• Jaehyeon Yun, Giovanni Filardo, Vishal Ahuja, Michael E Bowen, and Daniel F Heitjan.
• Department of Statistics & Data Science, Southern Methodist University, 144 Heroy Hall, 3225 Daniel St, Dallas, TX 75275-0332. Email: dheitjan@smu.edu.
• Am J Manag Care. 2023 Aug 1; 29 (8): e229e234e229-e234.

ObjectivesReadmission is common and costly for hospitalized Medicaid patients with diabetes. We aimed to develop a model predicting risk of 30-day readmission in Medicaid patients with diabetes hospitalized for any cause.Study DesignUsing 2016-2019 Medicaid claims from 7 US states, we identified patients who (1) had a diagnosis of diabetes or were prescribed any diabetes drug, (2) were hospitalized for any cause, and (3) were discharged to home or to a nonhospice facility. For each encounter, we assessed whether the patient was readmitted within 30 days of discharge.MethodsApplying least absolute shrinkage and selection operator variable selection, we included demographic data and claims history in a logistic regression model to predict 30-day readmission. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristic curve (AUROC).ResultsAmong 69,640 eligible patients, there were 129,170 hospitalizations, of which 29,410 (22.8%) were 30-day readmissions. The final model included age, sex, age-sex interaction, past diagnoses, US state of admission, number of admissions in the preceding year, index admission type, index admission diagnosis, discharge status, length of stay, and length of stay-sex interaction. The observed vs predicted plot showed good fit. The estimated AUROC of 0.761 was robust in analyses that assessed sensitivity to a range of model assumptions.ConclusionsOur model has moderate power for identifying hospitalized Medicaid patients with diabetes who are at high risk of readmission. It is a template for identifying patients at risk of readmission and for adjusting comparisons of 30-day readmission rates among sites or over time.

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