Development of machine learning models for mortality risk prediction after cardiac surgery

Yunlong Fan; Junfeng Dong; Yuanbin Wu; Ming Shen; Siming Zhu; Xiaoyi He; Shengli Jiang; Jiakang Shao; Chao Song

doi:10.21037/cdt-21-648

Development of machine learning models for mortality risk prediction after cardiac surgery

Cardiovasc Diagn Ther. 2022 Feb;12(1):12-23. doi: 10.21037/cdt-21-648.

Authors

Yunlong Fan^#^{1

2}, Junfeng Dong^#³, Yuanbin Wu^{1

2}, Ming Shen⁴, Siming Zhu^{1

2}, Xiaoyi He^{1

2}, Shengli Jiang², Jiakang Shao¹, Chao Song^{1

2}

Affiliations

¹ Medical School of Chinese PLA, Beijing, China.
² Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing, China.
³ Department of Organ Transplantation, Changzhen Hospital, Navy Medical University, Shanghai, China.
⁴ Department of Cardiology, The First Hospital of Hebei Medical University, Shijiazhuang, China.

^# Contributed equally.

Abstract

Background: We developed machine learning models that combine preoperative and intraoperative risk factors to predict mortality after cardiac surgery.

Methods: Machine learning involving random forest, neural network, support vector machine, and gradient boosting machine was developed and compared with the risk scores of EuroSCORE I and II, Society of Thoracic Surgeons (STS), as well as a logistic regression model. Clinical data were collected from patients undergoing adult cardiac surgery at the First Medical Centre of Chinese PLA General Hospital between December 2008 and December 2017. The primary outcome was post-operative mortality. Model performance was estimated using several metrics, including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). The visualization algorithm was implemented using Shapley's additive explanations.

Results: A total of 5,443 patients were enrolled during the study period. The mean EuroSCORE II score was 3.7%, and the actual in-hospital mortality rate was 2.7%. For predicting operative mortality after cardiac surgery, the AUC scores were 0.87, 0.79, 0.81, and 0.82 for random forest, neural network, support vector machine, and gradient boosting machine, compared with 0.70, 0.73, 0.71, and 0.74 for EuroSCORE I and II, STS, and logistic regression model. Shapley's additive explanations analysis of random forest yielded the top-20 predictors and individual-level explanations for each prediction.

Conclusions: Machine learning models based on available clinical data may be superior to clinical scoring tools in predicting postoperative mortality in patients following cardiac surgery. Explanatory models show the potential to provide personalized risk profiles for individuals by accounting for the contribution of influencing factors. Additional prospective multicenter studies are warranted to confirm the clinical benefit of these machine learning-driven models.

Keywords: Mortality prediction; artificial intelligence; cardiac surgery; machine learning; random forest.