Ding Mozhu, Murata Shunsuke, Louro Javier, Hammar Niklas, Modig Karin
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Open Heart. 2025 Aug 22;12(2):e003451. doi: 10.1136/openhrt-2025-003451.
There is a lack of atrial fibrillation (AF) prediction models tailored for individuals without prior cardiovascular diseases (CVDs) to facilitate early intervention. This study aimed to develop and validate an AF prediction model using machine-learning methods based on routine biomarkers in middle-aged individuals without overt CVD.
Data were derived from 122 822 individuals in the Swedish AMORIS (Apolipoprotein-Mortality Risk) cohort who were aged 40-65 years and without CVD diagnosis at baseline (1985-96) and followed for 20 years for incident AF. The sample was split into training and validation data sets. Random forest was used to identify AF predictors from 16 routine biomarkers covering lipids, liver/kidney markers, glucose control and inflammation.
10 356 (8.4%) incident AF diagnosis occurred over a mean of 18.1 years (SD 4.4). Model performance increased sharply when adding the first seven predictors and plateaued when adding additional ones. Therefore, a final AF prediction model was established based on seven predictors: age, albumin, uric acid, triglycerides, glucose, alkaline phosphatase and sex. C-statistics of the final model were 0.82 (95% CI: 0.81 to 0.82) in the training and 0.71 (0.70 to 0.72) in the validation data set in predicting 20-year AF. The model was well-calibrated in the full sample and age and sex subgroups.
A new AF prediction model was established using seven biomarkers from a population without pre-existing CVDs, thus complementing currently available AF prediction models. These markers are readily accessible in primary and specialist care and demonstrate acceptable performance in predicting short- and long-term AF risk.
缺乏专门针对无心血管疾病（CVD）病史个体的房颤（AF）预测模型，难以实现早期干预。本研究旨在基于中年无明显CVD个体的常规生物标志物，利用机器学习方法开发并验证AF预测模型。
数据来源于瑞典AMORIS（载脂蛋白-死亡率风险）队列中的122822名个体，年龄在40 - 65岁之间，基线时（1985 - 1996年）无CVD诊断，随访20年观察房颤发生情况。样本被分为训练集和验证集。随机森林用于从涵盖脂质、肝/肾标志物、血糖控制和炎症的16种常规生物标志物中识别AF预测因子。
在平均18.1年（标准差4.4）的随访期内，共发生10356例（8.4%）房颤诊断。添加前七个预测因子时，模型性能急剧提升，增加更多预测因子时趋于平稳。因此，基于七个预测因子建立了最终的AF预测模型：年龄、白蛋白、尿酸、甘油三酯、血糖、碱性磷酸酶和性别。在预测20年房颤时，最终模型在训练集中的C统计量为0.82（95%CI：0.81至0.82），在验证集中为0.71（0.70至0.72）。该模型在全样本以及年龄和性别亚组中校准良好。
利用无CVD病史人群的七种生物标志物建立了新的AF预测模型，从而补充了现有的AF预测模型。这些标志物在基层医疗和专科医疗中易于获取，在预测短期和长期AF风险方面表现出可接受的性能。
