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A nomogram for predicting atrial fibrillation detected after acute ischemic stroke
BACKGROUND: Atrial fibrillation detected after stroke (AFDAS) is associated with an increased risk of ischemic stroke (IS) recurrence and death. Early diagnosis can help identify strategies for secondary prevention and improve prognosis. However, there are no validated predictive tools to assess the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614087/ https://www.ncbi.nlm.nih.gov/pubmed/36313507 http://dx.doi.org/10.3389/fneur.2022.1005885 |
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author | Pang, Ming Li, Zhuanyun Sun, Lin Zhao, Na Hao, Lina |
author_facet | Pang, Ming Li, Zhuanyun Sun, Lin Zhao, Na Hao, Lina |
author_sort | Pang, Ming |
collection | PubMed |
description | BACKGROUND: Atrial fibrillation detected after stroke (AFDAS) is associated with an increased risk of ischemic stroke (IS) recurrence and death. Early diagnosis can help identify strategies for secondary prevention and improve prognosis. However, there are no validated predictive tools to assess the population at risk for AFDAS. Therefore, this study aimed to develop and validate a predictive model for assessing the incidence of AFDAS after acute ischemic stroke (AIS). METHODS: This study was a multicenter retrospective study. We collected clinical data from 5332 patients with AIS at two hospitals between 2014.01 and 2021.12 and divided the development and validation of clinical prediction models into a training cohort (n = 3173) and a validation cohort (n = 2159). Characteristic variables were selected from the training cohort using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariable logistic regression analysis. A nomogram model was developed, and its performance was evaluated regarding calibration, discrimination, and clinical utility. RESULTS: We found the best subset of risk factors based on clinical characteristics and laboratory variables, including age, congestive heart failure (CHF), previous AIS/transient ischemia attack (TIA), national institutes of health stroke scale (NIHSS) score, C-reactive protein (CRP), and B-type natriuretic peptide (BNP). A predictive model was developed. The model showed good calibration and discrimination, with calibration values of Hosmer-Lemeshow χ(2) = 4.813, P = 0.732 and Hosmer-Lemeshow χ(2) = 4.248, P = 0.834 in the training and validation cohorts, respectively. The area under the ROC curve (AUC) was 0.815, 95% CI (0.777–0.853) and 0.808, 95% CI (0.770–0.847). The inclusion of neuroimaging variables significantly improved the performance of the integrated model in both the training cohort (AUC. 0.846 (0.811–0.882) vs. 0.815 (0.777–0.853), P = 0.001) and the validation cohort (AUC: 0.841 (0.804–0.877) vs. 0.808 (0.770–0.847), P = 0.001). The decision curves showed that the integrated model added more net benefit in predicting the incidence of AFDAS. CONCLUSION: Predictive models based on clinical characteristics, laboratory variables, and neuroimaging variables showed good calibration and high net clinical benefit, informing clinical decision-making in diagnosing and treating patients with AFDAS. |
format | Online Article Text |
id | pubmed-9614087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96140872022-10-29 A nomogram for predicting atrial fibrillation detected after acute ischemic stroke Pang, Ming Li, Zhuanyun Sun, Lin Zhao, Na Hao, Lina Front Neurol Neurology BACKGROUND: Atrial fibrillation detected after stroke (AFDAS) is associated with an increased risk of ischemic stroke (IS) recurrence and death. Early diagnosis can help identify strategies for secondary prevention and improve prognosis. However, there are no validated predictive tools to assess the population at risk for AFDAS. Therefore, this study aimed to develop and validate a predictive model for assessing the incidence of AFDAS after acute ischemic stroke (AIS). METHODS: This study was a multicenter retrospective study. We collected clinical data from 5332 patients with AIS at two hospitals between 2014.01 and 2021.12 and divided the development and validation of clinical prediction models into a training cohort (n = 3173) and a validation cohort (n = 2159). Characteristic variables were selected from the training cohort using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariable logistic regression analysis. A nomogram model was developed, and its performance was evaluated regarding calibration, discrimination, and clinical utility. RESULTS: We found the best subset of risk factors based on clinical characteristics and laboratory variables, including age, congestive heart failure (CHF), previous AIS/transient ischemia attack (TIA), national institutes of health stroke scale (NIHSS) score, C-reactive protein (CRP), and B-type natriuretic peptide (BNP). A predictive model was developed. The model showed good calibration and discrimination, with calibration values of Hosmer-Lemeshow χ(2) = 4.813, P = 0.732 and Hosmer-Lemeshow χ(2) = 4.248, P = 0.834 in the training and validation cohorts, respectively. The area under the ROC curve (AUC) was 0.815, 95% CI (0.777–0.853) and 0.808, 95% CI (0.770–0.847). The inclusion of neuroimaging variables significantly improved the performance of the integrated model in both the training cohort (AUC. 0.846 (0.811–0.882) vs. 0.815 (0.777–0.853), P = 0.001) and the validation cohort (AUC: 0.841 (0.804–0.877) vs. 0.808 (0.770–0.847), P = 0.001). The decision curves showed that the integrated model added more net benefit in predicting the incidence of AFDAS. CONCLUSION: Predictive models based on clinical characteristics, laboratory variables, and neuroimaging variables showed good calibration and high net clinical benefit, informing clinical decision-making in diagnosing and treating patients with AFDAS. Frontiers Media S.A. 2022-10-14 /pmc/articles/PMC9614087/ /pubmed/36313507 http://dx.doi.org/10.3389/fneur.2022.1005885 Text en Copyright © 2022 Pang, Li, Sun, Zhao and Hao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Pang, Ming Li, Zhuanyun Sun, Lin Zhao, Na Hao, Lina A nomogram for predicting atrial fibrillation detected after acute ischemic stroke |
title | A nomogram for predicting atrial fibrillation detected after acute ischemic stroke |
title_full | A nomogram for predicting atrial fibrillation detected after acute ischemic stroke |
title_fullStr | A nomogram for predicting atrial fibrillation detected after acute ischemic stroke |
title_full_unstemmed | A nomogram for predicting atrial fibrillation detected after acute ischemic stroke |
title_short | A nomogram for predicting atrial fibrillation detected after acute ischemic stroke |
title_sort | nomogram for predicting atrial fibrillation detected after acute ischemic stroke |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614087/ https://www.ncbi.nlm.nih.gov/pubmed/36313507 http://dx.doi.org/10.3389/fneur.2022.1005885 |
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