Pediatric cardiac surgery: machine learning models for postoperative complication prediction.

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Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a significant challenge for anesthesiologists. Machine Learning (ML)-assisted tools have the potential to enhance the recognition of patients at risk of complications and predict potential issues, ultimately improving outcomes. We evaluated the prediction capacity of six models, ranging from logistic regression to support vector machine, using a dataset comprising 33 variables and 1364 subjects. TheArea Under the Curve (AUC)and theF1 scoreserved as the primary evaluation metrics. Our primary objectives were twofold: first, to develop aneffective prediction model, and second, to create auser-friendly comprehensive modelfor identifying high-risk patients. Thelogistic regression model demonstrated the highest effectiveness, achieving anAUC of 83.65%, and an F1 score of 0.7296, with balanced sensitivity and specificity of77.94%and76.47%, respectively. In comparison, thecomprehensive three-layer decision tree modelachieved anAUC of 72.84%, with sensitivity (79.41%) comparable to more complex models. Our machine learning-assisted tools provide an additional perspective and enhance the predictive capabilities of traditional scoring methods. These tools can assist anesthesiologists in making well-informed decisions. Furthermore, we have successfully demonstrated the feasibility of creating a practical white-box model. The next steps involve conducting clinical validation and multicenter cross-validation. NCT05537168.