Development and validation of a novel prediction model for new-onset atrial fibrillation after lung resection
Background Postoperative atrial fibrillation (POAF) is the most prevalent and potentially life-threatening arrhythmia following thoracic surgery. This study aimed to construct and validate a predictive model for assessing POAF risk.Methods A meta-analysis was conducted to rank risk factors associate...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Annals of Medicine |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2025.2519673 |
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| author | Yasha Chen Yangxi Hu Jiamei Wang Jianmin Sun Bowen Hu Kun Huang Zhiqing He Chun Liang Yunling Lin |
| author_facet | Yasha Chen Yangxi Hu Jiamei Wang Jianmin Sun Bowen Hu Kun Huang Zhiqing He Chun Liang Yunling Lin |
| author_sort | Yasha Chen |
| collection | DOAJ |
| description | Background Postoperative atrial fibrillation (POAF) is the most prevalent and potentially life-threatening arrhythmia following thoracic surgery. This study aimed to construct and validate a predictive model for assessing POAF risk.Methods A meta-analysis was conducted to rank risk factors associated with POAF based on their respective risk ratios (RRs). Significant risk factors identified from the meta-analyses were incorporated into the model and assigned weights. External validation was performed using a retrospective cohort from China. Receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis (DCA) were employed to assess the model’s predictive performance, calibration and clinical utility.Results We screened 40 cohort studies involving 58,899 patients. We developed a risk model that incorporated age ≥ 70 years (RR 2.10, 95% CI 1.34–3.30; p < 0.05), male sex (RR 1.46, 95% CI 1.34–1.60; p < 0.05), COPD (RR 2.28, 95% CI 1.81–2.89; p < 0.05), CAD (RR 1.72, 95% CI 1.49–1.99; p < 0.05), heart failure (RR 1.62, 95% CI 1.12–2.35; p < 0.05), pneumonectomy (RR 2.32, 95% CI 2.01–2.67; p < 0.05) and lobectomy (RR 1.86, 95% CI 1.38–2.51; p < 0.05) and thoracotomy (RR 1.46, 95% CI 1.30–1.64; p < 0.05). Validation was performed in an external cohort of 1546 participants, demonstrating strong discrimination with an area under the receiver operating characteristic curve (95% CI) of 0.89 (95% CI 0.81–0.83). The calibration curve and DCA curve results demonstrated good concordance and applicability.Conclusions This model, built with easily accessible clinical variables, could accurately predict the risk of POAF. This holds promise for improving clinical decision making and guiding early interventions. |
| format | Article |
| id | doaj-art-bbdd6b99a40b463c915ddf30954c6701 |
| institution | Kabale University |
| issn | 0785-3890 1365-2060 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj-art-bbdd6b99a40b463c915ddf30954c67012025-08-20T03:31:11ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602025-12-0157110.1080/07853890.2025.2519673Development and validation of a novel prediction model for new-onset atrial fibrillation after lung resectionYasha Chen0Yangxi Hu1Jiamei Wang2Jianmin Sun3Bowen Hu4Kun Huang5Zhiqing He6Chun Liang7Yunling Lin8Department of Cardiology, Shanghai Changzheng Hospital, Navy Medical University, Shanghai, ChinaDepartment of Cardiology, Shanghai Changzheng Hospital, Navy Medical University, Shanghai, ChinaDepartment of Cardiology, Shanghai Changzheng Hospital, Navy Medical University, Shanghai, ChinaDepartment of Cardiology, Fujian Medical University Union Hospital, Fujian Heart Medical Center, Fujian Clinical Medical Research Center for Heart and Macrovascular Diseases, Fujian Institute of Coronary Artery Disease, Fuzhou, ChinaDepartment of Cardiology, Shanghai Changzheng Hospital, Navy Medical University, Shanghai, ChinaDepartment of Cardiology, Shanghai Changzheng Hospital, Navy Medical University, Shanghai, ChinaDepartment of Cardiology, Shanghai Changzheng Hospital, Navy Medical University, Shanghai, ChinaDepartment of Cardiology, Shanghai Changzheng Hospital, Navy Medical University, Shanghai, ChinaDepartment of Cardiology, Fujian Medical University Union Hospital, Fujian Heart Medical Center, Fujian Clinical Medical Research Center for Heart and Macrovascular Diseases, Fujian Institute of Coronary Artery Disease, Fuzhou, ChinaBackground Postoperative atrial fibrillation (POAF) is the most prevalent and potentially life-threatening arrhythmia following thoracic surgery. This study aimed to construct and validate a predictive model for assessing POAF risk.Methods A meta-analysis was conducted to rank risk factors associated with POAF based on their respective risk ratios (RRs). Significant risk factors identified from the meta-analyses were incorporated into the model and assigned weights. External validation was performed using a retrospective cohort from China. Receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis (DCA) were employed to assess the model’s predictive performance, calibration and clinical utility.Results We screened 40 cohort studies involving 58,899 patients. We developed a risk model that incorporated age ≥ 70 years (RR 2.10, 95% CI 1.34–3.30; p < 0.05), male sex (RR 1.46, 95% CI 1.34–1.60; p < 0.05), COPD (RR 2.28, 95% CI 1.81–2.89; p < 0.05), CAD (RR 1.72, 95% CI 1.49–1.99; p < 0.05), heart failure (RR 1.62, 95% CI 1.12–2.35; p < 0.05), pneumonectomy (RR 2.32, 95% CI 2.01–2.67; p < 0.05) and lobectomy (RR 1.86, 95% CI 1.38–2.51; p < 0.05) and thoracotomy (RR 1.46, 95% CI 1.30–1.64; p < 0.05). Validation was performed in an external cohort of 1546 participants, demonstrating strong discrimination with an area under the receiver operating characteristic curve (95% CI) of 0.89 (95% CI 0.81–0.83). The calibration curve and DCA curve results demonstrated good concordance and applicability.Conclusions This model, built with easily accessible clinical variables, could accurately predict the risk of POAF. This holds promise for improving clinical decision making and guiding early interventions.https://www.tandfonline.com/doi/10.1080/07853890.2025.2519673Postoperative atrial fibrillationlung resectionprediction modelmeta-analysis |
| spellingShingle | Yasha Chen Yangxi Hu Jiamei Wang Jianmin Sun Bowen Hu Kun Huang Zhiqing He Chun Liang Yunling Lin Development and validation of a novel prediction model for new-onset atrial fibrillation after lung resection Annals of Medicine Postoperative atrial fibrillation lung resection prediction model meta-analysis |
| title | Development and validation of a novel prediction model for new-onset atrial fibrillation after lung resection |
| title_full | Development and validation of a novel prediction model for new-onset atrial fibrillation after lung resection |
| title_fullStr | Development and validation of a novel prediction model for new-onset atrial fibrillation after lung resection |
| title_full_unstemmed | Development and validation of a novel prediction model for new-onset atrial fibrillation after lung resection |
| title_short | Development and validation of a novel prediction model for new-onset atrial fibrillation after lung resection |
| title_sort | development and validation of a novel prediction model for new onset atrial fibrillation after lung resection |
| topic | Postoperative atrial fibrillation lung resection prediction model meta-analysis |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2025.2519673 |
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