PDF(Pubmed)
Abstract：
Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an important risk factor for ischemic cerebrovascular events. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset AF that incorporated the use electrocardiogram to diagnose AF, data from participants with a wide age range, and considered hypertension and measures of atrial stiffness. In Japan, Industrial Safety and Health Law requires employers to provide annual health check-ups to their employees. This study included 13 410 individuals who underwent health check-ups on at least four successive years between 2005 and 2015 (new-onset AF, n = 110; non-AF, n = 13 300). Data were entered into a risk prediction model using machine learning methods (eXtreme Gradient Boosting and Shapley Additive Explanation values). Data were randomly split into a training set (80%) used for model construction and development, and a test set (20%) used to test performance of the derived model. The area under the receiver operator characteristic curve for the model in the test set was 0.789. The best predictor of new-onset AF was age, followed by the cardio-ankle vascular index, estimated glomerular filtration rate, sex, body mass index, uric acid, γ-glutamyl transpeptidase level, triglycerides, systolic blood pressure at cardio-ankle vascular index measurement, and alanine aminotransferase level. This new model including arterial stiffness measure, developed with data from a general population using machine learning methods, could be used to identify at-risk individuals and potentially facilitation the prevention of future AF development.
摘要：
心房颤动(房颤)是临床上最常见的有意义的心律失常,是缺血性脑血管事件的重要危险因素。这项研究使用机器学习技术来开发和验证新发作房颤的新风险预测模型，该模型结合了使用心电图来诊断房颤。来自年龄范围广泛的参与者的数据，并考虑高血压和心房僵硬度的测量。在日本,《工业安全与健康法》要求雇主为员工提供年度健康检查。这项研究纳入了2005年至2015年期间至少连续四年接受健康检查的13410人（新发房颤，n=110；非AF，n=13300)。使用机器学习方法(极限梯度提升和Shapley加法解释值)将数据输入到风险预测模型中。数据被随机分成一个训练集(80%)，用于模型构建和开发，以及用于测试派生模型性能的测试集(20%)。测试集中模型的接收器操作员特征曲线下面积为0.789。新发房颤的最佳预测因素是年龄,其次是心踝血管指数,估计肾小球滤过率，性别,身体质量指数,尿酸,γ-谷氨酰转肽酶水平,甘油三酯，心-踝血管指数测量时的收缩压，丙氨酸转氨酶水平.这种新模型包括动脉僵硬度测量，使用机器学习方法用普通人群的数据开发，可用于识别有风险的个体，并可能促进预防未来的房颤发展。
