Department of Epidemiology, Erasmus MC University Medical Center, Office Na-2714, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Clin Res Cardiol. 2023 Jun;112(6):736-746. doi: 10.1007/s00392-022-02071-6. Epub 2022 Aug 10.
Clinical guidelines categorize atrial fibrillation (AF) based on the temporality of AF events. Due to its dependence on event duration, this classification is not applicable to population-based cohort settings. We aimed to develop a simple and standardized method to classify AF patterns at population level. Additionally, we compared the longitudinal trajectories of cardiovascular risk factors preceding the AF patterns, and between men and women.
Between 1990 and 2014, participants from the population-based Rotterdam study were followed for AF status, and categorized into 'single-documented AF episode', 'multiple-documented AF episodes', or 'long-standing persistent AF'. Using repeated measurements we created linear mixed-effects models to assess the longitudinal evolution of risk factors prior to AF diagnosis.
We included 14,061 participants (59.1% women, mean age 65.4 ± 10.2 years). After a median follow-up of 9.4 years (interquartile range 8.27), 1,137 (8.1%) participants were categorized as 'single-documented AF episode', 208 (1.5%) as 'multiple-documented AF episodes', and 57 (0.4%) as 'long-standing persistent AF'. In men, we found poorer trajectories of weight and waist circumference preceding 'long-standing persistent AF' as compared to the other patterns. In women, we found worse trajectories of all risk factors between 'long-standing persistent AF' and the other patterns.
We developed a standardized method to classify AF patterns in the general population. Participants categorized as 'long-standing persistent AF' showed poorer trajectories of cardiovascular risk factors prior to AF diagnosis, as compared to the other patterns. Our findings highlight sex differences in AF pathophysiology and provide insight into possible risk factors of AF patterns.
临床指南根据房颤（AF）事件的时间性对房颤进行分类。由于其依赖于事件持续时间，这种分类不适用于基于人群的队列设置。我们旨在开发一种简单且标准化的方法，用于在人群水平上对房颤模式进行分类。此外，我们比较了房颤模式之前心血管危险因素的纵向轨迹，以及男女之间的差异。
在 1990 年至 2014 年期间，来自基于人群的鹿特丹研究的参与者接受了房颤状态的随访，并分为“单次记录的房颤发作”、“多次记录的房颤发作”或“持续性房颤”。我们使用重复测量建立了线性混合效应模型，以评估房颤诊断前危险因素的纵向演变。
我们纳入了 14061 名参与者（59.1%为女性，平均年龄 65.4±10.2 岁）。在中位数为 9.4 年（四分位距 8.27）的随访后，1137 名（8.1%）参与者被归类为“单次记录的房颤发作”，208 名（1.5%）为“多次记录的房颤发作”，57 名（0.4%）为“持续性房颤”。在男性中，与其他模式相比，我们发现“持续性房颤”患者在房颤诊断前体重和腰围的轨迹较差。在女性中，我们发现“持续性房颤”与其他模式之间所有危险因素的轨迹更差。
我们开发了一种标准化方法来对一般人群中的房颤模式进行分类。与其他模式相比，归类为“持续性房颤”的参与者在房颤诊断前的心血管危险因素轨迹较差。我们的研究结果强调了房颤病理生理学中的性别差异，并提供了对房颤模式可能的危险因素的深入了解。
