Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI "READMITS" (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus, Nonmale Sex, Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score. Issue 8 (12 September 0207)

• Record Type: Journal Article
• Authors: Nguyen, Oanh Kieu; Makam, Anil N.; Clark, Christopher; Zhang, Song; Das, Sandeep R.; Halm, Ethan A.
• Is Part Of: Journal of the American Heart Association. Volume 7:Issue 8(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 0207-09-12
• Subjects: acute myocardial infarction -- health services research -- hospital performance -- prediction -- readmission; Heart -- Diseases -- Periodicals; Cardiovascular system -- Diseases -- Periodicals; Cerebrovascular disease -- Periodicals; Cardiology -- Periodicals; 616.1
• DOI: 10.1161/JAHA.118.008882
• Languages: English
• ISSNs: 2047-9980

Abstract

Abstract : Background: Readmissions after hospitalization for acute myocardial infarction (AMI) are common. However, the few currently available AMI readmission risk prediction models have poor‐to‐modest predictive ability and are not readily actionable in real time. We sought to develop an actionable and accurate AMI readmission risk prediction model to identify high‐risk patients as early as possible during hospitalization. Methods and Results: We used electronic health record data from consecutive AMI hospitalizations from 6 hospitals in north Texas from 2009 to 2010 to derive and validate models predicting all‐cause nonelective 30‐day readmissions, using stepwise backward selection and 5‐fold cross‐validation. Of 826 patients hospitalized with AMI, 13% had a 30‐day readmission. The first‐day AMI model (the AMI "READMITS" score) included 7 predictors: renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure, had an optimism‐corrected C‐statistic of 0.73 (95% confidence interval, 0.71–0.74) and was well calibrated. The full‐stay AMI model, which included 3 additional predictors (use of intravenous diuretics, anemia on discharge, and discharge to postacute care), had an optimism‐corrected C‐statistic of 0.75 (95% confidence interval, 0.74–0.76) with minimally improved net reclassification and calibration. Both AMI models outperformed corresponding multicondition readmission models. Conclusions: The parsimonious AMI READMITS score enables early prospective identification of high‐risk AMI patients for targeted readmissions reduction interventions within the first 24 hours of hospitalization. A full‐stay AMI readmission model only modestly outperformed the AMI READMITS score in terms of discrimination, but surprisingly did not meaningfully improve reclassification.