Incremental value of clinical data beyond claims data in predicting 30-day outcomes after heart failure hospitalization.

BACKGROUND: Administrative claims data are used routinely for risk adjustment and hospital profiling for heart failure outcomes. As clinical data become more readily available, the incremental value of adding clinical data to claims-based models of mortality and readmission is unclear. METHODS AND RESULTS: We linked heart failure hospitalizations from the Get With The Guidelines-Heart Failure registry with Medicare claims data for patients discharged between January 1, 2004, and December 31, 2006. We evaluated the performance of claims-only and claims-clinical regression models for 30-day mortality and readmission, and compared hospital rankings from both models. There were 25 766 patients from 308 hospitals in the mortality analysis, and 24 163 patients from 307 hospitals in the readmission analysis. The claims-clinical mortality model (area under the curve [AUC], 0.761; generalized R(2)=0.172) had better fit than the claims-only mortality model (AUC, 0.718; R(2)=0.113). The claims-only readmission model (AUC, 0.587; R(2)=0.025) and the claims-clinical readmission model (AUC, 0.599; R(2)=0.031) had similar performance. Among hospitals ranked as top or bottom performers by the claims-only mortality model, 12% were not ranked similarly by the claims-clinical model. For the claims-only readmission model, 3% of top or bottom performers were not ranked similarly by the claims-clinical model. CONCLUSIONS: Adding clinical data to claims data for heart failure hospitalizations significantly improved prediction of mortality, and shifted mortality performance rankings for a substantial proportion of hospitals. Clinical data did not meaningfully improve the discrimination of the readmission model, and had little effect on performance rankings.

Duke Scholars

Author Bradley Gordon Hammill Population Health Sciences

Author Lesley H. Curtis Population Health Sciences

Author Adrian Felipe Hernandez Medicine, Cardiology