ArticleHeliyon2023
A prediction model for new-onset atrial fibrillation following coronary artery bypass graft surgery: A multicenter retrospective study.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- A systematic review and meta-analysis of differences between men and women in short-term outcomes following coronary artery bypass graft surgery.Scientific reports · 2024Pooled it
- Machine learning and Regression-Based models for prediction of postoperative atrial fibrillation following coronary artery bypass grafting: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Development and validation of a machine learning-based predictive model for new-onset atrial fibrillation after CABG.Frontiers in cardiovascular medicine · 2026Article
- Risk factors analysis and nomogram model construction for postoperative atrial fibrillation after off-pump coronary artery bypass grafting.Pakistan journal of medical sciences · 2025Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Developing and assessing a risk prediction model of postoperative atrial fibrillation (POAF) after coronary artery bypass grafting (CABG), and aims to provide a reference for the prediction and prevention. Design: A retrospective case-control study. Setting: Three major urban teaching and university hospitals and tertiary referral centers. Participants: consecutive patients undergoing CABG. Interventions: The study was retrospective and no interventions were administered to patients. Measurements and main results: In the study, the overall new-onset POAF prevalence was approximately 28%. A prediction model for POAF with nine significant indicators was developed, and identified new predictors of POAF: left ventricular end diastolic diameter (LVEDD), intraoperative defibrillation, and intraoperative temporary pacing lead implantation. The model had good discrimination in both the derivation and validation cohorts, with the area under the receiver operating characteristic curves (AUCs) of 0.621 (95% CI = 0.602-0.640) and 0.616 (95% CI = 0.579-0.651), respectively, and showed good calibration. Compared with CHA Conclusion: We have developed and validated a new prediction model of POAF after CABG using multicenter data that can be used in the clinic for early identification of high-risk patients of POAF, and to help effectively prevent POAF in postoperative patients.
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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.