Department of Cardiovascular Medicine Mayo Clinic Rochester MN USA.
Division of Cardiovascular Medicine Tsuchiura Kyodo General Hospital Ibaraki Japan.
J Am Heart Assoc. 2024 Mar 5;13(5):e031859. doi: 10.1161/JAHA.123.031859. Epub 2024 Feb 23.
Recent studies have indicated high rates of future major adverse cardiovascular events in patients with Takotsubo cardiomyopathy (TC), but there is no well-established tool for risk stratification. This study sought to evaluate the prognostic value of several artificial intelligence-augmented ECG (AI-ECG) algorithms in patients with TC.
This study examined consecutive patients in the prospective and observational Mayo Clinic Takotsubo syndrome registry. Several previously validated AI-ECG algorithms were used for the estimation of ECG- age, probability of low ejection fraction, and probability of atrial fibrillation. Multivariable models were constructed to evaluate the association of AI-ECG and other clinical characteristics with major adverse cardiac events, defined as cardiovascular death, recurrence of TC, nonfatal myocardial infarction, hospitalization for congestive heart failure, and stroke. In the final analysis, 305 patients with TC were studied over a median follow-up of 4.8 years. Patients with future major adverse cardiac events were more likely to be older, have a history of hypertension, congestive heart failure, worse renal function, as well as high-risk AI-ECG findings compared with those without. Multivariable Cox proportional hazards analysis indicated that the presence of 2 or 3 high-risk findings detected by AI-ECG remained a significant predictor of major adverse cardiac events in patients with TC after adjustment by conventional risk factors (hazard ratio, 4.419 [95% CI, 1.833-10.66], =0.001).
The combined use of AI-ECG algorithms derived from a single 12-lead ECG might detect subtle underlying patterns associated with worse outcomes in patients with TC. This approach might be beneficial for stratifying high-risk patients with TC.
最近的研究表明，患有 Takotsubo 心肌病（TC）的患者未来发生主要不良心血管事件的几率较高，但目前尚无完善的风险分层工具。本研究旨在评估几种人工智能增强心电图（AI-ECG）算法在 TC 患者中的预后价值。
本研究纳入了前瞻性、观察性 Mayo 诊所 Takotsubo 综合征注册研究中的连续患者。使用了几种先前经过验证的 AI-ECG 算法来评估心电图年龄、低射血分数概率和房颤概率。构建多变量模型来评估 AI-ECG 与其他临床特征与主要不良心脏事件（定义为心血管死亡、TC 复发、非致死性心肌梗死、充血性心力衰竭住院和中风）的相关性。最终分析中，对 305 例 TC 患者进行了中位随访 4.8 年的研究。与无未来主要不良心脏事件的患者相比，未来发生主要不良心脏事件的患者年龄更大、有高血压、充血性心力衰竭、肾功能更差以及 AI-ECG 存在高危发现的可能性更高。多变量 Cox 比例风险分析表明，在调整常规危险因素后，AI-ECG 检测到的 2 或 3 个高危发现的存在仍然是 TC 患者发生主要不良心脏事件的显著预测因素（危险比，4.419[95%CI，1.833-10.66]，=0.001）。
联合使用源自单个 12 导联心电图的 AI-ECG 算法可能会检测到与 TC 患者预后较差相关的微妙潜在模式。这种方法可能有益于对 TC 高危患者进行分层。
