each year , 200 000 inhospital cardiac arrests ( ihca ) occur in the united states , with overall survival rates of 18% to 20% .
however , the extent to which hospitals vary in survival rates for ihca remains poorly understood , as prior studies have largely reported aggregate survival rates at the patient level or have had a limited sample of hospitals , and most studies have lacked detailed information on patient factors to ensure adequate casemix adjustment .
furthermore , the extent of sitelevel variation in neurologic status among survivors  an outcome of great importance to many patients , their family members , and providers  remains unknown .
defining variability in survival rates , and examining the degree to which variation persists even after adjusting for patient casemix , would provide important insights into which hospitals could improve resuscitation care and offer opportunities to identify best practices at these hospitals .
if significant variability in survival exists even after adjusting for patient casemix , this may suggest that resuscitation process factors ( eg , time to defibrillation , quality of cardiopulmonary resuscitation ) and quality improvement initiatives may be the primary reasons for substantially higher survival rates in topperforming hospitals .
moreover , it would suggest a greater urgency to develop appropriate research methodology to identify which factors are associated with 
best practices  at hospitals with the highest ihca survival . accordingly , within the american heart association 's get with the guidelinesresuscitation ( gwtgr ) , a large national inhospital cardiac arrest registry , we examined the extent to which hospitals varied in rates of survival to discharge among patients with ihca .
gwtgr is an ideal data source , as it contains ihca data from over 400 us hospitals and collects detailed information on a number of patient and cardiac arrest characteristics to ensure robust casemix adjustment in a specific patient cohort .
this was a multicenter cohort study using data from gwtgr to calculate hospital riskadjusted survival rates for ihca .
gwtgr is sponsored by the american heart association ( aha ) and is a large , comprehensive , prospective national database of ihca in the us .
this dataset also has information from outcome , a quintiles company which is the data collection and coordination center for the gwtgr program .
data are collected from participating acute care hospitals according to standardized utstein definitions for ihca .
specially trained research coordinators at each facility routinely abstract detailed data from the medical chart about resuscitation events if the event elicits an inhospital emergency resuscitation response and a code record is generated .
these data include pre , intra , and postarrest variables , as well as survival at discharge .
the aha provides rigorous quality control and oversight for all gwtgr data collection , analysis , reporting , and research studies .
the study population included all adult patients 18 years of age or older enrolled within the registry between january 1 , 2001 and december 31 , 2010 .
we excluded ihca events in individuals who were not hospital patients , including hospital visitors or employees ( n=293 ) . since patients may have had recurrent ihca events , we focused on the index ihca and excluded 26 190 recurrent arrests . as our study objective was to examine hospital variation in survival outcomes , we excluded 872 ihca events from 94 hospitals with a case volume of fewer than 20 ihcas .
this figure illustrates the exclusion and inclusion criteria applied to the get with the guidelinesresuscitation dataset .
these factors are important to control for , as older and sicker patients are known to have worse outcomes after inhospital arrest .
therefore , the following patientlevel factors were evaluated for model inclusion : age , sex , race , residence prior to hospitalization , and prearrest comorbidities ( neurologic conditions , arrhythmia , myocardial infarction , congestive heart failure , diabetes , pneumonia , respiratory insufficiency , hepatic insufficiency / failure , renal insufficiency / failure , hypotension / hypoperfusion , hematology / oncology conditions , major trauma , and septicemia ) .
additionally , available arrest characteristics were collected in gwtgr and included arrest etiology , presenting rhythm , event location , prearrest inplace interventions , and time and day of arrest .
several hospital factors have previously been identified as important predictors of ihca outcomes yet these factors are not often accounted for in multicenter studies of ihca .
modifiable hospitallevel factors that are associated with outcomes are particularly important as they can reveal opportunities for improvement .
structural variables included in our analysis were : bed size ( small ; 6 to 99 beds , medium ; 100 to 400 , and large ; greater than 400 ) , academic status , and urban compared with rural location .
these variables are included in gwtgr and are adapted from the american hospital association annual survey .
the primary outcome measure was survival at hospital discharge . in this study , we calculated riskadjusted hospital rates for these outcomes .
moreover , since several hospital factors have previously been identified as important predictors of ihca outcomes , we examined how ( patientlevel ) riskadjusted survival rates varied by hospital bed size , academic status , and urban designation .
the unadjusted ihca survival rate for each hospital was calculated , and hospital variation in unadjusted rates of survival was examined by deciles .
we then used multivariable hierarchical regression models to calculate riskadjusted hospital rates of survival to discharge .
these included the previously described demographic variables ( age , race , gender , residence prior to admission ) , comorbidities , intraarrest factors ( eg , initial rhythm , inplace interventions ) , and arrest factors ( eg , etiology , location , time ) . the year of arrest was also included to control for temporal trends in survival . from the model
, we used the model coefficients to calculate the predicted survival probability for each patient .
these probabilities were summed within each hospital to then produce a hospital survival rate . for illustrative purposes , the survival rates for hospitals in each decile were compared .
moreover , we examined survival rates ( riskadjusted for patient level factors ) across hospital deciles after stratifying the sample by bed size , academic status , and urban designation .
finally , we quantified the extent of variation in riskadjusted survival across hospitals in the registry using the median odds ratio ( mor ) , which was derived from the hierarchical model after adjusting for patient factors as follows :   the mor is estimated from hierarchical models and includes only patientlevel factors .
the mor describes the likelihood that patients with similar measured covariates would have different outcomes at randomly chosen hospitals .
a mor of 1.5 suggests that a patient would have a 50% greater odds of a particular outcome at a randomly identified hospital when compared with a similar patient .
this was a multicenter cohort study using data from gwtgr to calculate hospital riskadjusted survival rates for ihca .
gwtgr is sponsored by the american heart association ( aha ) and is a large , comprehensive , prospective national database of ihca in the us .
this dataset also has information from outcome , a quintiles company which is the data collection and coordination center for the gwtgr program .
data are collected from participating acute care hospitals according to standardized utstein definitions for ihca .
specially trained research coordinators at each facility routinely abstract detailed data from the medical chart about resuscitation events if the event elicits an inhospital emergency resuscitation response and a code record is generated .
these data include pre , intra , and postarrest variables , as well as survival at discharge .
the aha provides rigorous quality control and oversight for all gwtgr data collection , analysis , reporting , and research studies .
the study population included all adult patients 18 years of age or older enrolled within the registry between january 1 , 2001 and december 31 , 2010 .
we excluded ihca events in individuals who were not hospital patients , including hospital visitors or employees ( n=293 ) . since patients may have had recurrent ihca events , we focused on the index ihca and excluded 26 190 recurrent arrests .
as our study objective was to examine hospital variation in survival outcomes , we excluded 872 ihca events from 94 hospitals with a case volume of fewer than 20 ihcas .
this figure illustrates the exclusion and inclusion criteria applied to the get with the guidelinesresuscitation dataset .
these factors are important to control for , as older and sicker patients are known to have worse outcomes after inhospital arrest .
therefore , the following patientlevel factors were evaluated for model inclusion : age , sex , race , residence prior to hospitalization , and prearrest comorbidities ( neurologic conditions , arrhythmia , myocardial infarction , congestive heart failure , diabetes , pneumonia , respiratory insufficiency , hepatic insufficiency / failure , renal insufficiency / failure , hypotension / hypoperfusion , hematology / oncology conditions , major trauma , and septicemia ) .
additionally , available arrest characteristics were collected in gwtgr and included arrest etiology , presenting rhythm , event location , prearrest inplace interventions , and time and day of arrest .
several hospital factors have previously been identified as important predictors of ihca outcomes yet these factors are not often accounted for in multicenter studies of ihca .
modifiable hospitallevel factors that are associated with outcomes are particularly important as they can reveal opportunities for improvement .
structural variables included in our analysis were : bed size ( small ; 6 to 99 beds , medium ; 100 to 400 , and large ; greater than 400 ) , academic status , and urban compared with rural location .
these variables are included in gwtgr and are adapted from the american hospital association annual survey .
the primary outcome measure was survival at hospital discharge . in this study , we calculated riskadjusted hospital rates for these outcomes .
moreover , since several hospital factors have previously been identified as important predictors of ihca outcomes , we examined how ( patientlevel ) riskadjusted survival rates varied by hospital bed size , academic status , and urban designation .
the unadjusted ihca survival rate for each hospital was calculated , and hospital variation in unadjusted rates of survival was examined by deciles .
we then used multivariable hierarchical regression models to calculate riskadjusted hospital rates of survival to discharge .
these included the previously described demographic variables ( age , race , gender , residence prior to admission ) , comorbidities , intraarrest factors ( eg , initial rhythm , inplace interventions ) , and arrest factors ( eg , etiology , location , time ) . the year of arrest was also included to control for temporal trends in survival .
from the model , we used the model coefficients to calculate the predicted survival probability for each patient .
these probabilities were summed within each hospital to then produce a hospital survival rate . for illustrative purposes , the survival rates for hospitals in each decile were compared .
moreover , we examined survival rates ( riskadjusted for patient level factors ) across hospital deciles after stratifying the sample by bed size , academic status , and urban designation .
finally , we quantified the extent of variation in riskadjusted survival across hospitals in the registry using the median odds ratio ( mor ) , which was derived from the hierarchical model after adjusting for patient factors as follows :   the mor is estimated from hierarchical models and includes only patientlevel factors .
the mor describes the likelihood that patients with similar measured covariates would have different outcomes at randomly chosen hospitals .
a mor of 1.5 suggests that a patient would have a 50% greater odds of a particular outcome at a randomly identified hospital when compared with a similar patient .
we evaluated a total of 135 896 inhospital cardiac arrest events at 468 hospitals ( figure 1 ) .
of these , 78 787 ( 58% ) were men , 93 945 ( 74% ) were of white race , and 112 877 ( 83% ) were over 50 years of age ( table s1 ) .
two in 5 ihca events had ventricular tachycardia or ventricular fibrillation ( vf / vt ) as the presenting rhythm and 86 672 ( 64% ) occurred in an intensive care unit .
half , 52% ( 245 ) of all study hospitals were academic teaching hospitals ( 245 ) , 89% ( 419 ) were in urban settings , and 278 ( 59% ) had at least 100 beds .
unadjusted rates of survival to discharge varied markedly across hospitals , with a median unadjusted rate for the bottom decile of 8.3% ( range : 0% to 10.7% ) and for the top decile of 31.4% ( range : 28.6% to 51.7% ) ( figure 2 ) . using hierarchical regression
, we identified 36 predictors of inhospital survival to discharge ( table 1 ) . among the predictors were key demographic factors including age , female sex ( or 1.10 ; 95% ci : 1.06 to 1.13 , p<0.01 ) , and black race ( or 0.93 ; 95% ci : 0.87 to 0.99 , p=0.03 ) , comorbidities such as arrhythmia ( or 1.24 ; 95% ci : 1.20 to 1.28 , p<0.01 ) and myocardial infarction ( or 1.19 ; 95% ci : 1.14 to 1.24 , p<0.01 ) , and intraarrest factors including cardiac etiology ( or 1.18 ; 95% ci : 1.14 to 1.23 , p<0.01 ) and initial rhythm of vf or vt ( or 3.14 ; 95% ci : 3.02 to 3.27 ) .
model predictors for survival to discharge ci indicates confidence interval ; icd , implanted cardioverter defibrillator ; or , odds ratio ; vf / vt , ventricular fibrillation / ventricular tachycardia .
this figure illustrates inhospital cardiac arrest ( ihca ) rates ( yaxis ) across hospitals .
rates are adjusted for the patientlevel factors identified in table 1 . after applying the model coefficients to patients in the sample , we calculated riskadjusted survival rates for ihca for each hospital .
the median adjusted rate was 12.4% ( range : 0% to 15.6% ) for the bottom decile and 22.7% ( range : 21.0% to 36.2% ) for the top decile .
of note , of the 46 hospitals in the top decile for ihca survival before risk adjustment , only 24 ( 52% ) remained in the top decile after risk adjustment .
to illustrate the extent of hospital variation in rates of survival to discharge , we determined that the median or was 1.42 ( 95% ci : 1.37 to 1.46 ) , which suggests that there was a 42% greater odds of patients with identical covariates surviving to hospital discharge at one randomly selected hospital compared with another .
after multivariable adjustment , the median adjusted survival rate was 11.9% ( range : 0% to 14.8% ) for the bottom decile of academic hospitals and 22.8% ( range : 20.8% to 36.2% ) for the top decile .
similar patterns of hospital variation were observed for nonacademic hospitals and by hospital bed size and rural versus urban status ( table 2 ) .
stratified ( bottom and top decile ) survival rates by hospital type max indicates maximum ; min , minimum .
unadjusted rates of survival to discharge varied markedly across hospitals , with a median unadjusted rate for the bottom decile of 8.3% ( range : 0% to 10.7% ) and for the top decile of 31.4% ( range : 28.6% to 51.7% ) ( figure 2 ) . using hierarchical regression
, we identified 36 predictors of inhospital survival to discharge ( table 1 ) . among the predictors were key demographic factors including age , female sex ( or 1.10 ; 95% ci : 1.06 to 1.13 , p<0.01 ) , and black race ( or 0.93 ; 95% ci : 0.87 to 0.99 , p=0.03 ) , comorbidities such as arrhythmia ( or 1.24 ; 95% ci : 1.20 to 1.28 , p<0.01 ) and myocardial infarction ( or 1.19 ; 95% ci : 1.14 to 1.24 , p<0.01 ) , and intraarrest factors including cardiac etiology ( or 1.18 ; 95% ci : 1.14 to 1.23 , p<0.01 ) and initial rhythm of vf or vt ( or 3.14 ; 95% ci : 3.02 to 3.27 ) .
model predictors for survival to discharge ci indicates confidence interval ; icd , implanted cardioverter defibrillator ; or , odds ratio ; vf / vt , ventricular fibrillation / ventricular tachycardia .
this figure illustrates inhospital cardiac arrest ( ihca ) rates ( yaxis ) across hospitals .
. rates are adjusted for the patientlevel factors identified in table 1 . after applying the model coefficients to patients in the sample , we calculated riskadjusted survival rates for ihca for each hospital .
the median adjusted rate was 12.4% ( range : 0% to 15.6% ) for the bottom decile and 22.7% ( range : 21.0% to 36.2% ) for the top decile .
of note , of the 46 hospitals in the top decile for ihca survival before risk adjustment , only 24 ( 52% ) remained in the top decile after risk adjustment .
to illustrate the extent of hospital variation in rates of survival to discharge , we determined that the median or was 1.42 ( 95% ci : 1.37 to 1.46 ) , which suggests that there was a 42% greater odds of patients with identical covariates surviving to hospital discharge at one randomly selected hospital compared with another .
after multivariable adjustment , the median adjusted survival rate was 11.9% ( range : 0% to 14.8% ) for the bottom decile of academic hospitals and 22.8% ( range : 20.8% to 36.2% ) for the top decile .
similar patterns of hospital variation were observed for nonacademic hospitals and by hospital bed size and rural versus urban status ( table 2 ) .
stratified ( bottom and top decile ) survival rates by hospital type max indicates maximum ; min , minimum .
in a large national registry of ihca , we found substantial variation in survival across hospitals .
this variability was not explained by differences in patient casemix or hospital characteristics . given that survival outcomes are of immense importance to patients with ihca , these findings raise questions about the quality of resuscitation care at hospitals in the lower deciles and offer important opportunities to identify best practices at hospitals in the upper deciles .
our study extends the work of prior studies of ihca , which largely focused on patientlevel aggregate rates of survival . while important , most of these studies have not examined the influence of the hospital in affecting survival . by employing multilevel hierarchical models to identify patient predictors in this study
, we were able to derive robust riskadjusted survival rates for each hospital , which allowed us to examine the extent of sitelevel variation .
indeed , we found that the hospital at which one is admitted may influence one 's odds of survival by 42% , an enormous effect considering that this is a  hard 
clinical end points . however , the reason for these large variations in outcomes across hospitals is less clear .
these variations in survival may suggest that significant differences in highquality resuscitation care exist among hospitals .
top performing hospitals may have better response times for defibrillation , chest compressions , and initiation of acute cardiac life support medications ( eg , epinephrine , vasopressin ) , or may provide higherquality cardiopulmonary resuscitation ( eg , adequate depth , positioning and rate , minimal interruptions ) .
these hospitals may also excel in postresuscitation care ( eg , therapeutic hypothermia ) , which arguably is an equally important determinant of survival after ihca .
moreover , top performing hospitals may differ in structural variables ( eg , nursing staff ratios ) , training variables ( eg , routine resuscitation simulations , debriefing after ihca events ) , and hospital culture ( eg , leadership , emphasis on quality improvement , differences in duration of cpr)all of which may improve ihca survival outcomes .
a second reason for significant variation in hospital survival may be related to differences in care prior to cardiac arrest .
it is possible that top performing hospitals were able to achieve high rates of survival because their prearrest systems of care were able to detect physiological decline in patients earlier rather than later . as a result ,
their ability to respond and treat patients who developed ihca was more timely and effective .
presence or absence of rapid response teams and the structure and function of these teams may also be a contributing factor to acrosshospital variation in outcomes .
alternatively , it is plausible that hospitals with the highest rates of ihca survival do so because they have a high cardiac arrest incidence rate . therefore , they only  appear  to perform well with cardiac arrest survival because some patients go on to develop ihca when the event could have been prevented . as
the relationship between incidence and outcomes is likely nonlinear , and there are multiple ways that incidence could impact outcomes , this variable was not included in this analyses but could be modeled in future work . finally , hospital differences in ihca survival and may be due to differential application of donotresuscitate orders for all admitted patients .
thus , it is possible that the top performing hospitals have higher rates of donotresuscitate orders  which would be a good  outcome  in the right patient  and therefore would only appear to have better resuscitation outcomes . in order to better understand the determinants of hospital variation in ihca outcomes ,
these approaches will require onsite qualitative interviews with hospital administrators and medical and nursing staff to better understand a given site 's resuscitation practices , strategies to monitor and prevent ihca , and hospital culture and leadership .
moreover , these analyses can provide insights into whether the success of higher performing hospitals is largely due to more liberal use of donotresuscitate orders . in the past
, a mixedmethods approach was critical in differentiating hospitals with better doortoballoon times for stelevation myocardial infarction and identifying core components of top performing hospitals . needed
are clear guidelines and policies that hospitals can implement to measure their performance , provide feedback to providers , and modify hospitalwide practices . in order to move the field of inhospital resuscitation science forward , future studies will need to shift from describing hospital variation to identifying and proscribing best practices at top performing hospitals . although our riskadjusted rates were able to control for multiple confounders , the presence of residual confounding in affecting ihca outcomes exists .
if rates of unmeasured confounders differed markedly across hospitals , this could affect the magnitude of our hospital variation findings .
there may also be additional , more granular patientlevel differences ( eg , laboratory values : prearrest creatinine , prearrest hematocrit , liver function tests ) or differences in data collection between hospitals that are not measurable in the gwtgr registry but may be important to consider in interpreting our reported results .
patient and family preferences regarding endoflife care were not collected in gwtgr , nor did we have rates of donotresuscitate orders ( and timing or reason for these orders ) for all hospital admissions . therefore , some of the observed variation in ihca survival may be due to systematic differences in how aggressively hospitals offered guidance to patients on endoflife care .
additionally , systematic validated information about neurologic status is not available for all patients in the gwgtr dataset
. there may be significant variability in hospitals ' survival to discharge rates and survival to discharge with favorable neurologic outcomes , which could be identified .
a better understanding of this important clinical outcome would be important for future work evaluating hospital variability in ihca outcomes . finally , gwtgr is a voluntary registry of hospitals ; therefore , our findings may not be generalizable to all us hospitals .
although our riskadjusted rates were able to control for multiple confounders , the presence of residual confounding in affecting ihca outcomes exists .
if rates of unmeasured confounders differed markedly across hospitals , this could affect the magnitude of our hospital variation findings .
there may also be additional , more granular patientlevel differences ( eg , laboratory values : prearrest creatinine , prearrest hematocrit , liver function tests ) or differences in data collection between hospitals that are not measurable in the gwtgr registry but may be important to consider in interpreting our reported results .
patient and family preferences regarding endoflife care were not collected in gwtgr , nor did we have rates of donotresuscitate orders ( and timing or reason for these orders ) for all hospital admissions . therefore , some of the observed variation in ihca survival may be due to systematic differences in how aggressively hospitals offered guidance to patients on endoflife care .
additionally , systematic validated information about neurologic status is not available for all patients in the gwgtr dataset
. there may be significant variability in hospitals ' survival to discharge rates and survival to discharge with favorable neurologic outcomes , which could be identified
. a better understanding of this important clinical outcome would be important for future work evaluating hospital variability in ihca outcomes .
finally , gwtgr is a voluntary registry of hospitals ; therefore , our findings may not be generalizable to all us hospitals .
although ihca is associated with low survival , significant variation in survival exists across hospitals , even after adjustment for patient casemix .
further research is needed to investigate additional patientlevel factors and identify best practices at top performing hospitals so that ihca outcomes can improve at all hospitals .
 backgroundinhospital cardiac arrest ( ihca ) is common and often fatal . 
 however , the extent to which hospitals vary in survival outcomes and the degree to which this variation is explained by patient and hospital factors is unknown.methods and resultswithin get with the guidelinesresuscitation , we identified 135 896 index ihca events at 468 hospitals . using hierarchical models 
 , we adjusted for demographics comorbidities and arrest characteristics ( eg , initial rhythm , etiology , arrest location ) to generate riskadjusted rates of inhospital survival . to quantify the extent of hospitallevel variation in riskadjusted rates , we calculated the median odds ratio ( or ) . among study hospitals 
 , there was significant variation in unadjusted survival rates . 
 the median unadjusted rate for the bottom decile was 8.3% ( range : 0% to 10.7% ) and for the top decile was 31.4% ( 28.6% to 51.7% ) . 
 after adjusting for 36 predictors of inhospital survival , there remained substantial variation in rates of inhospital survival across sites : bottom decile ( median rate , 12.4% [ 0% to 15.6% ] ) versus top decile ( median rate , 22.7% [ 21.0% to 36.2% ] ) . 
 the median or for riskadjusted survival was 1.42 ( 95% ci : 1.37 to 1.46 ) , which suggests a substantial 42% difference in the odds of survival for patients with similar casemix at similar hospitals . 
 further , significant variation persisted within hospital subgroups ( eg , bed size , academic).conclusionsignificant variability in ihca survival exists across hospitals , and this variation persists despite adjustment for measured patient factors and within hospital subgroups . 
 these findings suggest that other hospital factors may account for the observed sitelevel variations in ihca survival . 
Introduction
Methods
Study Database
Study Population
Study Design and Variables
HospitalLevel Factors
Outcomes
Statistical Analyses
Results
Variation in Hospital Survival Rates
Discussion
Limitations
Conclusion