ArticleBMC anesthesiology2022
Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.
Article in BMC anesthesiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Artificial intelligence for predicting perioperative anaesthetic complications and supporting clinical decision-making: a scoping review.Journal of clinical monitoring and computing · 2026Review
- Unraveling 'F' factor: towards a genetic-clinical framework for the musculoskeletal-heart crosstalk in metabolic aging.Cardiovascular diabetology · 2026Article
- An interpretable machine learning tool for predicting perioperative cardiac events in patients scheduled for hip fracture surgery: insights from the multicenter LUSHIP study.Journal of anesthesia, analgesia and critical care · 2025Article
- Development and validation of machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study.International journal of colorectal disease · 2025Article
- Prediction of early postoperative complications and transfusion risk after lumbar spinal stenosis surgery in geriatric patients: machine learning approach based on comprehensive geriatric assessment.BMC medical informatics and decision making · 2025Article
- A Transformer-Based Framework for Counterfactual Estimation of Antihypertensive Treatment Effect on COVID-19 Infection Risk - A Proof-of-Concept Study.American journal of hypertension · 2025Article
- Naïve Bayes is an interpretable and predictive machine learning algorithm in predicting osteoporotic hip fracture in-hospital mortality compared to other machine learning algorithms.PLOS digital health · 2025Article
- Advanced Non-linear Modeling and Explainable Artificial Intelligence Techniques for Predicting 30-Day Complications in Bariatric Surgery: A Single-Center Study.Obesity surgery · 2024Article
- Machine learning with clinical and intraoperative biosignal data for predicting postoperative delirium after cardiac surgery.iScience · 2024Article
- Machine learning-based prediction of cerebral artery territorial infarcts in acute ischemic stroke using non-contrast CT.Frontiers in neurologyArticle
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostoperative 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.
methodsWe 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.
resultsWe 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).
conclusionsIn 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 registrationThe protocol of this study was registered at www.chictr.org.cn (15/08/2019, ChiCTR1900025160).
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