Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.
Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.
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DOI：
10.1161/circoutcomes.118.005595
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发表时间：
2019-10
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影响因子：
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通讯作者：
Turakhia MP
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文献类型：
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作者：
Han L;Askari M;Altman RB;Schmitt SK;Fan J;Bentley JP;Narayan SM;Turakhia MP
房颤（AF）使卒中风险增加5倍，人们越来越关注确定AF严重程度或负担是否可以进一步对这些患者进行风险分层，特别是对于近期事件。使用来自心脏植入式电子设备（CIED）的连续远程监测数据，我们试图评估与传统风险评分相比，机器学习的AF负荷特征是否可以提供卒中近期风险的预后信息。我们回顾性地确定了退伍军人健康管理局（VA）服务的患者CIED远程监测数据和至少一天的设备注册的AF。前30天的远程监测非中风对照与最后30天的远程监测中风前的情况进行了比较。我们在数据上训练了三种类型的模型：1）卷积神经网络（CNN），2）随机森林（RF）和3）L1正则化逻辑回归（LASSO）。我们计算了每例患者的CHA 2DS 2-VASc评分，并将其性能与基于不同测试队列中AF负担的机器学习指数进行了比较。最后，我们研究了将我们的AF负荷模型与CHA 2DS 2-VASc组合的效果。我们确定了3，114名非中风对照和71名中风病例，基线特征无显著差异。RF在测试数据集中表现最好（AUC=0.662），CNN在验证数据集中表现最好（AUC=0.702）;而CHA 2DS 2-VASc在两个数据集中的AUC均为0.5或更低。将CHA 2DS 2-VASc与随机森林和CNN相结合，得到了0.696的验证AUC和0.634的测试AUC，得到了非训练数据的最高平均AUC。这项概念验证研究发现，结合每日AF负荷特征的机器学习和集成方法为短期卒中风险的CHA 2DS 2-VASc以外的风险分层提供了增量预后价值。
Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitoring data from cardiac implantable electronic devices (CIED), we sought to evaluate if machine learned signatures of AF burden could provide prognostic information on near-term risk of stroke when compared to conventional risk scores. We retrospectively identified Veterans Health Administration (VA) serviced patients with CIED remote monitoring data and at least one day of device-registered AF. The first 30 days of remote monitoring in non-stroke controls were compared against the last 30 days of remote monitoring prior to stroke in cases. We trained three types of models on our data: 1) convolutional neural networks (CNN), 2) random forest (RF), and 3) L1 regularized logistic regression (LASSO). We calculated the CHA2DS2-VASc score for each patient and compared its performance against machine learned indices based on AF burden in separate test cohorts. Finally, we investigated the effect of combining our AF burden models with CHA2DS2-VASc. We identified 3,114 non-stroke controls and 71 stroke cases, with no significant differences in baseline characteristics. RF performed the best in the test dataset (AUC=0.662) and CNN in the validation dataset (AUC=0.702); whereas, CHA2DS2-VASc had an AUC of 0.5 or less in both datasets. Combining CHA2DS2-VASc with random forest and CNN yielded a validation AUC of 0.696 and test AUC of 0.634, yielding the highest average AUC on non-training data. This proof of concept study found that machine learning and ensemble methods that incorporate daily AF burden signature provided incremental prognostic value for risk stratification beyond CHA2DS2-VASc for near-term risk of stroke.