ImportanceCongenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG).ObjectiveTo develop a deep learning–based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and LQTS2) using 12-lead ECG.Design, Setting, and ParticipantsThis diagnostic accuracy study used ECGs from patients with suspected inherited arrhythmia enrolled in the Hearts in Rhythm Organization Registry (HiRO) from August 2012 to December 2021. The internal dataset was derived at 2 sites and an external validation dataset at 4 sites within the HiRO Registry; an additional cross-sectional validation dataset was from the Montreal Heart Institute. The cohort with LQTS included probands and relatives with pathogenic or likely pathogenic variants in KCNQ1 or KCNH2 genes with normal or prolonged corrected QT (QTc) intervals.ExposuresConvolutional neural network (CNN) discrimination between LQTS1, LQTS2, and negative genetic test results.Main Outcomes and MeasuresThe main outcomes were area under the curve (AUC), F1 scores, and sensitivity for detecting LQTS and differentiating genotypes using a CNN method compared with QTc-based detection.ResultsA total of 4521 ECGs from 990 patients (mean [SD] age, 42 [18] years; 589 [59.5%] female) were analyzed. External validation within the national registry (101 patients) demonstrated the CNN’s high diagnostic capacity for LQTS detection (AUC, 0.93; 95% CI, 0.89-0.96) and genotype differentiation (AUC, 0.91; 95% CI, 0.86-0.96). This surpassed expert-measured QTc intervals in detecting LQTS (F1 score, 0.84 [95% CI, 0.78-0.90] vs 0.22 [95% CI, 0.13-0.31]; sensitivity, 0.90 [95% CI, 0.86-0.94] vs 0.36 [95% CI, 0.23-0.47]), including in patients with normal or borderline QTc intervals (F1 score, 0.70 [95% CI, 0.40-1.00]; sensitivity, 0.78 [95% CI, 0.53-0.95]). In further validation in a cross-sectional cohort (406 patients) of high-risk patients and genotype-negative controls, the CNN detected LQTS with an AUC of 0.81 (95% CI, 0.80-0.85), which was better than QTc interval–based detection (AUC, 0.74; 95% CI, 0.69-0.78).Conclusions and RelevanceThe deep learning model improved detection of congenital LQTS from resting ECGs and allowed for differentiation between the 2 most common genetic subtypes. Broader validation over an unselected general population may support application of this model to patients with suspected LQTS.

中文翻译：

---

深度学习增强心电图分析用于先天性长 QT 综合征的筛查和基因型预测

重要性先天性长 QT 综合征 (LQTS) 与晕厥、室性心律失常和猝死有关。尽管 LQTS 经常通过静息心电图 (ECG) 上的 QT 延长来检测，但一半的 LQTS 患者具有正常或临界正常 QT 间期。 目的开发基于深度学习的神经网络，用于识别 LQTS 和区分基因型（LQTS1 和 LQTS2） ）使用 12 导联心电图。设计、设置和参与者这项诊断准确性研究使用了 2012 年 8 月至 2021 年 12 月在心律组织注册中心 (HiRO) 登记的疑似遗传性心律失常患者的心电图。内部数据集来自 2 个地点以及 HiRO 注册中心内 4 个站点的外部验证数据集；另一个横截面验证数据集来自蒙特利尔心脏研究所。LQTS 队列包括先证者和具有致病性或可能致病性变异的亲属KCNQ1或者氯化钾具有正常或延长的校正 QT (QTc) 间期的基因。暴露卷积神经网络 (CNN) 区分 LQTS1、LQTS2 和阴性基因测试结果。主要结果和测量主要结果是曲线下面积 (AUC)、F1 分数和敏感性与基于 QTc 的检测相比，使用 CNN 方法检测 LQTS 并区分基因型。结果分析了来自 990 名患者（平均 [SD] 年龄，42 [18] 岁；589 [59.5%] 女性）的总共 4521 份心电图。国家登记处（101 名患者）的外部验证证明了 CNN 对 LQTS 检测（AUC，0.93；95% CI，0.89-0.96）和基因型区分（AUC，0.91；95% CI，0.86-0.96）的高诊断能力。这在检测 LQTS 方面超过了专家测量的 QTc 间期（F1 评分，0.84 [95% CI，0.78-0.90] vs 0.22 [95% CI，0.13-0.31]；灵敏度，0.90 [95% CI，0.86-0.94] vs 0.36 [95% CI，0.23-0.47]），包括 QTc 间期正常或临界值的患者（F1 评分，0.70 [95% CI，0.40-1.00]；敏感性，0.78 [95% CI，0.53-0.95]）。在高危患者和基因型阴性对照的横断面队列（406 名患者）的进一步验证中，CNN 检测到 LQTS 的 AUC 为 0.81（95% CI，0.80-0.85），优于 QTc 间期 –基于检测（AUC，0.74；95% CI，0.69-0.78）。结论和相关性深度学习模型改进了静息心电图对先天性 LQTS 的检测，并允许区分 2 种最常见的遗传亚型。对未经选择的一般人群进行更广泛的验证可能支持将该模型应用于疑似 LQTS 患者。