Development, validation, and clinical evaluation of a machine-learning based model for diagnosing early infection after cardiovascular surgery (DEICS): a multi-center cohort study

Background: This study addresses the critical need for timely and accurate diagnosis of early postoperative infection (EPI) following cardiac surgery. EPI significantly impacts patient outcomes and healthcare costs, making its early detection vital. Objectives: To develop, validate, and clinically implement a machine-learning-based model for diagnosing EPI post-cardiac surgery, enhancing postoperative care. Methods: In this multi-center cohort study spanning 2020 to 2022, data from four medical centers involved 2001 participants. Of these, 1400 were used for trainingand 601 for validation. Several machines-learning algorithms, including XGBoost, random forest, support vector machines, least absolute shrinkage and selection operator, and single-layer neural networks, were applied to develop predictive models. These were compared against a traditional logistic regression model. The model with the highest area under the receiver operating characteristic curve (AUROC) was deemed optimal. Implemented across four centers since 1 January 2023, a retrospective real-world study assessed its clinical applicability. Among 400 patients with an estimated EPI risk above 10%, identified by the optimal model, 55 followed its antibiotic upgrade recommendations (DEICS group). The remaining 345 patients upgraded antibiotics empirically, with 55 in the control group, matched 1:1 with the DEICS group. Clinical utility was evaluated through antibiotic use density (AUD), hospital costs, and ICU stay duration. Results: The XGBoost model achieved the highest performance with an AUROC of 0.96 (95% CI: 0.93–0.98). The calibration curve exhibited strong agreement with Brier scores of 0.02. According to the XGBoost model, the DEICS group significantly demonstrated reduced AUD ( P < 0.01) in the matched cohort, along with decreased ICU stay time (median: 5 vs. 6 days, P = 0.01) and hospital costs (median: ¥150 000 vs. median: ¥200 000, P = 0.01) in the EPI cohort. Conclusion: The successful implementation of the XGBoost model facilitates accurate EPI diagnosis, improves postoperative recovery, and lowers hospital costs.