Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome
Objective. Atrial fibrillation (AF) is one of the most common complications of acute coronary syndrome (ACS) patients. Possible risk factors related to new-onset AF (NOAF) in ACS patients have been reported in some studies, and several prediction models have been established. However, the predictive...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Journal of Clinical Practice |
| Online Access: | http://dx.doi.org/10.1155/2023/3473603 |
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| _version_ | 1832546744707055616 |
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| author | Na Wu Junzheng Li Xiang Xu Zhiquan Yuan Lili Yang Yanxiu Chen Tingting Xia Qin Hu Zheng Chen Chengying Li Ying Xiang Zhihui Zhang Li Zhong Yafei Li |
| author_facet | Na Wu Junzheng Li Xiang Xu Zhiquan Yuan Lili Yang Yanxiu Chen Tingting Xia Qin Hu Zheng Chen Chengying Li Ying Xiang Zhihui Zhang Li Zhong Yafei Li |
| author_sort | Na Wu |
| collection | DOAJ |
| description | Objective. Atrial fibrillation (AF) is one of the most common complications of acute coronary syndrome (ACS) patients. Possible risk factors related to new-onset AF (NOAF) in ACS patients have been reported in some studies, and several prediction models have been established. However, the predictive power of these models was modest and lacked independent validation. The aim of this study is to define risk factors of NOAF in patients with ACS during hospitalization and to develop a prediction model and nomogram for individual risk prediction. Methods. Retrospective cohort studies were conducted. A total of 1535 eligible ACS patients from one hospital were recruited for model development. External validation was performed using an external cohort of 1635 ACS patients from another hospital. The prediction model was created using multivariable logistic regression and validated in an external cohort. The discrimination, calibration, and clinical utility of the model were evaluated, and a nomogram was constructed. A subgroup analysis was performed for unstable angina (UA) patients. Results. During hospitalization, the incidence of NOAF was 8.21% and 6.12% in the training and validation cohorts, respectively. Age, admission heart rate, left atrial diameter, right atrial diameter, heart failure, brain natriuretic peptide (BNP) level, less statin use, and no percutaneous coronary intervention (PCI) were independent predictors of NOAF. The AUC was 0.891 (95% CI: 0.863–0.920) and 0.839 (95% CI: 0.796–0.883) for the training and validation cohort, respectively, and the model passed the calibration test (P>0.05). The clinical utility evaluation shows that the model has a clinical net benefit within a certain range of the threshold probability. Conclusion. A model with strong predictive power was constructed for predicting the risk of NOAF in patients with ACS during hospitalization. It might help with the identification of ACS patients at risk and early intervention of NOAF during hospitalization. |
| format | Article |
| id | doaj-art-884db6bc28c443e0b1feca3646071df3 |
| institution | Kabale University |
| issn | 1742-1241 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Clinical Practice |
| spelling | doaj-art-884db6bc28c443e0b1feca3646071df32025-02-03T06:47:17ZengWileyInternational Journal of Clinical Practice1742-12412023-01-01202310.1155/2023/3473603Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary SyndromeNa Wu0Junzheng Li1Xiang Xu2Zhiquan Yuan3Lili Yang4Yanxiu Chen5Tingting Xia6Qin Hu7Zheng Chen8Chengying Li9Ying Xiang10Zhihui Zhang11Li Zhong12Yafei Li13Department of EpidemiologyDepartment of EpidemiologyDepartment of Cardiology and the Center for Circadian Metabolism and Cardiovascular DiseaseDepartment of EpidemiologyDepartment of InformationDepartment of Cardiology and the Center for Circadian Metabolism and Cardiovascular DiseaseDepartment of EpidemiologyDepartment of EpidemiologyDepartment of EpidemiologyDepartment of EpidemiologyDepartment of EpidemiologyDepartment of Cardiology and the Center for Circadian Metabolism and Cardiovascular DiseaseCardiovascular Disease CenterDepartment of EpidemiologyObjective. Atrial fibrillation (AF) is one of the most common complications of acute coronary syndrome (ACS) patients. Possible risk factors related to new-onset AF (NOAF) in ACS patients have been reported in some studies, and several prediction models have been established. However, the predictive power of these models was modest and lacked independent validation. The aim of this study is to define risk factors of NOAF in patients with ACS during hospitalization and to develop a prediction model and nomogram for individual risk prediction. Methods. Retrospective cohort studies were conducted. A total of 1535 eligible ACS patients from one hospital were recruited for model development. External validation was performed using an external cohort of 1635 ACS patients from another hospital. The prediction model was created using multivariable logistic regression and validated in an external cohort. The discrimination, calibration, and clinical utility of the model were evaluated, and a nomogram was constructed. A subgroup analysis was performed for unstable angina (UA) patients. Results. During hospitalization, the incidence of NOAF was 8.21% and 6.12% in the training and validation cohorts, respectively. Age, admission heart rate, left atrial diameter, right atrial diameter, heart failure, brain natriuretic peptide (BNP) level, less statin use, and no percutaneous coronary intervention (PCI) were independent predictors of NOAF. The AUC was 0.891 (95% CI: 0.863–0.920) and 0.839 (95% CI: 0.796–0.883) for the training and validation cohort, respectively, and the model passed the calibration test (P>0.05). The clinical utility evaluation shows that the model has a clinical net benefit within a certain range of the threshold probability. Conclusion. A model with strong predictive power was constructed for predicting the risk of NOAF in patients with ACS during hospitalization. It might help with the identification of ACS patients at risk and early intervention of NOAF during hospitalization.http://dx.doi.org/10.1155/2023/3473603 |
| spellingShingle | Na Wu Junzheng Li Xiang Xu Zhiquan Yuan Lili Yang Yanxiu Chen Tingting Xia Qin Hu Zheng Chen Chengying Li Ying Xiang Zhihui Zhang Li Zhong Yafei Li Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome International Journal of Clinical Practice |
| title | Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome |
| title_full | Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome |
| title_fullStr | Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome |
| title_full_unstemmed | Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome |
| title_short | Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome |
| title_sort | prediction model of new onset atrial fibrillation in patients with acute coronary syndrome |
| url | http://dx.doi.org/10.1155/2023/3473603 |
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