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

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.

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

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.

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.

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.

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.

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.