Prediction of Hospital Acute Myocardial Infarction and Heart Failure 30-Day Mortality Rates Using Publicly Reported Performance Measures

Authors


For more information on this article, contact R. Adams Dudley at adams.dudley@ucsf.edu.

Abstract

Objective

To identify an approach to summarizing publicly reported hospital performance data for acute myocardial infarction (AMI) or heart failure (HF) that best predicts current year hospital mortality rates.

Setting

A total of 1,868 U.S. hospitals reporting process and outcome measures for AMI and HF to the Centers for Medicare and Medicaid Services (CMS) from July 2005 to June 2006 (Year 0) and July 2006 to June 2007 (Year 1).

Design

Observational cohort study measuring the percentage variation in Year 1 hospital 30-day risk-adjusted mortality rate explained by denominator-based weighted composite scores summarizing hospital Year 0 performance.

Data Collection

Data were prospectively collected from hospitalcompare.gov.

Results

Percentage variation in Year 1 mortality was best explained by mortality rate alone in Year 0 over other composites including process performance. If only Year 0 mortality rates were reported, and consumers using hospitals in the highest decile of mortality instead chose hospitals in the lowest decile of mortality rate, the number of deaths at 30 days that potentially could have been avoided was 1.31 per 100 patients for AMI and 2.12 for HF (p < .001).

Conclusion

Public reports focused on 30-day risk-adjusted mortality rate may more directly address policymakers’ goals of facilitating consumer identification of hospitals with better outcomes.

Ancillary