人工智能对心电图波形图像 (AI-ECG) 的解释可以识别预测未来不良心脏事件的模式。我们假设这种在普通内科和外科患者中得到充分描述的方法将为因血液恶性肿瘤而接受造血细胞移植（HCT）的患者提供有关心脏并发症风险和总体死亡率的预后信息。我们回顾性地将 HCT 前获得的心电图置于外部训练的深度学习模型中，该模型旨在预测心房颤动 (AF) 的风险。包括 1,377 名患者（849 名自体 HCT 接受者和 528 名同种异体 HCT 接受者）。中位随访时间为 2.9 年。自体 HCT 患者的 AF 三年累积发生率为 9%（95% CI：7-12%），同种异体 HCT 患者的 AF 三年累积发生率为 13%（10-16%）。在整个队列中，HCT 前 AI-ECG 对 AF 风险的估计与临床 AF 的发生高度相关（风险比 (HR) 7.37、3.53-15.4，p <0.001）、较差的总生存期（HR：2.4；1.3-4.5） ，p = 0.004），并且非复发死亡率风险更高（HR 3.36、1.39-8.13，p = 0.007），但复发风险并未增加。仅在 allo HCT 接受者中发现与死亡率的显着相关性，其中非复发死亡的风险更大。与基于钙调神经磷酸酶抑制剂的移植物抗宿主病预防相比，移植后环磷酰胺的使用导致 AF 的 90 天发生率更高（13% 对比 5%，p = 0.01），这与 AI-ECG AF 预测的时间变化相对应HCT 后。总之，AI-ECG 可以告知 HCT 患者移植后心脏结局和生存的风险，并代表了 HCT 后个性化风险评估的一种新策略。版权所有 © 2024 美国血液学会。

Artificial intelligence enabled interpretation of electrocardiogram waveform images (AI-ECG) can identify patterns predictive of future adverse cardiac events. We hypothesized such an approach, which is well described in general medical and surgical patients, would provide prognostic information with respect to the risk of cardiac complications and overall mortality in patients undergoing hematopoietic cell transplantation (HCT) for blood malignancy. We retrospectively subjected ECGs obtained pre-HCT to an externally trained, deep learning model designed to predict risk of atrial fibrillation (AF). Included were 1,377 patients (849 autologous HCT and 528 allogeneic HCT recipients). Median follow-up was 2.9 years. The three-year cumulative incidence of AF was 9% (95% CI: 7-12%) in autologous HCT patients and 13% (10-16%) in allogeneic HCT patients. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with development of clinical AF (Hazard Ratio (HR) 7.37, 3.53-15.4, p <0.001), inferior overall survival (HR: 2.4; 1.3-4.5, p = 0.004), and greater risk of non-relapse mortality (HR 3.36, 1.39-8.13, p = 0.007), without increased risk of relapse. Significant associations with mortality were only noted in allo HCT recipients, where the risk of non-relapse mortality was greater. Compared to calcineurin inhibitor-based graft versus host disease prophylaxis, the use of post-transplantation cyclophosphamide resulted in greater 90-day incidence of AF (13% versus 5%, p = 0.01), corresponding to temporal changes in AI-ECG AF prediction post HCT. In summary, AI-ECG can inform risk of post-transplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment after HCT.Copyright © 2024 American Society of Hematology.