Development and validation of a tool to predict high-need, high-cost patients hospitalised with ischaemic heart disease

OBJECTIVE: To develop and validate a tool to predict patients with ischaemic heart disease (IHD) at risk of excessive healthcare resource utilisation. DESIGN: A retrospective cohort study. SETTING: We identified patients through the State of Florida Agency for Health Care Administration (N=586 518) inpatient dataset. PARTICIPANTS: Adult patients (at least 40 years of age) admitted to the hospital with a diagnosis of IHD between 1 January 2007 and 31 December 2016. PRIMARY OUTCOME MEASURES: We identified patients whose healthcare utilisation is higher than presumed (analysis of residuals) and used logistic regression (binary and multinomial) in estimating the predictive models to classify individual as high-need, high-care (HNHC) patients relative to inpatient visits (frequency of hospitalisation), cost and hospital length of stay. Discrimination power, prediction accuracy and model improvement for the binary logistic model were assessed using receiver operating characteristic statistic, the Brier score and the log-likelihood (LL)-based pseudo-R(2), respectively. LL-based pseudo-R(2) and Brier score were used 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-R(2) 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-R(2) 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.