Using the Super Learner algorithm to predict risk of 30-day readmission after bariatric surgery in the United States

Background: Risk prediction models that estimate patient probabilities of adverse events are commonly deployed in bariatric surgery. The objective was to validate a machine learning (Super Learner) prediction model of 30-day readmission after bariatric surgery in comparison with a traditional logistic regression. Methods: This prognostic study for validation of risk prediction models used data from the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program. Patients who underwent elective laparoscopic gastric bypass or laparoscopic sleeve gastrectomy between 2015 and 2018 were included. Models used 5-fold cross-validation and were evaluated using the area under the receiver operating characteristic curve, the net reclassification index, and the integrated discrimination improvement. Results: The 30-day readmission rate among 393,833 patients was 3.9%. Super Learner area under the receiver operating characteristic curve was 0.674 (95% confidence interval 0.670–0.679), compared to 0.650 (95% confidence interval 0.645–0.654) for logistic regression. The net reclassification index was 0.239 (95% confidence interval 0.223–0.254), and 0.252 (95% confidence interval 0.249–0.255) for those who were and were not readmitted within 30 days. The integrated discrimination improvement was 0.0032 (95% confidence interval 0.0030–0.0033). Conclusion: The Super Learner outperformed traditional logistic regression in predicting risk of 30-day readmission after bariatric surgery. Machine learning models may help target high-risk patients more optimally and prevent unnecessary readmissions.