Leveraging Machine Learning to Predict 30-Day Hospital Readmission After Cardiac Surgery

Abstract

Background: Hospital readmission within 30 days of discharge is a well-studied outcome. Predicting readmission after cardiac surgery, however, is notoriously challenging; the best-performing models in the literature have areas under the curve around .65. A reliable predictive model would enable clinicians to identify patients at risk for readmission and to develop prevention strategies.

Methods: We analyzed The Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database at our institution, augmented with electronic medical record data. Predictors included demographics, preoperative comorbidities, proxies for intraoperative risk, indicators of postoperative complications, and time series-derived variables. We trained several machine learning models, evaluating each on a held-out test set.

Results: Our analysis cohort consisted of 4924 cases from 2011 to 2016. Of those, 723 (14.7%) were readmitted within 30 days of discharge. Our models included 141 STS-derived and 24 electronic medical records-derived variables. A random forest model performed best, with test area under the curve 0.76 (95% confidence interval, 0.73 to 0.79). Using exclusively preoperative variables, as in STS calculated risk scores, degraded the area under the curve, to 0.64 (95% confidence interval, 0.60 to 0.68). Key predictors included length of stay (12.5 times more important than the average variable) and whether the patient was discharged to a rehabilitation facility (11.2 times).

Conclusions: Our approach, augmenting STS variables with electronic medical records data and using flexible machine learning modeling, yielded state-of-the-art performance for predicting 30-day readmission. Separately, the importance of variables not directly related to inpatient care, such as discharge location, amplifies questions about the efficacy of assessing care quality by readmissions.