Development and Validation of a Tool to Predict High-Need, High-Cost Patients Hospitalized with Ischemic Heart Disease
Description
Objective: To develop and validate a tool to predict patients with ischemic heart disease (IHD) at-risk of excessive healthcare resource utilization. Design: A retrospective cohort study Setting: We identified patients through the State of Florida Agency for Health Care Administration (AHCA) (N=586,518) inpatient dataset. Participants: Adult patients (at least 40 years of age) admitted to the hospital with a diagnosis of ischemic heart disease between January 1, 2007 to December 31, 2016. Primary Outcome Measures: We identified patients whose healthcare utilization is higher than presumed (analysis of residuals) and used logistic regression (binary and multinomial) in estimating the predictive models to classify individuals as high-need, high-care (HNHC) patients relative to inpatient visits (frequency of hospitalization), cost and hospital length of stay (LOS). Discrimination power, prediction accuracy, and model improvement for the binary logistic model were assessed using Receiver Operating Characteristic (ROC) statistic, the Brier score, and the log-likelihood (LL)-based pseudo R2, respectively. LL-based-pseudo R2 and Brier score were utilized for multinomial logistic models. Results: The binary logistic model had good discrimination power (c-statistic =0.6496), an accuracy of probabilistic predictions (Brier Score) of 0.0621, and an LL-based-pseudo R2 of 0.0338 in the development cohort. The model performed similarly in the validation cohort (c-statistic =0.6480), an accuracy of probabilistic predictions (Brier Score) of 0.0620, and an LL-based-pseudo R2 of 0.0380. A user-friendly Excel-based HNHC risk predictive tool was developed and readily available for clinicians and policy decision-makers. Conclusions: The Excel-based HNHC risk predictive tool can accurately identify at-risk patients for HNHC based on three measures of healthcare expenditures.