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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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Autores principales: Wu, Na, Li, Junzheng, Xu, Xiang, Yuan, Zhiquan, Yang, Lili, Chen, Yanxiu, Xia, Tingting, Hu, Qin, Chen, Zheng, Li, Chengying, Xiang, Ying, Zhang, Zhihui, Zhong, Li, Li, Yafei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981295/
https://www.ncbi.nlm.nih.gov/pubmed/36874383
http://dx.doi.org/10.1155/2023/3473603
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author Wu, Na
Li, Junzheng
Xu, Xiang
Yuan, Zhiquan
Yang, Lili
Chen, Yanxiu
Xia, Tingting
Hu, Qin
Chen, Zheng
Li, Chengying
Xiang, Ying
Zhang, Zhihui
Zhong, Li
Li, Yafei
author_facet Wu, Na
Li, Junzheng
Xu, Xiang
Yuan, Zhiquan
Yang, Lili
Chen, Yanxiu
Xia, Tingting
Hu, Qin
Chen, Zheng
Li, Chengying
Xiang, Ying
Zhang, Zhihui
Zhong, Li
Li, Yafei
author_sort Wu, Na
collection PubMed
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.
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spelling pubmed-99812952023-03-03 Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome Wu, Na Li, Junzheng Xu, Xiang Yuan, Zhiquan Yang, Lili Chen, Yanxiu Xia, Tingting Hu, Qin Chen, Zheng Li, Chengying Xiang, Ying Zhang, Zhihui Zhong, Li Li, Yafei Int J Clin Pract Research Article 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. Hindawi 2023-02-23 /pmc/articles/PMC9981295/ /pubmed/36874383 http://dx.doi.org/10.1155/2023/3473603 Text en Copyright © 2023 Na Wu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Na
Li, Junzheng
Xu, Xiang
Yuan, Zhiquan
Yang, Lili
Chen, Yanxiu
Xia, Tingting
Hu, Qin
Chen, Zheng
Li, Chengying
Xiang, Ying
Zhang, Zhihui
Zhong, Li
Li, Yafei
Prediction Model of New Onset Atrial Fibrillation in Patients with Acute Coronary Syndrome
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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981295/
https://www.ncbi.nlm.nih.gov/pubmed/36874383
http://dx.doi.org/10.1155/2023/3473603
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