现在的位置: 首页时讯速递, 进展交流>正文
[Lancet在线发表]:心电图AI算法鉴别窦性心律时的房颤
2019年08月21日 时讯速递, 进展交流 暂无评论

ARTICLES|ONLINE FIRST

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

Zachi I Attia, Peter A Noseworthy, Francisco Lopez-Jimenez, et al

Lancet Published:August 01, 2019 DOI:https://doi.org/10.1016/S0140-6736(19)31721-0

Summary

Background 背景

Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning.

房颤常常没有临床症状,因而难以发现,但伴随卒中、心衰和死亡风险增加。现有的筛查方法需要通过长时间监测实现,受到费用较高及检出率低等因素的局限。我们旨在通过机器学习建立一种快速、便宜的床旁监测方法,鉴别房颤患者。

Methods 方法

We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, specificity, and F1 score with two-sided 95% CIs.

我们采用卷积神经网络方法建立ECG的人工智能(AI)算法,通过标准的10秒12导ECG,在正常窦性心律时检测房颤的ECG特征。我们纳入1993年12月31日至2017年7月21日间Mayo诊所ECG实验室进行ECG检查的所有18岁以上患者。患者在仰卧位时接受ECG检查,且至少有一个数字化的10秒钟12导联标准ECG检查结果。在心脏内科专家监督下,经过培训的人员确认ECG节律。我们将至少一个ECG显示为房颤或房扑戒律的患者定义为房颤患者。我们根据7:1:2的比例,将ECG分为训练数据集、内部验证数据集和检验数据集。针对内部验证数据集,我们计算ROC曲线下面积(AUC),以选择用于检验数据集的概率阈值。我们根据AUC、准确性、敏感性、特异性和F1评分(双侧95%CI)评价检验数据集模型的准确性。

Findings 结果

We included 180 922 patients with 649 931 normal sinus rhythm ECGs for analysis: 454 789 ECGs recorded from 126 526 patients in the training dataset, 64 340 ECGs from 18 116 patients in the internal validation dataset, and 130 802 ECGs from 36 280 patients in the testing dataset. 3051 (8·4%) patients in the testing dataset had verified atrial fibrillation before the normal sinus rhythm ECG tested by the model. A single AI-enabled ECG identified atrial fibrillation with an AUC of 0·87 (95% CI 0·86–0·88), sensitivity of 79·0% (77·5–80·4), specificity of 79·5% (79·0–79·9), F1 score of 39·2% (38·1–40·3), and overall accuracy of 79·4% (79·0–79·9). Including all ECGs acquired during the first month of each patient's window of interest (ie, the study start date or 31 days before the first recorded atrial fibrillation ECG) increased the AUC to 0·90 (0·90–0·91), sensitivity to 82·3% (80·9–83·6), specificity to 83·4% (83·0–83·8), F1 score to 45·4% (44·2–46·5), and overall accuracy to 83·3% (83·0–83·7).

我们纳入了180 922 名患者的 649 931 份正常窦性心律 ECGs 用于分析:训练数据集包括126 525 名患者的 454 789 份 ECG,内部验证数据集包括 18 116 名患者的 64 340 份 ECG,检验数据集包括36 280 名患者的 130 802 份 ECG。在正常窦性心律ECG用于模型检验前,检验数据集中 3051 名 (8·4%) 患者被确认为房颤。单次AI 分析 ECG 鉴别房颤的 AUC 0·87 (95% CI 0·86–0·88),敏感性 79·0% (77·5–80·4),特异性 79·5% (79·0–79·9),F1 评分 39·2% (38·1–40·3),总的准确性为 79·4% (79·0–79·9)。对于每位患者,纳入研究窗首月内所有ECG(即研究开始日期或首次记录房颤前31天),可使 AUC 增加到 0·90 (0·90–0·91),敏感性增加到 82·3% (80·9–83·6),特异性 83·4% (83·0–83·8), F1 评分 45·4% (44·2–46·5),总体准确性 83·3% (83·0–83·7)。

Interpretation 结论

An AI-enabled ECG acquired during normal sinus rhythm permits identification at point of care of individuals with atrial fibrillation.

采用AI分析正常窦性心律下的ECG,能够在床旁鉴别房颤患者。

Funding 资助

None.

给我留言

您必须 [ 登录 ] 才能发表留言!

×
腾讯微博