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Risk-Standardizing Survival for In-Hospital Cardiac Arrest to Facilitate Hospital Comparisons
Risk-Standardizing Survival for In-Hospital Cardiac Arrest to Facilitate Hospital Comparisons JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY Chan, P. S., Berg, R. A., Spertus, J. A., Schwamm, L. H., Bhatt, D. L., Fonarow, G. C., Heidenreich, P. A., Nallamothu, B. K., Tang, F., Merchant, R. M. 2013; 62 (7): 601-609Abstract
OBJECTIVES: To develop a method for risk-standardizing hospital survival after cardiac arrest. BACKGROUND: A foundation 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. METHODS: Within the Get With The Guidelines-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. RESULTS: The survival 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. Prior to risk-adjustment, the median hospital survival rate was 20% (IQR: 14%-26%), with a wide range (0%-85%). After adjustment, the distribution of RSSRs was substantially narrower: median of 21% (IQR: 19%-23%; range: 11%-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. CONCLUSION: We have 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.
View details for DOI 10.1016/j.jacc.2013.05.051
View details for Web of Science ID 000323605200007
View details for PubMedID 23770167