Predicting Factors Affecting Survival Rate in Patients Undergoing On-Pump Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review

Abstract

Background and aim: Coronary artery bypass grafting (CABG) is a key treatment for coronary artery disease, but accurately predicting patient survival after the procedure presents significant challenges. This study aimed to systematically review articles using machine learning techniques to predict patient survival rates and identify factors affecting these rates after CABG surgery.

Methods: From January 1, 2015, to January 20, 2024, a comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Inclusion criteria included studies that evaluated survival rates and predictors associated with CABG patients during the specified period.

Results: After eliminating duplicates, a total of 1330 articles were identified. Following a systematic screening, 24 studies met the inclusion criteria. Our findings revealed 43 distinct factors influencing survival rates in patients undergoing CABG. Notably, five factors-age, ejection fraction, diabetes mellitus, a history of cerebrovascular disease or accidents, and renal function-were consistently identified across multiple studies as significant predictors of postsurgical survival.

Conclusion: This systematic review identifies key factors influencing survival rates after CABG surgery and highlights the role of machine learning in improving predictive accuracy. By identifying high-risk patients through these key factors, our findings offer practical insights for healthcare providers, enhancing patient management and customizing therapeutic strategies after CABG. This study significantly enhances existing literature by combining machine learning techniques with clinical factors, thereby improving the understanding of patient outcomes in CABG surgery.

Keywords: coronary artery bypass; machine learning; survival rate; systematic review.

Figure 1

Flow diagram of the literature search and study selection. This figure illustrates the stages of screening studies, from identification to inclusion. Given the study's objectives and the significance of inclusion and exclusion criteria, 24 studies were ultimately included in the final analysis out of the initial 2206 studies retrieved in the first stage.

Figure 2

Distribution of published studies by year. The figure represents the number of articles published from 2015 to 2023. It clearly shows that the highest number of articles published in our field is in 2018.

Figure 3

Distribution of published studies by country. The figure illustrates the number of articles published on our topic in various countries. It appears that patient survival following on‐pump CABG surgery is particularly popular among authors in the United States.

Figure 4

Distribution of published studies by the journal. This figure displays the published articles reviewed in our study across different journals. The two journals in the first column on the left appear to be the most popular choices for accepting these articles, each publishing eight articles authored by the researchers.

Figure 5

Distribution of published studies based on applied analysis methods. This figure illustrates the distribution of machine learning methods utilized in studies. Cox regression is the most commonly used machine learning technique for data analysis in the studies conducted.