| Summary: | Objective To develop a model that predicts the risk of 30-day, all-cause readmission in Medicaid patients hospitalized for heart failure. Design Retrospective study of a population cohort to create a predictive model. Setting and Participants We analyzed 2016-2019 Medicaid claims data from seven US states. We defined a heart failure admission as one in which either the admission diagnosis or the first or second clinical (discharge) diagnosis bore an ICD-10 code for heart failure. A readmission was an admission for any condition (not necessarily heart failure) that occurred within 30 days of a heart failure discharge. Methods We estimated a mixed-effects logistic model to predict 30-day readmission from patient demographic data, comorbidities, past healthcare utilization, and characteristics of the index hospitalization. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristics curve (AUC). Results 6,859 patients contributed 9,336 heart failure hospitalizations; 2,667 (28.6 percent) were 30-day readmissions. The final model included age, number of admissions and emergency room visits in the preceding year, length of stay, discharge status, index admission type, US state of admission, and past diagnoses. The observed vs. predicted plot showed good fit, and the estimated AUC of 0.745 was robust in sensitivity analyses. Conclusions and Implications Our model robustly and with moderate precision identifies Medicaid patients hospitalized for heart failure who are at a high risk of readmission. One can use the model to guide the development of post-discharge management interventions for reducing readmissions and for rigorously adjusting comparisons of 30-day readmission rates between sites/providers or over time.
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