Yao Yuchen, Zhang Michael J, Wang Wendy, Zhuang Zhong, He Ruoyu, Ji Yuekai, Knutson Katherine A, Norby Faye L, Alonso Alvaro, Soliman Elsayed Z, Tang Weihong, Pankow James S, Pan Wei, Chen Lin Yee
School of Statistics, College of Liberal Arts, University of Minnesota, 313 Church Street SE, Minneapolis, MN 55455, USA.
Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, 2221 University Ave SE, Minneapolis, MN 55414, USA.
Eur Heart J Digit Health. 2024 Nov 4;6(1):126-136. doi: 10.1093/ehjdh/ztae081. eCollection 2025 Jan.
Many studies have utilized data sources such as clinical variables, polygenic risk scores, electrocardiogram (ECG), and plasma proteins to predict the risk of atrial fibrillation (AF). However, few studies have integrated all four sources from a single study to comprehensively assess AF prediction.
We included 8374 (Visit 3, 1993-95) and 3730 (Visit 5, 2011-13) participants from the Atherosclerosis Risk in Communities Study to predict incident AF and prevalent (but covert) AF. We constructed a (i) clinical risk score using CHARGE-AF clinical variables, (ii) polygenic risk score using pre-determined weights, (iii) protein risk score using regularized logistic regression, and (iv) ECG risk score from a convolutional neural network. Risk prediction performance was measured using regularized logistic regression. After a median follow-up of 15.1 years, 1910 AF events occurred since Visit 3 and 229 participants had prevalent AF at Visit 5. The area under curve (AUC) improved from 0.660 to 0.752 (95% CI, 0.741-0.763) and from 0.737 to 0.854 (95% CI, 0.828-0.880) after addition of the polygenic risk score to the CHARGE-AF clinical variables for predicting incident and prevalent AF, respectively. Further addition of ECG and protein risk scores improved the AUC to 0.763 (95% CI, 0.753-0.772) and 0.875 (95% CI, 0.851-0.899) for predicting incident and prevalent AF, respectively.
A combination of clinical and polygenic risk scores was the most effective and parsimonious approach to predicting AF. Further addition of an ECG risk score or protein risk score provided only modest incremental improvement for predicting AF.
许多研究利用临床变量、多基因风险评分、心电图（ECG）和血浆蛋白等数据源来预测心房颤动（AF）的风险。然而，很少有研究从单一研究中整合所有这四个数据源来全面评估AF预测。
我们纳入了社区动脉粥样硬化风险研究中的8374名参与者（第3次访视，1993 - 1995年）和3730名参与者（第5次访视，2011 - 2013年），以预测新发AF和现患（但隐匿性）AF。我们构建了：（i）使用CHARGE - AF临床变量的临床风险评分，（ii）使用预定权重的多基因风险评分，（iii）使用正则化逻辑回归的蛋白风险评分，以及（iv）来自卷积神经网络的ECG风险评分。使用正则化逻辑回归测量风险预测性能。在中位随访15.1年后，自第3次访视以来发生了1910次AF事件，并且在第5次访视时有229名参与者患有现患AF。在将多基因风险评分添加到CHARGE - AF临床变量中分别用于预测新发和现患AF后，曲线下面积（AUC）从0.660提高到0.752（95%CI，0.741 - 0.763）以及从0.737提高到0.854（95%CI，0.828 - 0.880）。进一步添加ECG和蛋白风险评分后，预测新发和现患AF的AUC分别提高到0.763（95%CI，0.753 - 0.772）和0.875（95%CI，0.851 - 0.899）。
临床和多基因风险评分的组合是预测AF最有效且最简洁的方法。进一步添加ECG风险评分或蛋白风险评分在预测AF方面仅提供了适度的增量改善。
