Background To improve value in the care of patients with acute myocardial infarction (MI), payment models increasingly hold providers accountable for costs. As such, providers need tools to predict length of stay (LOS) during hospitalization and the likelihood of needing postacute care facilities after discharge for acute MI patients. We developed models to estimate risk for prolonged LOS and postacute care for acute MI patients at time of hospital admission to facilitate coordinated care planning. Methods and Results We identified patients in the National Cardiovascular Data Registry ACTION registry (Acute Coronary Treatment and Intervention Outcomes Network) who were discharged alive after hospitalization for acute MI between July 1, 2008 and March 31, 2017. Within a 70% random sample (Training cohort) we developed hierarchical, proportional odds models to predict LOS and hierarchical logistic regression models to predict discharge to postacute care. Models were validated in the remaining 30%. Of 633 737 patients in the Training cohort, 16.8% had a prolonged LOS (≥7 days) and 7.8% were discharged to a postacute facility (extended care, a transitional care unit, or rehabilitation). Model discrimination was moderate in the validation dataset for predicting LOS (C statistic=0.640) and strong for predicting discharge to postacute care (C statistic=0.827). For both models, discrimination was similar in ST-segment-elevation MI and non-ST-segment-elevation MI subgroups and calibration was excellent. Conclusions These models developed in a national registry can be used at the time of initial hospitalization to predict LOS and discharge to postacute facilities. Prospective testing of these models is needed to establish how they can improve care coordination and lower costs.
Keywords: acute myocardial infarction; delivery of health care; hospitalization; length of stay; rehabilitation; transitional care.