean interest in quitting was 7.9 (SD 2.9) and the mean craving score on a scale of 0 to 4 was 1.2 (SD 1.4) suggesting slight to mild craving.

Figure 2

Table 1.Demographic, Smoking Characteristics and Treatment Provided to 513 Patients Seen by UKanQuit Service From Sept 1, 2007 to Aug 31, 2008

Characteristics	Treated (n = 513)
NOTE: For each variable, subsamples were slightly different from total sample due to missing data. Missing data were not included in the analysis. Abbreviations: AA, Afro American; SD, standard deviation. * Interest in quitting range from 0 to 10. craving range from 0 to 4.
Demographics
Mean age (SD), years	50.2 (13.6)
Male, n (%)	291 (56.7)
Ethnicity, n (%)
White	371 (73.6)
AA	107 (21.2)
Latino	18 (3.6)
Other	8 (1.6)
Referral source, n (%)
Nursing profile	477 (94.1)
Physician	5 (1.0)
Other	25 (4.9)
Smoking characteristics
Mean number of years smoked (SD)	28.9 (14.6)
Smokes within 5 minutes of waking n (%)	270 (58.3)
Mean cigarettes smoked per day (SD)	18.4 (12.6)
Mean interest in quitting (SD)*	7.9 (2.9)
Mean craving (SD)	1.2 (1.4)
Tobacco treatment provided
Counseling
Average time spent with patients (SD)	19.9 (9.1)
Received information packet, n (%)	490 (97.4)
Set goals for quitting, n (%)	352 (73.3)
Had quit plan, n (%)	151 (33.2)
Accepted fax referral to quitline, n (%)	277 (55.8)
Opted for UKanQuit counseling, n (%)	29 (5.9)
Medication
On smoking cessation medication, n (%)	133 (26.2)
Interested in receiving or changing smoking cessation medication, n (%)	132 (26.7)
Added or changed smoking cessation medication, n (%)	195 (40.5)
Discharge med, n (%)	196 (40.2)

Discussion

Among patients served by this inpatient program, interest in quitting was high but administration of inpatient medications upon admission was low (26%). Nearly all patients were provided with written materials, a majority set some form of goal for quitting or cutting down, and many developed quit plans and received assistance adjusting inpatient and/or discharge medications. After discharge, the majority of study participants made unassisted quit attempts, as utilization of medications and quitline services was suboptimal. Fax‐referral to quitline may not, on its own, fulfill guideline recommendations for post‐discharge follow‐up.

Our intent‐to‐treat quit rate was about half of what was found in Taylor et al.'s11 hospital program dissemination trial. Their program may have had better effects as it was somewhat more intensive. It included at least 1 follow‐up phone call immediately after discharge, as well as an accompanying video and relaxation audiotape or compact disc. However, differences in outcomes may also be due to large differences between the study populations. Hospitals participating in Taylor's study only conducted intervention and outcome assessment among smokers who were ready to quit, willing to enroll in a clinical trial, and willing to complete informed consent. Our intervention and outcome data included patients who agreed to speak with UKanQuit staff, regardless of readiness to quit. Our participants did not have to complete informed consent and enroll in a trial as our analyses were conducted post hoc. Our study outcomes might better reflect quit rates for a program serving all smokers, at all levels of readiness to quit, in actual hospital practices. The mean reduction in cigarette smoking among smokers who continued to smoke at 6 months' follow‐up was statistically significant. However, findings from a lung health study show that 50% or more reduction in smoking was ultimately related to successful quitting.23

Hospitalization can be considered a teachable moment for smoking cessation13 for the 6.5 million adult smokers who are hospitalized in the United States each year.4 Smokers who receive tobacco treatment during hospitalization and outpatient follow‐up treatment for at least 1 month are more likely to quit than patients who receive no treatment.5, 6

Unless tobacco treatment is explicitly delegated to other providers, physicians shoulder the responsibility of encouraging smokers to quit and prescribing smoking cessation medications. This is problematic in that physicians sometimes fail to counsel their patients about quitting smoking7, 8 or recommend outpatient follow‐up.9 Few hospitals provide comprehensive treatment. In a review of 33 studies on the prevalence of smoking care delivery in hospitals, 3 hospitals reported they provided advice to quit alone, 29 provided advice plus counseling and assistance in quitting, and 8 provided advice or prescription for cessation pharmacotherapy.9 Although post‐discharge support is a key component of effective treatment for hospitalized smokers,6 only 11 reported providing follow‐up treatment, or referral for follow‐up treatment, after discharge. Among these 11 hospitals, respondents reported they provided referral or follow‐up to 1% to 74% of their smokers, with a median percentage of 24%. The 1 study that specified the type of outpatient treatment provided reported the hospital provided the state quitline number to smokers.

Instituting a dedicated smoking cessation program may enhance inpatient treatment, outpatient follow‐up, and treatment outcomes. Two studies have found that institutional smoking cessation programs increased the likelihood that patients would receive treatment and quit compared to hospitals without dedicated programs.10, 11

Although many US hospitals are developing programs to provide systematic treatment for tobacco dependence,9 little is known regarding how programs structure their staff, enroll patients, or provide treatment to patients that smoke. Instituting tobacco treatment services usually requires policy change and system‐wide approaches with quality improvement endpoint goals.8, 1214 In the United States, elements of these services include: 1) developing a cadre of trained tobacco treatment specialists, 2) implementing hospital systems for identifying smokers and referring them to the service, 3) providing inpatient treatment based on current treatment guidelines15 and 4) providing or facilitating follow‐up treatment after discharge, often via fax‐referral to tobacco quitlines. This systematic approach is still lacking in many hospitals.

To date, few evaluations of dedicated hospital‐based smoking cessation programs have been reported in the literature.8, 11 The purpose of this study is to describe patient characteristics and outcomes of a dedicated tobacco treatment service, with paid staff, in a large academic medical center. We describe treatment protocols, profile patients served, treatments provided, and summarization of 6‐month post‐discharge outcomes for smokers referred to the UKanQuit service over a 1‐year period. We close with lessons learned on how to improve the delivery of tobacco treatment to hospitalized patients.

Methods

Program Intervention

UKanQuit staff visit patients at the bedside to deliver tobacco treatment. This consists of: (a) assessing withdrawal; (b) working with the health care team to adjust nicotine replacement to keep the patient comfortable; (c) assessing patients' interest in quitting smoking; (d) providing brief motivational intervention to patients not interested in quitting; and (e) providing assistance in quitting (developing a quit plan, arranging for medications on discharge) to patients interested in quitting (Figure 1). UKanQuit staff recommend medications based on the patients' level of dependence, history of cessation, and cessation medication preferences. The recommendation is communicated in person and by chart documentation to the medical team, usually by the nursing staff. The patients' resident or attending physician makes the final determination regarding medication provided. The hospital has nicotine replacement therapy (NRT; patch, gum, and lozenge), bupropion, and varenicline in its formulary. Patients are then offered an option of fax referral to the state tobacco quitline for follow‐up counseling. UKanQuit staff documents the services provided in the EMR via SOAR (Subjective, Objective, Assessment and Referral) notes.

Figure 1

Results

Baseline

Within the study period (September 1, 2007 to August 31, 2008), 22,624 patients were admitted to the medical center (Figure 2). A total of 4150 were current smokers (ie, smoked within the past 30 days). UKanQuit staff met with 513 (68%) of 753 patients referred to the service. Some of the reasons why 32% of referred patients were not seen by the UKanQuit staff have been described in our previous paper.17 These include patient was asleep, doctor in the room, out of bed for procedure, and unable to speak. Table 1 displays the characteristics of 513 smokers treated by UKanQuit from September 1, 2007 to August 31, 2008. Patients were predominantly white (74%) with mean age of 50 years. Slightly more than half of smokers were male (57%). They had smoked an average of 18 cigarettes per day for a mean duration of 29 years, and over half (58%) smoked within 5 minutes of waking suggesting a high level of dependence. On a scale of 1 to 10 the mean interest in quitting was 7.9 (SD 2.9) and the mean craving score on a scale of 0 to 4 was 1.2 (SD 1.4) suggesting slight to mild craving.

Figure 2

Table 1.Demographic, Smoking Characteristics and Treatment Provided to 513 Patients Seen by UKanQuit Service From Sept 1, 2007 to Aug 31, 2008

Characteristics	Treated (n = 513)
NOTE: For each variable, subsamples were slightly different from total sample due to missing data. Missing data were not included in the analysis. Abbreviations: AA, Afro American; SD, standard deviation. * Interest in quitting range from 0 to 10. craving range from 0 to 4.
Demographics
Mean age (SD), years	50.2 (13.6)
Male, n (%)	291 (56.7)
Ethnicity, n (%)
White	371 (73.6)
AA	107 (21.2)
Latino	18 (3.6)
Other	8 (1.6)
Referral source, n (%)
Nursing profile	477 (94.1)
Physician	5 (1.0)
Other	25 (4.9)
Smoking characteristics
Mean number of years smoked (SD)	28.9 (14.6)
Smokes within 5 minutes of waking n (%)	270 (58.3)
Mean cigarettes smoked per day (SD)	18.4 (12.6)
Mean interest in quitting (SD)*	7.9 (2.9)
Mean craving (SD)	1.2 (1.4)
Tobacco treatment provided
Counseling
Average time spent with patients (SD)	19.9 (9.1)
Received information packet, n (%)	490 (97.4)
Set goals for quitting, n (%)	352 (73.3)
Had quit plan, n (%)	151 (33.2)
Accepted fax referral to quitline, n (%)	277 (55.8)
Opted for UKanQuit counseling, n (%)	29 (5.9)
Medication
On smoking cessation medication, n (%)	133 (26.2)
Interested in receiving or changing smoking cessation medication, n (%)	132 (26.7)
Added or changed smoking cessation medication, n (%)	195 (40.5)
Discharge med, n (%)	196 (40.2)

Discussion

Among patients served by this inpatient program, interest in quitting was high but administration of inpatient medications upon admission was low (26%). Nearly all patients were provided with written materials, a majority set some form of goal for quitting or cutting down, and many developed quit plans and received assistance adjusting inpatient and/or discharge medications. After discharge, the majority of study participants made unassisted quit attempts, as utilization of medications and quitline services was suboptimal. Fax‐referral to quitline may not, on its own, fulfill guideline recommendations for post‐discharge follow‐up.

Our intent‐to‐treat quit rate was about half of what was found in Taylor et al.'s11 hospital program dissemination trial. Their program may have had better effects as it was somewhat more intensive. It included at least 1 follow‐up phone call immediately after discharge, as well as an accompanying video and relaxation audiotape or compact disc. However, differences in outcomes may also be due to large differences between the study populations. Hospitals participating in Taylor's study only conducted intervention and outcome assessment among smokers who were ready to quit, willing to enroll in a clinical trial, and willing to complete informed consent. Our intervention and outcome data included patients who agreed to speak with UKanQuit staff, regardless of readiness to quit. Our participants did not have to complete informed consent and enroll in a trial as our analyses were conducted post hoc. Our study outcomes might better reflect quit rates for a program serving all smokers, at all levels of readiness to quit, in actual hospital practices. The mean reduction in cigarette smoking among smokers who continued to smoke at 6 months' follow‐up was statistically significant. However, findings from a lung health study show that 50% or more reduction in smoking was ultimately related to successful quitting.23

Hospitalization can be considered a teachable moment for smoking cessation13 for the 6.5 million adult smokers who are hospitalized in the United States each year.4 Smokers who receive tobacco treatment during hospitalization and outpatient follow‐up treatment for at least 1 month are more likely to quit than patients who receive no treatment.5, 6

Unless tobacco treatment is explicitly delegated to other providers, physicians shoulder the responsibility of encouraging smokers to quit and prescribing smoking cessation medications. This is problematic in that physicians sometimes fail to counsel their patients about quitting smoking7, 8 or recommend outpatient follow‐up.9 Few hospitals provide comprehensive treatment. In a review of 33 studies on the prevalence of smoking care delivery in hospitals, 3 hospitals reported they provided advice to quit alone, 29 provided advice plus counseling and assistance in quitting, and 8 provided advice or prescription for cessation pharmacotherapy.9 Although post‐discharge support is a key component of effective treatment for hospitalized smokers,6 only 11 reported providing follow‐up treatment, or referral for follow‐up treatment, after discharge. Among these 11 hospitals, respondents reported they provided referral or follow‐up to 1% to 74% of their smokers, with a median percentage of 24%. The 1 study that specified the type of outpatient treatment provided reported the hospital provided the state quitline number to smokers.

Instituting a dedicated smoking cessation program may enhance inpatient treatment, outpatient follow‐up, and treatment outcomes. Two studies have found that institutional smoking cessation programs increased the likelihood that patients would receive treatment and quit compared to hospitals without dedicated programs.10, 11

Although many US hospitals are developing programs to provide systematic treatment for tobacco dependence,9 little is known regarding how programs structure their staff, enroll patients, or provide treatment to patients that smoke. Instituting tobacco treatment services usually requires policy change and system‐wide approaches with quality improvement endpoint goals.8, 1214 In the United States, elements of these services include: 1) developing a cadre of trained tobacco treatment specialists, 2) implementing hospital systems for identifying smokers and referring them to the service, 3) providing inpatient treatment based on current treatment guidelines15 and 4) providing or facilitating follow‐up treatment after discharge, often via fax‐referral to tobacco quitlines. This systematic approach is still lacking in many hospitals.

To date, few evaluations of dedicated hospital‐based smoking cessation programs have been reported in the literature.8, 11 The purpose of this study is to describe patient characteristics and outcomes of a dedicated tobacco treatment service, with paid staff, in a large academic medical center. We describe treatment protocols, profile patients served, treatments provided, and summarization of 6‐month post‐discharge outcomes for smokers referred to the UKanQuit service over a 1‐year period. We close with lessons learned on how to improve the delivery of tobacco treatment to hospitalized patients.

Methods

Program Intervention

UKanQuit staff visit patients at the bedside to deliver tobacco treatment. This consists of: (a) assessing withdrawal; (b) working with the health care team to adjust nicotine replacement to keep the patient comfortable; (c) assessing patients' interest in quitting smoking; (d) providing brief motivational intervention to patients not interested in quitting; and (e) providing assistance in quitting (developing a quit plan, arranging for medications on discharge) to patients interested in quitting (Figure 1). UKanQuit staff recommend medications based on the patients' level of dependence, history of cessation, and cessation medication preferences. The recommendation is communicated in person and by chart documentation to the medical team, usually by the nursing staff. The patients' resident or attending physician makes the final determination regarding medication provided. The hospital has nicotine replacement therapy (NRT; patch, gum, and lozenge), bupropion, and varenicline in its formulary. Patients are then offered an option of fax referral to the state tobacco quitline for follow‐up counseling. UKanQuit staff documents the services provided in the EMR via SOAR (Subjective, Objective, Assessment and Referral) notes.

Figure 1

Results

Baseline

Within the study period (September 1, 2007 to August 31, 2008), 22,624 patients were admitted to the medical center (Figure 2). A total of 4150 were current smokers (ie, smoked within the past 30 days). UKanQuit staff met with 513 (68%) of 753 patients referred to the service. Some of the reasons why 32% of referred patients were not seen by the UKanQuit staff have been described in our previous paper.17 These include patient was asleep, doctor in the room, out of bed for procedure, and unable to speak. Table 1 displays the characteristics of 513 smokers treated by UKanQuit from September 1, 2007 to August 31, 2008. Patients were predominantly white (74%) with mean age of 50 years. Slightly more than half of smokers were male (57%). They had smoked an average of 18 cigarettes per day for a mean duration of 29 years, and over half (58%) smoked within 5 minutes of waking suggesting a high level of dependence. On a scale of 1 to 10 the mean interest in quitting was 7.9 (SD 2.9) and the mean craving score on a scale of 0 to 4 was 1.2 (SD 1.4) suggesting slight to mild craving.

Figure 2

Table 1.Demographic, Smoking Characteristics and Treatment Provided to 513 Patients Seen by UKanQuit Service From Sept 1, 2007 to Aug 31, 2008

Characteristics	Treated (n = 513)
NOTE: For each variable, subsamples were slightly different from total sample due to missing data. Missing data were not included in the analysis. Abbreviations: AA, Afro American; SD, standard deviation. * Interest in quitting range from 0 to 10. craving range from 0 to 4.
Demographics
Mean age (SD), years	50.2 (13.6)
Male, n (%)	291 (56.7)
Ethnicity, n (%)
White	371 (73.6)
AA	107 (21.2)
Latino	18 (3.6)
Other	8 (1.6)
Referral source, n (%)
Nursing profile	477 (94.1)
Physician	5 (1.0)
Other	25 (4.9)
Smoking characteristics
Mean number of years smoked (SD)	28.9 (14.6)
Smokes within 5 minutes of waking n (%)	270 (58.3)
Mean cigarettes smoked per day (SD)	18.4 (12.6)
Mean interest in quitting (SD)*	7.9 (2.9)
Mean craving (SD)	1.2 (1.4)
Tobacco treatment provided
Counseling
Average time spent with patients (SD)	19.9 (9.1)
Received information packet, n (%)	490 (97.4)
Set goals for quitting, n (%)	352 (73.3)
Had quit plan, n (%)	151 (33.2)
Accepted fax referral to quitline, n (%)	277 (55.8)
Opted for UKanQuit counseling, n (%)	29 (5.9)
Medication
On smoking cessation medication, n (%)	133 (26.2)
Interested in receiving or changing smoking cessation medication, n (%)	132 (26.7)
Added or changed smoking cessation medication, n (%)	195 (40.5)
Discharge med, n (%)	196 (40.2)

Discussion

Among patients served by this inpatient program, interest in quitting was high but administration of inpatient medications upon admission was low (26%). Nearly all patients were provided with written materials, a majority set some form of goal for quitting or cutting down, and many developed quit plans and received assistance adjusting inpatient and/or discharge medications. After discharge, the majority of study participants made unassisted quit attempts, as utilization of medications and quitline services was suboptimal. Fax‐referral to quitline may not, on its own, fulfill guideline recommendations for post‐discharge follow‐up.

Our intent‐to‐treat quit rate was about half of what was found in Taylor et al.'s11 hospital program dissemination trial. Their program may have had better effects as it was somewhat more intensive. It included at least 1 follow‐up phone call immediately after discharge, as well as an accompanying video and relaxation audiotape or compact disc. However, differences in outcomes may also be due to large differences between the study populations. Hospitals participating in Taylor's study only conducted intervention and outcome assessment among smokers who were ready to quit, willing to enroll in a clinical trial, and willing to complete informed consent. Our intervention and outcome data included patients who agreed to speak with UKanQuit staff, regardless of readiness to quit. Our participants did not have to complete informed consent and enroll in a trial as our analyses were conducted post hoc. Our study outcomes might better reflect quit rates for a program serving all smokers, at all levels of readiness to quit, in actual hospital practices. The mean reduction in cigarette smoking among smokers who continued to smoke at 6 months' follow‐up was statistically significant. However, findings from a lung health study show that 50% or more reduction in smoking was ultimately related to successful quitting.23

The incidence of sudden pediatric cardiac or respiratory arrest is low.1 Most inpatient pediatric arrests appear to occur as progression of respiratory distress or shock.2 The outcome of inpatient pediatric cardiorespiratory arrests continues to be poor, emphasizing the need for early recognition and intervention. In 2006, as part of its 100,000 Lives Campaign the Institute for Healthcare Improvement recommended the implementation of Rapid Response Teams (RRT) as 1 of the strategies to reduce the number of preventable inpatient deaths.3 We reviewed all emergency response team (ERT) activations for last 13 years at The Children's Hospital in Denver, CO to assist in the development of a new RRT and to identify at risk populations, situations, and system processes the RRT should address.

This is a retrospective review of 13 years of data collection on ERT activations at The Children's Hospital in Denver, CO. We describe demographic and clinical variables, including outcomes of ERT activations at a free‐standing tertiary care children's hospital.

Background/Methods

The Children's Hospital (TCH) is a 270 inpatient bed tertiary care free standing children's hospital associated with University of Colorado at Denver Health Sciences Center. The current distribution of inpatient beds includes: 168 medical/surgical beds, 102 critical care beds (26 pediatric intensive care unit, 16 cardiac intensive care unit, and 60 neonatal intensive care unit). In 2006, nearly 10,000 patients were admitted for care at this hospital with an average inpatient stay of 6 days. TCH is a Level One Regional Trauma Center serving a catchment area of seven states, and a major transplant center for heart, solid organs, and bone marrow.

The history of the TCH Emergency Response Team dates back to 1990 when we first began to follow cardiorespiratory arrests in noncritical care areas of the hospital. In 1992, a Cardiac Or Respiratory event (COR) team including the most senior in‐house specialist was available 24 hours a day, 7 days a week to respond to all arrests within the hospital. The COR committee provided oversight and monitoring of the arrest events, including standardization of crash carts and the development of mock codes to ensure that the responding personnel were qualified in resuscitation practices. The COR team has evolved over the ensuing years, including a name change to the Emergency Response Team (ERT); now a single number activated by any medical staff is used to call the operator who activates the ERT via overhead paging and via code pager system. The 14 member Emergency Response Team consists of PICU/CICU/anesthesia/surgical fellows, ED attending, in‐house residents, PICU/CICU/ED charge nurse, nursing supervisor, resource RN, pharmacist, respiratory therapist, and a messenger.

A database has been maintained by 1 of the authors (DBH) since the inception of the COR/ERT at TCH 16 years ago. This is a retrospective review of this database of ERT activations. An ERT activation could have been triggered by any event that was felt to be emergent, life threatening, and/or needing immediate medical attention. After the event, a debriefing form was filled out about the event. Data collected included date, time, medical record number, location, primary care service, age, sex, primary and secondary diagnoses, and disposition. Data not captured in this database included initial rhythm, need for compressions, cardiac medications, defibrillation or intubation.

Analyses were performed on data collected from January 1993 through April 2007. Medical records of the documented ERT activations were reviewed for missing information and/or clarification of the events. Categories entered in Statistical Package for Social Statistics (SPSS) were similar to information included in the debriefing form: age, sex, admission diagnosis, precipitating event, percentage of admissions, acute vs. chronic diagnosis, winter vs. nonwinter months (October‐March/April‐September), day (6 am‐6 pm) and night (6 pm‐6 am) shifts, survival of ERT activation, survival to discharge, and primary attending service. Data were analyzed using SPSS 16.0 (2007, Chicago, IL). The study was approved by the Colorado Multiple Institutional Review Board.

Results/Conclusion

There were 1537 ERT activations in the database. A total of 203 ERT activations were eliminated from the database: 177 were eliminated from analysis because of missing age, admission diagnosis or time of day of activation, and 26 were ERT activations that had been triggered on adult visitors or adult employees. The remaining 1334 ERT activations were included for analysis.

Table 1 shows the demographics of the patients. The median age was 1.8 years, with a range of 0 to 29 years. A total of 39%(511) of all ERT activations occurred in patients under the age of 1 year with the highest incidence between 1 month and 1 year. Overall, the children at highest risk were males less than 1 year of age with a chronic diagnosis. In addition, time of day and time of year of ERT activations were analyzed as shown. There was no statistical difference between nonwinter (April‐September) and winter (October‐March) months. Statistically, there were significantly more ERT activations during day shifts (6 am‐6 pm) as compared to night shifts (P < 0.001).

The most common admission diagnosis (Table 2) and underlying chronic condition was cardiac disease; other common admission diagnoses were infectious disease, trauma, and pulmonary disease. The medical categories of admission diagnosis included congenital/metabolic (39%), gastrointestinal (29%), renal (18%), rheumatology (4%), toxicology (4%), psychiatry (3%), endocrine (2%), and allergy (1%). The surgery category of admission diagnosis included otolaryngology (63%), orthopedics (28%), urology (4%), dental (4%), and ophthalmology (1%).

Finally, the patients' survival rate after an ERT itself was to be 90% (Table 3), with an overall survival rate to discharge of 78% (Table 4). Survival rate to discharge of those patients who survived the ERT event was 87%. Two patients were missing survival event data and 137 patients were missing survival to discharge data.

Discussion

We present a retrospective review of 1334 emergency response team activations over 13 years at an academic free‐standing tertiary care children's hospital. In keeping with previous reports, we found that children less than 1 year of age were at the highest risk for activation of the emergency response team.1, 4, 5

The National Registry for CardioPulmonary Resuscitation (NRCPR) database cite respiratory failure (asphyxia) and circulatory shock (ischemia) as the most common causes of in‐hospital cardiac arrests.1 Additionally, more than half of pediatric patients that experience a cardiopulmonary arrest have an underlying chronic illness.1, 2, 4, 5, 6 These are similar to our findings that chronic pulmonary and cardiac diseases were among the most frequent admission diagnosis.

Unlike Peberdy et al.,7we did not find an increase in the number of emergency response team activations at night or on weekends. Instead, we found that an ERT activation was more likely to be requested during the day shifts (6 am‐6 pm) which is a similar to that reported by Jones et al.8 Similarly, Jones et al.8 reported that the hourly rate of their medical emergency team activation was greater during the time between 8 am and 6 pm.

Our overall survival rate (78%) to discharge after an ERT event was much higher than what has been reported by Topjian et al.9 (25%). This likely reflects our inclusion of all emergency response team activations, not just apneic and asystolic arrests. The improved survival rate may also be influenced by the 24/7 presence of pediatric fellows, residents and surgeons in our hospital, which has been associated with improved 24‐hour survival for children receiving in‐hospital cardiopulmonary resuscitation.10

There are several limitations to our data collection and this report. This was a retrospective review and as previously noted some medical details were absent resulting in the exclusion of some cases. We also found that the original debriefing form which was used as the basis for the database did not include some important clinical variable, such as vital signs, more detailed events such as rhythm, medications used, and deficits or changes in baseline function. We suggest including multiple variables in a future multicenter study of pediatric RRT's: facility, admitting service, admission diagnosis, age (chronologic and gestational), sex, how long patient has been in hospital prior to the event, past medical history, vitals signs before and after activation, time/date, location, any precipitating events and actions taken by the RRT (eg, cardiopulmonary resuscitation, defibrillation, emergent intubation, and other emergent interventions), medications, survival of event, survival to discharge, deficits or changes in baseline function after the event and to discharge. In particular, information about history of prematurity would have been helpful in assessing further risk factors. Analysis of survival with or without significant deficits or changes in baseline function would be another useful outcome measure. Figure 1 is an example of a debriefing form a multi‐institutional study or hospitalist led quality improvement project may use to collect this data.

Figure 1

We were able to identify a population of higher‐risk patients (less than 1 year of age with comorbidities and an admission diagnosis of cardiac or respiratory disease) to focus our educational efforts on earlier recognition of patient deterioration for both inpatient ward staff and RRT responders. These findings may assist in future quality assurance issues such as patient placement and early ICU admissions depending on age, chronic conditions, and/or admission diagnosis. Future directions should include multi‐center study of RRT to improve external validity. In addition, a more careful analysis of events surrounding the activation, including incorporating such tools as the Pediatric Early Warning (PEW) Score,11 may further assist hospitals and practitioners identify hospitalized children at risk for deterioration on the inpatient ward.

The incidence of sudden pediatric cardiac or respiratory arrest is low.1 Most inpatient pediatric arrests appear to occur as progression of respiratory distress or shock.2 The outcome of inpatient pediatric cardiorespiratory arrests continues to be poor, emphasizing the need for early recognition and intervention. In 2006, as part of its 100,000 Lives Campaign the Institute for Healthcare Improvement recommended the implementation of Rapid Response Teams (RRT) as 1 of the strategies to reduce the number of preventable inpatient deaths.3 We reviewed all emergency response team (ERT) activations for last 13 years at The Children's Hospital in Denver, CO to assist in the development of a new RRT and to identify at risk populations, situations, and system processes the RRT should address.

This is a retrospective review of 13 years of data collection on ERT activations at The Children's Hospital in Denver, CO. We describe demographic and clinical variables, including outcomes of ERT activations at a free‐standing tertiary care children's hospital.

Background/Methods

The Children's Hospital (TCH) is a 270 inpatient bed tertiary care free standing children's hospital associated with University of Colorado at Denver Health Sciences Center. The current distribution of inpatient beds includes: 168 medical/surgical beds, 102 critical care beds (26 pediatric intensive care unit, 16 cardiac intensive care unit, and 60 neonatal intensive care unit). In 2006, nearly 10,000 patients were admitted for care at this hospital with an average inpatient stay of 6 days. TCH is a Level One Regional Trauma Center serving a catchment area of seven states, and a major transplant center for heart, solid organs, and bone marrow.

The history of the TCH Emergency Response Team dates back to 1990 when we first began to follow cardiorespiratory arrests in noncritical care areas of the hospital. In 1992, a Cardiac Or Respiratory event (COR) team including the most senior in‐house specialist was available 24 hours a day, 7 days a week to respond to all arrests within the hospital. The COR committee provided oversight and monitoring of the arrest events, including standardization of crash carts and the development of mock codes to ensure that the responding personnel were qualified in resuscitation practices. The COR team has evolved over the ensuing years, including a name change to the Emergency Response Team (ERT); now a single number activated by any medical staff is used to call the operator who activates the ERT via overhead paging and via code pager system. The 14 member Emergency Response Team consists of PICU/CICU/anesthesia/surgical fellows, ED attending, in‐house residents, PICU/CICU/ED charge nurse, nursing supervisor, resource RN, pharmacist, respiratory therapist, and a messenger.

A database has been maintained by 1 of the authors (DBH) since the inception of the COR/ERT at TCH 16 years ago. This is a retrospective review of this database of ERT activations. An ERT activation could have been triggered by any event that was felt to be emergent, life threatening, and/or needing immediate medical attention. After the event, a debriefing form was filled out about the event. Data collected included date, time, medical record number, location, primary care service, age, sex, primary and secondary diagnoses, and disposition. Data not captured in this database included initial rhythm, need for compressions, cardiac medications, defibrillation or intubation.

Analyses were performed on data collected from January 1993 through April 2007. Medical records of the documented ERT activations were reviewed for missing information and/or clarification of the events. Categories entered in Statistical Package for Social Statistics (SPSS) were similar to information included in the debriefing form: age, sex, admission diagnosis, precipitating event, percentage of admissions, acute vs. chronic diagnosis, winter vs. nonwinter months (October‐March/April‐September), day (6 am‐6 pm) and night (6 pm‐6 am) shifts, survival of ERT activation, survival to discharge, and primary attending service. Data were analyzed using SPSS 16.0 (2007, Chicago, IL). The study was approved by the Colorado Multiple Institutional Review Board.

Results/Conclusion

There were 1537 ERT activations in the database. A total of 203 ERT activations were eliminated from the database: 177 were eliminated from analysis because of missing age, admission diagnosis or time of day of activation, and 26 were ERT activations that had been triggered on adult visitors or adult employees. The remaining 1334 ERT activations were included for analysis.

Table 1 shows the demographics of the patients. The median age was 1.8 years, with a range of 0 to 29 years. A total of 39%(511) of all ERT activations occurred in patients under the age of 1 year with the highest incidence between 1 month and 1 year. Overall, the children at highest risk were males less than 1 year of age with a chronic diagnosis. In addition, time of day and time of year of ERT activations were analyzed as shown. There was no statistical difference between nonwinter (April‐September) and winter (October‐March) months. Statistically, there were significantly more ERT activations during day shifts (6 am‐6 pm) as compared to night shifts (P < 0.001).

The most common admission diagnosis (Table 2) and underlying chronic condition was cardiac disease; other common admission diagnoses were infectious disease, trauma, and pulmonary disease. The medical categories of admission diagnosis included congenital/metabolic (39%), gastrointestinal (29%), renal (18%), rheumatology (4%), toxicology (4%), psychiatry (3%), endocrine (2%), and allergy (1%). The surgery category of admission diagnosis included otolaryngology (63%), orthopedics (28%), urology (4%), dental (4%), and ophthalmology (1%).

Finally, the patients' survival rate after an ERT itself was to be 90% (Table 3), with an overall survival rate to discharge of 78% (Table 4). Survival rate to discharge of those patients who survived the ERT event was 87%. Two patients were missing survival event data and 137 patients were missing survival to discharge data.

Discussion

We present a retrospective review of 1334 emergency response team activations over 13 years at an academic free‐standing tertiary care children's hospital. In keeping with previous reports, we found that children less than 1 year of age were at the highest risk for activation of the emergency response team.1, 4, 5

The National Registry for CardioPulmonary Resuscitation (NRCPR) database cite respiratory failure (asphyxia) and circulatory shock (ischemia) as the most common causes of in‐hospital cardiac arrests.1 Additionally, more than half of pediatric patients that experience a cardiopulmonary arrest have an underlying chronic illness.1, 2, 4, 5, 6 These are similar to our findings that chronic pulmonary and cardiac diseases were among the most frequent admission diagnosis.

Unlike Peberdy et al.,7we did not find an increase in the number of emergency response team activations at night or on weekends. Instead, we found that an ERT activation was more likely to be requested during the day shifts (6 am‐6 pm) which is a similar to that reported by Jones et al.8 Similarly, Jones et al.8 reported that the hourly rate of their medical emergency team activation was greater during the time between 8 am and 6 pm.

Our overall survival rate (78%) to discharge after an ERT event was much higher than what has been reported by Topjian et al.9 (25%). This likely reflects our inclusion of all emergency response team activations, not just apneic and asystolic arrests. The improved survival rate may also be influenced by the 24/7 presence of pediatric fellows, residents and surgeons in our hospital, which has been associated with improved 24‐hour survival for children receiving in‐hospital cardiopulmonary resuscitation.10

There are several limitations to our data collection and this report. This was a retrospective review and as previously noted some medical details were absent resulting in the exclusion of some cases. We also found that the original debriefing form which was used as the basis for the database did not include some important clinical variable, such as vital signs, more detailed events such as rhythm, medications used, and deficits or changes in baseline function. We suggest including multiple variables in a future multicenter study of pediatric RRT's: facility, admitting service, admission diagnosis, age (chronologic and gestational), sex, how long patient has been in hospital prior to the event, past medical history, vitals signs before and after activation, time/date, location, any precipitating events and actions taken by the RRT (eg, cardiopulmonary resuscitation, defibrillation, emergent intubation, and other emergent interventions), medications, survival of event, survival to discharge, deficits or changes in baseline function after the event and to discharge. In particular, information about history of prematurity would have been helpful in assessing further risk factors. Analysis of survival with or without significant deficits or changes in baseline function would be another useful outcome measure. Figure 1 is an example of a debriefing form a multi‐institutional study or hospitalist led quality improvement project may use to collect this data.

Figure 1

We were able to identify a population of higher‐risk patients (less than 1 year of age with comorbidities and an admission diagnosis of cardiac or respiratory disease) to focus our educational efforts on earlier recognition of patient deterioration for both inpatient ward staff and RRT responders. These findings may assist in future quality assurance issues such as patient placement and early ICU admissions depending on age, chronic conditions, and/or admission diagnosis. Future directions should include multi‐center study of RRT to improve external validity. In addition, a more careful analysis of events surrounding the activation, including incorporating such tools as the Pediatric Early Warning (PEW) Score,11 may further assist hospitals and practitioners identify hospitalized children at risk for deterioration on the inpatient ward.

The incidence of sudden pediatric cardiac or respiratory arrest is low.1 Most inpatient pediatric arrests appear to occur as progression of respiratory distress or shock.2 The outcome of inpatient pediatric cardiorespiratory arrests continues to be poor, emphasizing the need for early recognition and intervention. In 2006, as part of its 100,000 Lives Campaign the Institute for Healthcare Improvement recommended the implementation of Rapid Response Teams (RRT) as 1 of the strategies to reduce the number of preventable inpatient deaths.3 We reviewed all emergency response team (ERT) activations for last 13 years at The Children's Hospital in Denver, CO to assist in the development of a new RRT and to identify at risk populations, situations, and system processes the RRT should address.

This is a retrospective review of 13 years of data collection on ERT activations at The Children's Hospital in Denver, CO. We describe demographic and clinical variables, including outcomes of ERT activations at a free‐standing tertiary care children's hospital.

Background/Methods

The Children's Hospital (TCH) is a 270 inpatient bed tertiary care free standing children's hospital associated with University of Colorado at Denver Health Sciences Center. The current distribution of inpatient beds includes: 168 medical/surgical beds, 102 critical care beds (26 pediatric intensive care unit, 16 cardiac intensive care unit, and 60 neonatal intensive care unit). In 2006, nearly 10,000 patients were admitted for care at this hospital with an average inpatient stay of 6 days. TCH is a Level One Regional Trauma Center serving a catchment area of seven states, and a major transplant center for heart, solid organs, and bone marrow.

The history of the TCH Emergency Response Team dates back to 1990 when we first began to follow cardiorespiratory arrests in noncritical care areas of the hospital. In 1992, a Cardiac Or Respiratory event (COR) team including the most senior in‐house specialist was available 24 hours a day, 7 days a week to respond to all arrests within the hospital. The COR committee provided oversight and monitoring of the arrest events, including standardization of crash carts and the development of mock codes to ensure that the responding personnel were qualified in resuscitation practices. The COR team has evolved over the ensuing years, including a name change to the Emergency Response Team (ERT); now a single number activated by any medical staff is used to call the operator who activates the ERT via overhead paging and via code pager system. The 14 member Emergency Response Team consists of PICU/CICU/anesthesia/surgical fellows, ED attending, in‐house residents, PICU/CICU/ED charge nurse, nursing supervisor, resource RN, pharmacist, respiratory therapist, and a messenger.

A database has been maintained by 1 of the authors (DBH) since the inception of the COR/ERT at TCH 16 years ago. This is a retrospective review of this database of ERT activations. An ERT activation could have been triggered by any event that was felt to be emergent, life threatening, and/or needing immediate medical attention. After the event, a debriefing form was filled out about the event. Data collected included date, time, medical record number, location, primary care service, age, sex, primary and secondary diagnoses, and disposition. Data not captured in this database included initial rhythm, need for compressions, cardiac medications, defibrillation or intubation.

Analyses were performed on data collected from January 1993 through April 2007. Medical records of the documented ERT activations were reviewed for missing information and/or clarification of the events. Categories entered in Statistical Package for Social Statistics (SPSS) were similar to information included in the debriefing form: age, sex, admission diagnosis, precipitating event, percentage of admissions, acute vs. chronic diagnosis, winter vs. nonwinter months (October‐March/April‐September), day (6 am‐6 pm) and night (6 pm‐6 am) shifts, survival of ERT activation, survival to discharge, and primary attending service. Data were analyzed using SPSS 16.0 (2007, Chicago, IL). The study was approved by the Colorado Multiple Institutional Review Board.

Results/Conclusion

There were 1537 ERT activations in the database. A total of 203 ERT activations were eliminated from the database: 177 were eliminated from analysis because of missing age, admission diagnosis or time of day of activation, and 26 were ERT activations that had been triggered on adult visitors or adult employees. The remaining 1334 ERT activations were included for analysis.

Table 1 shows the demographics of the patients. The median age was 1.8 years, with a range of 0 to 29 years. A total of 39%(511) of all ERT activations occurred in patients under the age of 1 year with the highest incidence between 1 month and 1 year. Overall, the children at highest risk were males less than 1 year of age with a chronic diagnosis. In addition, time of day and time of year of ERT activations were analyzed as shown. There was no statistical difference between nonwinter (April‐September) and winter (October‐March) months. Statistically, there were significantly more ERT activations during day shifts (6 am‐6 pm) as compared to night shifts (P < 0.001).

The most common admission diagnosis (Table 2) and underlying chronic condition was cardiac disease; other common admission diagnoses were infectious disease, trauma, and pulmonary disease. The medical categories of admission diagnosis included congenital/metabolic (39%), gastrointestinal (29%), renal (18%), rheumatology (4%), toxicology (4%), psychiatry (3%), endocrine (2%), and allergy (1%). The surgery category of admission diagnosis included otolaryngology (63%), orthopedics (28%), urology (4%), dental (4%), and ophthalmology (1%).

Finally, the patients' survival rate after an ERT itself was to be 90% (Table 3), with an overall survival rate to discharge of 78% (Table 4). Survival rate to discharge of those patients who survived the ERT event was 87%. Two patients were missing survival event data and 137 patients were missing survival to discharge data.

Discussion

We present a retrospective review of 1334 emergency response team activations over 13 years at an academic free‐standing tertiary care children's hospital. In keeping with previous reports, we found that children less than 1 year of age were at the highest risk for activation of the emergency response team.1, 4, 5

The National Registry for CardioPulmonary Resuscitation (NRCPR) database cite respiratory failure (asphyxia) and circulatory shock (ischemia) as the most common causes of in‐hospital cardiac arrests.1 Additionally, more than half of pediatric patients that experience a cardiopulmonary arrest have an underlying chronic illness.1, 2, 4, 5, 6 These are similar to our findings that chronic pulmonary and cardiac diseases were among the most frequent admission diagnosis.

Unlike Peberdy et al.,7we did not find an increase in the number of emergency response team activations at night or on weekends. Instead, we found that an ERT activation was more likely to be requested during the day shifts (6 am‐6 pm) which is a similar to that reported by Jones et al.8 Similarly, Jones et al.8 reported that the hourly rate of their medical emergency team activation was greater during the time between 8 am and 6 pm.

Our overall survival rate (78%) to discharge after an ERT event was much higher than what has been reported by Topjian et al.9 (25%). This likely reflects our inclusion of all emergency response team activations, not just apneic and asystolic arrests. The improved survival rate may also be influenced by the 24/7 presence of pediatric fellows, residents and surgeons in our hospital, which has been associated with improved 24‐hour survival for children receiving in‐hospital cardiopulmonary resuscitation.10

There are several limitations to our data collection and this report. This was a retrospective review and as previously noted some medical details were absent resulting in the exclusion of some cases. We also found that the original debriefing form which was used as the basis for the database did not include some important clinical variable, such as vital signs, more detailed events such as rhythm, medications used, and deficits or changes in baseline function. We suggest including multiple variables in a future multicenter study of pediatric RRT's: facility, admitting service, admission diagnosis, age (chronologic and gestational), sex, how long patient has been in hospital prior to the event, past medical history, vitals signs before and after activation, time/date, location, any precipitating events and actions taken by the RRT (eg, cardiopulmonary resuscitation, defibrillation, emergent intubation, and other emergent interventions), medications, survival of event, survival to discharge, deficits or changes in baseline function after the event and to discharge. In particular, information about history of prematurity would have been helpful in assessing further risk factors. Analysis of survival with or without significant deficits or changes in baseline function would be another useful outcome measure. Figure 1 is an example of a debriefing form a multi‐institutional study or hospitalist led quality improvement project may use to collect this data.

Figure 1

We were able to identify a population of higher‐risk patients (less than 1 year of age with comorbidities and an admission diagnosis of cardiac or respiratory disease) to focus our educational efforts on earlier recognition of patient deterioration for both inpatient ward staff and RRT responders. These findings may assist in future quality assurance issues such as patient placement and early ICU admissions depending on age, chronic conditions, and/or admission diagnosis. Future directions should include multi‐center study of RRT to improve external validity. In addition, a more careful analysis of events surrounding the activation, including incorporating such tools as the Pediatric Early Warning (PEW) Score,11 may further assist hospitals and practitioners identify hospitalized children at risk for deterioration on the inpatient ward.