Predicting Factors Affecting Postoperative Length of Stay in Patients Undergoing Coronary Artery Bypass Graft Surgery...

Area of Science:

• Cardiovascular Surgery
• Health Informatics
• Medical Data Science

Background:

• Coronary artery bypass graft (CABG) surgery is a common treatment for coronary artery disease.
• CABG surgery is associated with a prolonged post-operative length of stay (PLOS).
• Identifying factors influencing PLOS is crucial for optimizing patient care and resource management.

Purpose of the Study:

• To systematically review factors affecting PLOS in CABG patients.
• To utilize machine learning methods for identifying these influential factors.
• To provide a comprehensive overview of predictors for extended hospital stays post-CABG.

Main Methods:

• A systematic literature search was conducted across major databases (PubMed, Scopus, IEEE Xplore, Web of Science) until September 25, 2023.
• Studies investigating factors affecting PLOS in CABG patients using machine learning were included.
• The review adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Main Results:

• Out of 9715 initially identified articles, 20 studies met the inclusion criteria.
• A total of 56 factors were identified as effective in predicting PLOS for CABG patients.
• Fifteen factors, including age, gender, ejection fraction, infection, BMI, and diabetes, were consistently reported across multiple studies.

Conclusions:

• Post-operative length of stay (PLOS) in CABG patients is multifactorial.
• Influential factors span pre-operative, intra-operative, and post-operative care domains.
• Machine learning approaches can effectively identify key predictors of PLOS in complex surgical patients.