ArticleHealth science reports2026
Predicting Factors Affecting Postoperative Length of Stay in Patients Undergoing Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review.
Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Factors Associated with Hospital Length of Stay After Coronary Artery Bypass Grafting: Perioperative Characteristics and Postoperative Complication Burden.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aim: Nowadays, coronary artery bypass graft (CABG) surgery has become a common method for treating coronary artery diseases. This surgery requires a long post-operative length of stay (PLOS) in the hospital. The purpose of this study was to systematically review the factors affecting PLOS in patients undergoing CABG surgery using machine learning methods. Method: A comprehensive search was conducted on PubMed, Scopus, IEEE Xplore, and Web of Science, from inception until September 25, 2023. This review was performed according to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses. All studies that investigated the factors affecting PLOS using machine learning methods in patients undergoing CABG surgery were included in the study. Result: In total, 9715 articles were identified after the removal of the duplicates. After the systematic screening, 20 studies met the inclusion criteria. The result showed there are 56 effective factors in predicting PLOS in patients undergoing CABG surgery. Of which 15 factors: age, gender, left ventricular ejection fraction, infection, Perceived Control (PC) levels, BMI, angina class, diabetes, Logistic Euro-score, smoking, fluid balance, inotropes, low cardiac output, atrial fibrillation, and history of cerebrovascular accident are mentioned in more than one article as an affecting factors. Conclusion: This systematic review highlights the multifactorial nature of PLOS, showing that patients' postoperative length of stay is influenced by factors across pre-, intra-, and postoperative care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.