ArticleBMC cardiovascular disorders2025
Development and internal validation of an OPCABG-specific prediction model for postoperative atrial fibrillation in Chinese patients: a retrospective cohort study.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Incidence and Predictors of Postoperative Atrial Fibrillation Following Cardiac Surgery: A Systematic Review.Annals of cardiac anaesthesia · 2026Pooled 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
- Integrating Preoperative NLR and PLR with Perioperative Clinical Factors: A Nomogram for Predicting Postoperative CRP Elevation After Laparoscopic Hysterectomy.International journal of women's health · 2026Article
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4 authors.
Funding
Abstract
backgroundPostoperative atrial fibrillation (POAF) is a common complication after off-pump coronary artery bypass grafting (OPCABG), associated with increased morbidity and healthcare costs. Existing POAF prediction models, developed mainly for Western populations, may not account for genetic, lifestyle, and healthcare disparities in Chinese patients. This study aimed to develop and validate a Chinese-specific nomogram for POAF risk stratification in OPCABG patients.
methodsA retrospective cohort study was conducted at a single Chinese center, including 456 consecutive OPCABG patients (2018-2022). Patients were divided into a training set (2018-2021, n = 319) and validation set (2022, n = 137). Multivariable logistic regression with LASSO regularization identified predictors of POAF (occurrence within 7 postoperative days). Model performance was evaluated using C-index, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).
resultsThe final nomogram included five independent predictors: age (OR, 1.03), diabetes (OR, 1.85), hypertension (OR, 1.90), previous PCI (OR, 2.51) and last intraoperative blood potassium concentration (OR, 0.30). The model demonstrated excellent discrimination (C-index: 0.809 in training, 0.886 in validation) and good calibration. DCA and CIC showed superior clinical utility compared with existing scores (C2HEST, CHADS2, CHA2DS2-VASc).
conclusionsThis OPCABG-specific nomogram outperforms conventional risk scores in predicting POAF in Chinese patients, enabling personalized prophylaxis and resource allocation. External validation in diverse populations is needed to confirm generalizability.
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