Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.

Background: Postoperative major adverse cardiovascular events (MACEs) account for more than one-third of perioperative deaths. Geriatric patients are more vulnerable to postoperative MACEs than younger patients. Identifying high-risk patients in advance can help with clinical decision making and improve prognosis. This study aimed to develop a machine learning model for the preoperative prediction of postoperative MACEs in geriatric patients.

Methods: We collected patients' clinical data and laboratory tests prospectively. All patients over 65 years who underwent surgeries in West China Hospital of Sichuan University from June 25, 2019 to June 29, 2020 were included. Models based on extreme gradient boosting (XGB), gradient boosting machine, random forest, support vector machine, and Elastic Net logistic regression were trained. The models' performance was compared according to area under the precision-recall curve (AUPRC), area under the receiver operating characteristic curve (AUROC) and Brier score. To minimize the influence of clinical intervention, we trained the model based on undersampling set. Variables with little contribution were excluded to simplify the model for ensuring the ease of use in clinical settings.

Results: We enrolled 5705 geriatric patients into the final dataset. Of those patients, 171 (3.0%) developed postoperative MACEs within 30 days after surgery. The XGB model outperformed other machine learning models with AUPRC of 0.404(95% confidence interval [CI]: 0.219-0.589), AUROC of 0.870(95%CI: 0.786-0.938) and Brier score of 0.024(95% CI: 0.016-0.032). Model trained on undersampling set showed improved performance with AUPRC of 0.511(95% CI: 0.344-0.667, p < 0.001), AUROC of 0.912(95% CI: 0.847-0.962, p < 0.001) and Brier score of 0.020 (95% CI: 0.013-0.028, p < 0.001). After removing variables with little contribution, the undersampling model showed comparable predictive accuracy with AUPRC of 0.507(95% CI: 0.338-0.669, p = 0.36), AUROC of 0.896(95%CI: 0.826-0.953, p < 0.001) and Brier score of 0.020(95% CI: 0.013-0.028, p = 0.20).

Conclusions: In this prospective study, we developed machine learning models for preoperative prediction of postoperative MACEs in geriatric patients. The XGB model showed the best performance. Undersampling method achieved further improvement of model performance.

Trial Registration: The protocol of this study was registered at www.chictr.org.cn (15/08/2019, ChiCTR1900025160).