A nomogram for predicting atrial fibrillation detected after acute ischemic stroke

Ming Pang; Zhuanyun Li; Lin Sun; Na Zhao; Lina Hao

doi:10.3389/fneur.2022.1005885

A nomogram for predicting atrial fibrillation detected after acute ischemic stroke

Front Neurol. 2022 Oct 14:13:1005885. doi: 10.3389/fneur.2022.1005885. eCollection 2022.

Authors

Ming Pang¹, Zhuanyun Li², Lin Sun¹, Na Zhao³, Lina Hao¹

Affiliations

¹ Neuroelectrophysiology Room, Function Department, Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Cangzhou, China.
² Department of Emergency Medicine, Union Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Department of Neurology, Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Cangzhou, China.

Abstract

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 χ² = 4.813, P = 0.732 and Hosmer-Lemeshow χ² = 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.

Keywords: acute ischemic stroke; atrial fibrillation; nomogram; prediction model; risk factors.