Effects of Neighborhood-level Data on Performance and Algorithmic Equity of a Model That Predicts 30-day Heart Failure Readmissions at an Urban Academic Medical Center

Background: Socioeconomic data may improve predictions of clinical events. However, owing to structural racism, algorithms may not perform equitably across racial subgroups. Therefore, we sought to compare the predictive performance overall, and by racial subgroup, of commonly used predictor variables for heart failure readmission with and without the area deprivation index (ADI), a neighborhood-level socioeconomic measure. Methods and Results: We conducted a retrospective cohort study of 1316 Philadelphia residents discharged with a primary diagnosis of congestive heart failure from the University of Pennsylvania Health System between April 1, 2015, and March 31, 2017. We trained a regression model to predict the probability of a 30-day readmission using clinical and demographic variables. A second model also included the ADI as a predictor variable. We measured predictive performance with the Brier Score (BS) in a held-out test set. The baseline model had moderate performance overall (BS 0.13, 95% CI 0.13-0.14), and among White (BS 0.12, 95% CI 0.12-0.13) and non-White (BS 0.13, 95% CI 0.13-0.14) patients. Neither performance nor algorithmic equity were significantly changed with the addition of the ADI. Conclusions: The inclusion of neighborhood-level data may not reliably improve performance or algorithmic equity.