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Predicting Factors Affecting Survival Rate in Patients Undergoing On-Pump Coronary Artery Bypass Graft Surgery Using
Alireza Jafarkhani1, Behzad Imani1, Soheila Saeedi2
1Department of Operating Room, School of Paramedicine Hamadan University of Medical Sciences Hamadan Iran.
Insights
Predicting survival after coronary artery bypass grafting (CABG) is challenging. This review identified key factors like age and renal function using machine learning to improve patient survival predictions post-CABG.
Area of Science:
- Cardiovascular Surgery
- Medical Informatics
- Biostatistics
Background:
- Coronary artery bypass grafting (CABG) is a critical intervention for coronary artery disease.
- Accurate prediction of patient survival post-CABG remains a significant clinical challenge.
- Machine learning (ML) offers potential for enhancing survival prediction accuracy.
Purpose of the Study:
- To systematically review literature on ML techniques for predicting patient survival after CABG.
- To identify key factors influencing survival rates in patients undergoing CABG surgery.
- To enhance understanding of patient outcomes and inform clinical management strategies.
Main Methods:
- A systematic literature search was performed from January 1, 2015, to January 20, 2024.
- Databases searched included PubMed, Scopus, IEEE Xplore, and Web of Science.
- The review followed PRISMA guidelines, including 24 selected studies predicting CABG patient survival.
Main Results:
- A total of 1330 articles were initially identified, with 24 meeting inclusion criteria.
- 43 distinct factors influencing survival rates post-CABG were identified.
- Age, ejection fraction, diabetes mellitus, cerebrovascular history, and renal function were consistently significant predictors.
Conclusions:
- This review highlights key predictors of survival after CABG surgery.
- Machine learning techniques can significantly improve the accuracy of survival predictions.
- Identifying high-risk patients using these factors enables personalized management and therapeutic strategies.
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.
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