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新发心房颤动预测模型的研究进展——基于经典心血管危险因素
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Abstract:
心房颤动(atrial fibrillation, AF)是最常见的持续性心律失常，显著增加死亡、心力衰竭、卒中、痴呆和认知功能障碍风险。临床上有相当一部分房颤患者没有症状，仅在查体或发生并发症时，才被发现罹患房颤。预测模型是房颤高风险人群筛选的有效工具，对该人群进行更积极的监控及制定预防措施能够带来临床获益。不同预测模型来源人群不同，最佳适用人群也不同，本文总结了基于经典心血管危险因素的房颤预测模型，现旨在针对不同适用人群的房颤预测模型进行讨论，以期为临床医务人员提供指导。
Atrial fibrillation (AF) is the most common persistent arrhythmia, which significantly increases risk of death, heart failure, stroke, dementia and cognitive dysfunction. A significant proportion of patients with AF have no symptoms in clinic, and they are not found AF until physical examination or complications occur. The predictive models of atrial fibrillation can help screen patients with high risk of atrial fibrillation, and clinical benefits could be brought with more aggressive monitoring and preventive measures. Different prediction models come from different people and are suitable for different people. This paper summarizes the research progress of atrial fibrillation prediction models based on classical cardiovascular risk factors. This paper aims to discuss the prediction models of atrial fibrillation for different applicable populations, in order to provide guidance for clinicians.
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