Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias.
Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias.
复制标题
从无心律失常的家庭单导联心电信号预测心房颤动。
DOI：
10.1038/s41746-023-00966-w
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发表时间：
2023-12-12
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
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作者：
房颤（AF）的早期识别可以降低中风、心力衰竭和其他严重心血管后果的风险。然而，即使连续监测两周，也可能无法检测到阵发性房颤。我们开发了一个模型来量化两周内近期房颤的风险，该模型基于459,889例基于补片的动态单导联心电图（改良导联II）记录的24小时无房颤心电图间隔长达14天。使用深度学习模型将ECG形态学数据与人口统计学和心律特征相结合，以预测房颤。观察1天无AF心电图记录，具有深度学习特征的模型对近期AF的预测最准确，曲线下面积AUC = 0.80(95%置信区间，CI = 0.79-0.81)，与单独的人口统计学指标相比，显著提高了识别能力（AUC 0.67; CI = 0.66-0.68）。基于不同长度的无AF单导联心电图记录，我们的模型能够在两周的时间框架内预测AF事件，具有很高的识别率。该模型的应用可以通过对房颤阴性门诊监测的个体进行风险分层，进行长时间或反复监测，从而实现提高房颤诊断捕获的数字化策略，从而可能导致更快速地开始治疗。
Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even after a two-week continuous monitoring period. We developed a model to quantify the risk of near-term AF in a two-week period, based on AF-free ECG intervals of up to 24 h from 459,889 patch-based ambulatory single-lead ECG (modified lead II) recordings of up to 14 days. A deep learning model was used to integrate ECG morphology data with demographic and heart rhythm features toward AF prediction. Observing a 1-day AF-free ECG recording, the model with deep learning features produced the most accurate prediction of near-term AF with an area under the curve AUC = 0.80 (95% confidence interval, CI = 0.79–0.81), significantly improving discrimination compared to demographic metrics alone (AUC 0.67; CI = 0.66–0.68). Our model was able to predict incident AF over a two-week time frame with high discrimination, based on AF-free single-lead ECG recordings of various lengths. Application of the model may enable a digital strategy for improving diagnostic capture of AF by risk stratifying individuals with AF-negative ambulatory monitoring for prolonged or recurrent monitoring, potentially leading to more rapid initiation of treatment.
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影响因子：
37.8
作者：
Khurshid S;Friedman S;Reeder C;Di Achille P;Diamant N;Singh P;Harrington LX;Wang X;Al-Alusi MA;Sarma G;Foulkes AS;Ellinor PT;Anderson CD;Ho JE;Philippakis AA;Batra P;Lubitz SA
通讯作者：
Lubitz SA
影响因子：
37.8
作者：
Raghunath S;Pfeifer JM;Ulloa-Cerna AE;Nemani A;Carbonati T;Jing L;vanMaanen DP;Hartzel DN;Ruhl JA;Lagerman BF;Rocha DB;Stoudt NJ;Schneider G;Johnson KW;Zimmerman N;Leader JB;Kirchner HL;Griessenauer CJ;Hafez A;Good CW;Fornwalt BK;Haggerty CM
通讯作者：
Haggerty CM
影响因子：
7.7
作者：
Ivaturi P;Gadaleta M;Pandey AC;Pazzani M;Steinhubl SR;Quer G
通讯作者：
Quer G
DOI：
10.1093/europace/euaa186
发表时间：
2020-12-23
期刊：
Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
影响因子：
--
作者：
Quer G;Freedman B;Steinhubl SR
通讯作者：
Steinhubl SR
影响因子：
3.7
作者：
Jaakkola, Jussi;Mustonen, Pirjo;Airaksinen, K. E. Juhani
通讯作者：
Airaksinen, K. E. Juhani