Pang Ming, Li Zhuanyun, Sun Lin, Zhao Na, Hao Lina
Neuroelectrophysiology Room, Function Department, Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Cangzhou, China.
Department of Emergency Medicine, Union Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Neurol. 2022 Oct 14;13:1005885. doi: 10.3389/fneur.2022.1005885. eCollection 2022.
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).
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 ( = 3173) and a validation cohort ( = 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.
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 = 4.813, = 0.732 and Hosmer-Lemeshow χ = 4.248, = 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), = 0.001) and the validation cohort (AUC: 0.841 (0.804-0.877) vs. 0.808 (0.770-0.847), = 0.001). The decision curves showed that the integrated model added more net benefit in predicting the incidence of AFDAS.
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.
卒中后房颤（AFDAS）与缺血性卒中（IS）复发及死亡风险增加相关。早期诊断有助于确定二级预防策略并改善预后。然而，尚无经过验证的预测工具来评估AFDAS的高危人群。因此，本研究旨在开发并验证一种预测模型，以评估急性缺血性卒中（AIS）后AFDAS的发生率。
本研究为多中心回顾性研究。我们收集了2014年1月至2021年12月期间两家医院5332例AIS患者的临床数据，并将临床预测模型的开发和验证分为训练队列（n = 3173）和验证队列（n = 2159）。使用最小绝对收缩和选择算子（LASSO）算法及多变量逻辑回归分析从训练队列中选择特征变量。开发了列线图模型，并对其校准、区分度和临床实用性进行了评估。
我们基于临床特征和实验室变量找到了最佳风险因素子集，包括年龄、充血性心力衰竭（CHF）、既往AIS/短暂性脑缺血发作（TIA）、美国国立卫生研究院卒中量表（NIHSS）评分、C反应蛋白（CRP）和B型利钠肽（BNP）。开发了一种预测模型。该模型显示出良好的校准和区分度，训练队列和验证队列中的Hosmer-Lemeshow χ²值分别为4.813，P = 0.732和4.248，P = 0.834。ROC曲线下面积（AUC）分别为0.815，95%CI（0.777 - 0.853）和0.808，95%CI（0.770 - 0.847）。纳入神经影像学变量显著改善了综合模型在训练队列（AUC：0.846（0.811 - 0.882）对0.815（0.777 - 0.853），P = 0.001）和验证队列（AUC：0.841（0.804 - 0.877）对0.808（0.770 - 0.847），P = 0.001）中的性能。决策曲线表明，综合模型在预测AFDAS发生率方面增加了更多净效益。
基于临床特征、实验室变量和神经影像学变量的预测模型显示出良好的校准和较高的净临床效益，为AFDAS患者的诊断和治疗提供临床决策依据。
