Risk-Standardizing Survival for In-Hospital Cardiac Arrest to Facilitate Hospital Comparisons

Objectives rpose of this study is to develop a method for risk-standardizing hospital survival after cardiac arrest. ound dation with which hospitals can improve quality is to be able to benchmark their risk-adjusted performance against other hospitals, something that cannot currently be done for survival after in-hospital cardiac arrest. s the Get With The Guidelines (GWTG)-Resuscitation registry, we identified 48,841 patients admitted between 2007 and 2010 with an in-hospital cardiac arrest. Using hierarchical logistic regression, we derived and validated a model for survival to hospital discharge and calculated risk-standardized survival rates (RSSRs) for 272 hospitals with at least 10 cardiac arrest cases. s rvival rate was 21.0% and 21.2% for the derivation and validation cohorts, respectively. The model had good discrimination (C-statistic 0.74) and excellent calibration. Eighteen variables were associated with survival to discharge, and a parsimonious model contained 9 variables with minimal change in model discrimination. Before risk adjustment, the median hospital survival rate was 20% (interquartile range: 14% to 26%), with a wide range (0% to 85%). After adjustment, the distribution of RSSRs was substantially narrower: median of 21% (interquartile range: 19% to 23%; range 11% to 35%). More than half (143 [52.6%]) of hospitals had at least a 10% positive or negative absolute change in percentile rank after risk standardization, and 50 (23.2%) had a ≥20% absolute change in percentile rank. sions e derived and validated a model to risk-standardize hospital rates of survival for in-hospital cardiac arrest. Use of this model can support efforts to compare hospitals in resuscitation outcomes as a foundation for quality assessment and improvement.