Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization

Abstract

Background: The accuracy of current models to predict the risk of unplanned readmission or death after a heart failure (HF) hospitalization is uncertain.

Methods: We linked four administrative databases in Alberta to identify all adults discharged alive after a HF hospitalization between April 1999 and 2009. We randomly selected one episode of care per patient and evaluated the accuracy of five administrative data-based models (4 already published, 1 new) for predicting risk of death or unplanned readmission within 30 days of discharge.

Results: Over 10 years, 59652 adults (mean age 76, 50% women) were discharged after a HF hospitalization. Within 30 days of discharge, 11199 (19%) died or had an unplanned readmission. All 5 administrative data models exhibited moderate discrimination for this outcome (c-statistic between 0.57 and 0.61). Neither Centers for Medicare and Medicaid Services (CMS)-endorsed model exhibited substantial improvements over the Charlson score for prediction of 30-day post-discharge death or unplanned readmission. However, a new model incorporating length of index hospital stay, age, Charlson score, and number of emergency room visits in the prior 6 months (the LaCE index) exhibited a 20.5% net reclassification improvement (95% CI, 18.4%-22.5%) over the Charlson score and a 19.1% improvement (95% CI, 17.1%-21.2%) over the CMS readmission model.

Conclusions: None of the administrative database models are sufficiently accurate to be used to identify which HF patients require extra resources at discharge. Models which incorporate length of stay such as the LaCE appear superior to current CMS-endorsed models for risk adjusting the outcome of "death or readmission within 30 days of discharge".