Background: Readmissions after heart failure (HF) are the focus of pay-for-performance initiatives. Early and accurate identifcation of patients at risk for readmissions may improve quality and reduce cost of care. Methods: OM1 Linked Data contains linked claims and EMR data from 20 million US patients. Of ~200,000 patients with HF, 24,615 met study criteria of at least 1 HF-related hospital admission and at least 6 months data preceding that admission. Logistic regression, random forests, classifcation and regression trees were used to identify pre-admission predictors of 30-day readmission or death. Models were tested by 10-fold cross-validation in CART and training-validation (67% versus 33%) in logistic regression. We computed the simple risk score by adding the assigned weights to each predictor based on the parameter estimates from the logistic regression. Results: Of the 24,615 patients with index HF hospitalization, 3,109 (13%) were readmitted within 30 days and 365 (1.5%) died. Hospitalizations in the previous year (odds ratio [confdence interval]: 1.2 [1.1, 1.3]), procedures in the previous year (1.1 [1.0, 1.1]), 5-point higher Charlson comorbidity index (1.6 [1.3, 2.0]), and months since last hospitalization (0.9 [0.9, 1.0]) were the strongest predictors. The overall classifcation rate in the cross-validation cohort was 69%; 73% in 21,141 patients who did not have an outcome, and 46% in 3,474 patients who died or were readmitted. The median [Q1, Q3] risk score was 35 [18, 59] in the validation cohort (n=8208). The risk score correlated well with the readmission rate within each decile: R2=0.90 in both the weighted and unweighted regression analyses where the weight was the number of patients within each decile. Patients with risk scores of ≥5 were classifed as high risk. In the validation cohort of 1,157 patients who were readmitted or died, 600 (51.9%) had a risk score of at least 5. Conclusions: Our model correctly predicted 30-day readmission or death in 7 out of 10 patients, prior to index HF admission. We developed a simple score based on routinely available data to identify patients at high risk. We will use notes, and machine learning techniques to further improve predictive accuracy.
Su, Z., Brecht, T., Gliklich, R., & Menon, V. (2017). A SIMPLE PREDICTIVE SCORE FOR PRE-ADMISSION IDENTIFICATION OF RISK OF 30-DAY HOSPITAL READMISSION OR DEATH IN HEART FAILURE. Journal of the American College of Cardiology, 69(11), 772. https://doi.org/10.1016/s0735-1097(17)34161-x