Machine learning-based prediction of 30-day major adverse cardiac and cerebrovascular events in non-cardiac surgery patients

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Abstract Background The number of non-cardiac surgeries performed worldwide has been steadily increasing, presenting a challenge for clinicians to accurately identify patients at high risk of complications and to allocate the appropriate level of perioperative care. Accurate prediction of postoperative mortality is crucial not only for successful patient care, but also for information-based shared decision-making with patients and efficient allocation of medical resources. Purpose In this study, we aimed to develop a novel predictive model using machine learning methods applied to electronic health record data. Our objective is to identify the risk factors most likely to lead to 30-day major adverse cardiac and cerebrovascular events after non-cardiac surgery Methods We conducted a retrospective analysis of data from a single tertiary care institution that included patients aged 65 years or over who underwent non-cardiac surgery from May 2003 and December 2020. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) data was used to build predictive models, which allowed for the utilization of demographic data, as well as preoperative characteristics such as diagnosis, lab results, vital signs, medications, and information on operations and procedures from the electronic health records (EHRs) in a standardized way. We employed machine learning models, which were developed and validated using the OHDSI Patient-Level-Prediction framework. Results We included a total of 47,915 patients to train (75%) and test (25%) our predictive models. To compare prediction performances, we applied gradient boosting machine (GBM), logistic regression (LR), random forest (RF), AdaBoost (AB), and decision tree (DT). Our results for a test data (Fig 1.) showed that the GBM model had the best performance in terms of the area under the receiver operating characteristic curve (AUROC) (0.903) and the area under the precision-recall curve (AUPRC) (0.395). Conclusions Our study demonstrates that applying machine learning algorithms to electronic health record data can effectively identify patients at high risk of major adverse cardiac and cerebrovascular events following non-cardiac surgery. This algorithm has the potential to support clinicians in effectively identifying patients at high risk and provide appropriate perioperative care. Further work is needed to validate and refine the proposed model to ensure its external validity and broader applicability in clinical practice.We plan to validate the proposed model externally by testing it on a cohort of approximately 280,000 patients from other tertiary care institution, and present the results at the 2023 ESC Congress.