New Model Could Help Standardize Reporting of Survival for Hospital Cardiac Arrest

A new model to risk-standardize hospital rates of survival for in-hospital cardiac arrest could help hospitals improve quality by allowing them to benchmark their risk-adjusted performance against other hospitals, according to a new study published in the Journal of the American College of Cardiology.

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Using hierarchical logistic regression, study investigators developed and validated a model for survival to hospital discharge and calculated risk-standardized survival rates (RSSRs) for 272 hospitals with at least 10 cardiac arrests. The model used 48,841 patients admitted between 2007 and 2010 with an in-hospital cardiac arrest from the Get With The Guidelines-Resuscitation registry. The mean patient age in the overall cohort was 65.6 + 16.1 years, 58 percent were male, and 21 percent were black. Over 80 percent of patients had a non-shockable cardiac arrest rhythm of asystole or pulseless electrical activity, and nearly half were already in an intensive care unit during the arrest. The authors note that respiratory insufficiency and renal insufficiency were the most prevalent comorbidities, while 25 percent of patients were hypotensive and one-third were receiving mechanical ventilation at the time of cardiac arrest.

Overall, 10,290 (21.1 percent) patients with an in-hospital cardiac arrest survived to hospital discharge. According to the authors, the survival rates were similar in the derivation (n=6844; 21.0%) and validation cohorts (n=3446; 21.2%). In general, the patients who survived "were younger, more frequently white, more likely to have an initial cardiac arrest rhythm of ventricular fibrillation or pulseless ventricular tachycardia, and had fewer comorbidities or interventions in place (e.g., intravenous vasopressors) at the time of cardiac arrest."

However, despite the reduction in variability with the risk-adjustment methodology, some hospitals did have notable differences in risk-standardized rates of survival. For example, 3.3 percent of hospitals had risk-standardized survival rates of >30%, or ~50% higher than the average hospital. "Which hospital factors or quality improvement initiatives are associated with the higher survival outcomes in these hospitals remain unknown," the authors note. Therefore, they suggest that identification of best practices at these top-performing hospitals be a priority in order to disseminate the information more broadly to other hospitals looking to improve their quality.

Moving forward, the authors note that their model had good discrimination (C-statistic 0.74) and excellent calibration. Importantly, they also note that the model adhered to recommended public reporting standards, including the use of hierarchical models, timely and high-quality data, and clearly defined study population and outcomes.

"Given poor survival outcomes for in-hospital cardiac arrest, there is growing national interest in developing performance metrics to benchmark hospital survival for this condition," the investigators said. "We believe that use of this model to adjust for patient case-mix represents an advance in ongoing efforts to profile hospitals in resuscitation outcomes, with the hope that clinicians and administrators will be stimulated to develop novel and effective quality improvement strategies to improve their hospital's performance."

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