Machine learning-based cardiovascular risk calculator for non-cardiac surgery

Abstract

BACKGROUND: Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least one cardiovascular risk factor. It is estimated that the 30-day mortality is between 0.5% and 2%.The main objective of this study is to develop a traditional machine learning (ML) model that provides a cardiovascular risk score for patients older than 50 years undergoing non-cardiac surgery, calculating the risk from the date of surgery until 30 days post surgery, with specific emphasis on interpretability and explainability of the model's decision-making process. METHODS: The NSQIP 2022 dataset was used to build the model. It consisted of a total of 4 97 011 patients after data cleaning. The primary clinical endpoint was death, myocardial infarction, cardiac arrest or stroke at 30 days postoperatively, which occurred in 1.44% of the patients. Different preprocessing techniques were performed for data cleaning and feature selection. The cleaned data were then used to model the selected learning algorithms, including Logistic Regression, Naive Bayes, Random Forest and boosting Decision Tree algorithms (CatBoost, AdaBoost, Light Gradient Boosting Machine (LightGBM, XGBoost, Gradient Boosting). These models were evaluated in terms of the area under the receiver operating characteristic curve (AUROC) and their corresponding 95% CI. RESULTS: For classification, the trained models were evaluated using AUROC on the test set. LightGBM achieved the highest AUROC of 0.9009 with a 95% CI of 0.8889 to 0.9126. The model consisted of six data elements: type of surgery, American Society of Anesthesiology classification, Blood Urea Nitrogen (BUN), sepsis, emergent surgery and mechanical ventilation. CONCLUSION: In our study, LightGBM classifier proved to be the best model for cardiovascular risk scoring, demonstrating a strong balance between prediction accuracy and generalisation.

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