心房颤动风险预测模型的评估（来自动脉粥样硬化多种族研究[MESA]）

       BACKGROUND

Atrial fibrillation (AF) is prevalent and strongly associated with higher cardiovascular disease (CVD) risk. Machine learning is increasingly used to identify novel predictors of CVD risk, but prediction improvements beyond established risk scores are uncertain.

房颤（AF）普遍存在，并与较高的心血管疾病（CVD）风险密切相关。机器学习算法已越来越被用于识别CVD风险因素，但未确定既定风险评分之外的预测改进方法。

METHODS

We evaluated improvements in predicting 5-year AF risk when adding novel candidate variables identified by machine learning to the CHARGE-AF Enriched score, which includes age, race/ethnicity, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes, and NT-proBNP. We included 3,534 participants (mean age, 61.3 years，52.0% female) with complete data from the prospective Multi-Ethnic Study of Atherosclerosis. Incident AF was defined based on study electrocardiograms and hospital discharge diagnosis ICD-9 codes. Prediction performance was evaluated using Cox regression and a parsimonious model was selected using LASSO.

       我们通过机器学习识别，在CHARGE-AF Enriched分数中添加了的新候选变量时，评估了预测5年房颤风险改善情况，该变量包括年龄、种族/族裔、身高、体重、收缩压和舒张压、当前吸烟情况、抗高血压药物使用、糖尿病和脑钠肽数量。我们招募了3,534名参与者（平均年龄61.3岁，女性占52.0％），并有前瞻性动脉粥样硬化多种族研究的完整数据。我们通过机器学习识别，在CHARGE-AF Enriched分数中添加了的新候选变量时，评估了预测5年房颤风险改善情况，该变量包括年龄、种族/族裔、身高、体重、收缩压和舒张压、当前吸烟情况、抗高血压药物使用、糖尿病和脑钠肽数量。我们招募了3,534名参与者（平均年龄61.3岁，女性占52.0％），并有前瞻性动脉粥样硬化多种族研究的完整数据。

       RESULTS

Within 5 years of baseline, 124 participants had incident AF. Compared with the CHARGE-AF Enriched model (c-statistic, 0.804), variables identified by machine learning, including biomarkers, cardiac magnetic resonance imaging variables, electrocardiogram variables, and subclinical CVD variables, did not significantly improve prediction. A 23-item score derived by machine learning achieved a c-statistic of 0.806, whereas a parsimonious model including the clinical risk factors age, weight, current smoking, NT-proBNP, coronary artery calcium score, and cardiac troponin-T achieved a c-statistic of 0.802. This analysis confirms that the CHARGE-AF Enriched model and a parsimonious 6-item model performed similarly to a more extensive model derived by machine learning.

在基线的5年内，有124位参与者发生了房颤。和CHARGE-AF Enriched模型（c-统计量，0.804）相比，通过机器学习识别，包括生物标志物、心脏磁共振成像变量、心电图变量和亚临床CVD变量在内的变量未明显改善预测。机器学习得出的23个项目评分的c-统计量为0.806，而包括临床风险因素、年龄、体重、当前吸烟、脑钠肽、冠状动脉钙化评分和心肌肌钙蛋白T的简化模型达到了0.802c-统计量。该分析证实，CHARGE-AF Enriched模型及简约6项模型和机器学习得到更广泛的模型作用相似。

CONCLUSIONS

In conclusion, these simple models remain the gold standard for risk prediction of AF, although addition of the coronary artery calcium score should be considered.

总之，尽管应考虑增加冠状动脉钙评分，这些简易模型仍是预测房颤风险的金标准。