A Simple and Powerful Risk-Adjustment Tool for 30-day Mortality Among Inpatients.

BACKGROUND

Risk adjustment for mortality is increasingly important in an era when hospitals and health care systems are being compared with respect to health outcomes and quality. A powerful predictive model has been developed to risk-adjust for 30-day mortality among inpatients, but it is complex and not widely used.

OBJECTIVE

To develop and validate a simpler model, with predictive power similar to more complex models.

RESEARCH DESIGN

This was a retrospective split-validation study. In a derivation cohort, a predictive model for 30-day mortality was developed using logistic regression with the Charlson comorbidity score, Laboratory-Based Acute Physiology Score, and age as the predictor variables. In the validation cohort, the performance and calibration of the model to predict 30-day mortality was examined.

SUBJECTS

All admissions to the medical service of 2 urban university-based teaching hospitals located in Bronx, New York, between July 1, 2002, and April 30, 2008.

MEASURES

All-cause mortality was taken from the social security death registry. Predictor variables were constructed from demographic characteristics, laboratory and billing data extracted from a clinical data repository.

RESULTS

The study sample included 147 991 admissions and overall 30-day mortality was 5.4%. The model had excellent discrimination, with a c-statistics of 0.8585 in the derivation cohort and 0.8484 in the validation cohort. The model accurately predicts 30-day mortality in all risk deciles.

CONCLUSIONS

This simple and powerful predictive model can be used by hospitals and health care systems as a risk-adjustment tool for quality and research purposes.