ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. General Cardiovascular Medicine
Comparison of multiple machine learning methods for predicting postoperative hyperglycemia in patients without diabetes undergoing cardiac surgery
Provisionally accepted
- 1Bengbu Medical University, Bengbu, China
- 2The Third The People's Hospital of Bengbu, Bengbu, China
- 3The First Affiliated Hospital of Bengbu Medical University, Bengbu, China
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Background: Stress-induced hyperglycemia (SHG) represents a significant metabolic complication in non-diabetic cardiac surgery older adult patients, with substantial implications for postoperative outcomes. Despite its clinical importance, reliable predictive tools remain scarce. This study systematically compared the performance of logistic regression versus advanced machine learning algorithms for SHG risk prediction in this vulnerable population. Patients and Methods: We conducted a retrospective cohort analysis of 600 patients (≥65 years) undergoing cardiac surgery at a tertiary medical center (January 2021 to May 2025). Six clinically relevant perioperative variables were incorporated into five predictive models: logistic regression, Random Forest (RF), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Model performance was rigorously evaluated using AUC-ROC with 95% confidence intervals, sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and precision. Results: The incidence of SHG in this cohort was 70.5%. Comparative analysis revealed logistic regression as the top-performing model (AUC 0.944, 95% CI 0.923–0.966), surpassing other algorithms: GBM (0.923, 0.902–0.952), XGBoost (0.904, 0.890–0.941), AdaBoost (0.916, 0.871–0.936), and RF (0.877, 0.866–0.932). Moreover, the logistic model achieved optimal performance in sensitivity (94.5%), specificity (93.4%), PPV (97.7%), and NPV (96.8%). Conclusion: In contrast to more complex machine learning approaches, logistic regression demonstrated superior predictive accuracy for SHG in non-diabetic cardiac surgery older adult patients. Its exceptional performance metrics and clinical interpretability support its practical utility as an effective decision-support tool for perioperative risk stratification and management.
Keywords: Stress hyperglycaemia, Model prediction, Nomograms, Risk Assessment, retrospective analysis
Received: 06 Sep 2025; Accepted: 06 Nov 2025.
Copyright: © 2025 Wu, Zhang, Cui, Yang, Wang, Duan and Xue. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Huan Duan, duanhuan886@sina.com
Fang Xue, 0700036@bbmc.edu.cn
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