Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations.
Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations.
复制标题
肥厚型心肌病患者级人工智能增强心电图：纵向治疗和临床生物标志物相关性。
DOI：
10.1016/j.jacadv.2023.100582
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Friedm
中科院分区：
文献类型：
--
作者：
Siontis,KonstantinosC;Abreau,Sean;Attia,ZachiI;Barrios,JoshuaP;Dewland,ThomasA;Agarwal,Priyanka;Balasubramanyam,Aarthi;Li,Yunfan;Lester,StevenJ;Masri,Ahmad;Wang,Andrew;Sehnert,AmyJ;Edelberg,JayM;Abraham,TheodoreP;Friedm
背景人工智能（AI）应用于12导联心电图（ECG）可以检测肥厚型心肌病（HCM）。 目的本研究的目的是确定人工智能增强心电图（AI-ECG）是否可以跟踪mavacamten治疗期间梗阻性HCM的纵向治疗反应和心脏结构、功能或血流动力学的变化。方法我们应用2种独立开发的AI-ECG算法（加州大学旧金山分校和梅奥大学）临床）到 mavacamten 治疗症状性梗阻性 HCM 2 期 PIONEER-OLE 试验的系列心电图（n = 216）（n = 13 名患者，平均年龄 57.8 岁，69.2% 男性）。获得了 2,600 名年龄和性别匹配的无 HCM 个体的对照心电图。 AI-ECG 输出与 mavacamten 治疗反应的超声心动图和实验室指标纵向相关。结果在验证队列中，两种算法在 HCM 诊断方面表现出相似的性能，并且在 mavacamten 治疗期间表现出平均 HCM 评分下降：患者水平评分下降范围为 Mayo 约 0.80 至 0.45，USCF 算法约 0.70 至 0.35； 13 名患者中有 11 名表现出两种算法从随访开始到结束的绝对评分下降。 HCM 评分与其他 HCM 相关参数显着相关，包括静息时、运动后和 Valsalva 时的左心室流出道梯度，以及 NT-proBNP 水平，与年龄和性别无关（均 P<0.01）。对于这两种算法，最强的纵向相关性是 AI-ECG HCM 评分与运动后左心室流出道梯度之间的关系（斜率估计：加州大学旧金山分校 0.70 [95% CI：0.45-0.96]，P< 0.0001；Mayo 0.40 [95% CI：0.11-0.68]，P= 0.007）。结论 AI-ECG 分析与阻塞性 HCM 治疗期间超声心动图和实验室标志物的变化呈纵向相关。这些结果为监测 HCM 治疗反应的潜在范例提供了早期证据。
BackgroundArtificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM).ObjectivesThe purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment.MethodsWe applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response.ResultsIn the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (allP< 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96],P< 0.0001; Mayo 0.40 [95% CI: 0.11-0.68],P= 0.007).ConclusionsAI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response.