Ayoub Chadi, Appari Lalith, Pereyra Milagros, Farina Juan M, Chao Chieh-Ju, Scalia Isabel G, Mahmoud Ahmed K, Abbas Mohammed Tiseer, Baba Nima Ali, Jeong Jiwoong, Lester Steven J, Patel Bhavik N, Arsanjani Reza, Banerjee Imon
Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, Arizona, USA.
Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA.
JACC Adv. 2024 Dec 13;4(1):101435. doi: 10.1016/j.jacadv.2024.101435. eCollection 2025 Jan.
Immune checkpoint inhibitor (ICI) therapy has dramatically improved the prognosis for some cancers but can be associated with myocarditis, adverse cardiovascular events, and mortality.
The aim of this study was to develop an artificial intelligence (AI) model to predict the increased likelihood for the development of ICI-related myocarditis and adverse cardiovascular events.
Cancer patients treated with ICI at a tertiary institution from 2011 to 2022 were reviewed. Baseline characteristics, laboratory values, electrocardiograms, and cardiovascular clinical outcomes were extracted. A composite outcome of ICI-related myocarditis and major adverse cardiovascular events (transient ischemic attack/stroke, new diagnosis of heart failure, myocardial infarction, and cardiac death) was used to develop a multimodal joint fusion AI model by combining baseline tabular data with electrocardiogram (ECG) in a single end-to-end model. ECG data were parsed using 1-D convolution and tubular data using multilayer perceptron.
Of 2,258 cancer patients who had ICI therapy and troponin measurement (mean age 68.5 ± 11.5 years, 59.7% male), the composite of cardiovascular clinical adverse events, including ICI-related myocarditis and major adverse cardiovascular events, occurred in 264 (11.7%) unique patients, with 428 events overall (including 59 [3%] ICI-related myocarditis events and 59 [3%] cardiac deaths). The proposed joint fusion model outperformed individual ECG and baseline electronic medical record data and laboratory value models with an area under the operating characteristics curve of 0.72 (0.64 true positive rate and 0.98 negative predictive value).
A multimodal fusion AI model to predict myocarditis and adverse cardiovascular events in cancer patients starting ICI therapy had good prognostic performance. It may have clinical utility in identifying at-risk patients who may benefit from closer surveillance.
免疫检查点抑制剂（ICI）疗法显著改善了某些癌症的预后，但可能与心肌炎、不良心血管事件及死亡率相关。
本研究旨在开发一种人工智能（AI）模型，以预测发生ICI相关心肌炎及不良心血管事件的可能性增加。
回顾了2011年至2022年在一家三级医疗机构接受ICI治疗的癌症患者。提取基线特征、实验室检查值、心电图及心血管临床结局。通过在单个端到端模型中将基线表格数据与心电图（ECG）相结合，使用ICI相关心肌炎和主要不良心血管事件（短暂性脑缺血发作/中风、新诊断的心力衰竭、心肌梗死及心源性死亡）的复合结局来开发多模态联合融合AI模型。ECG数据使用一维卷积进行解析，表格数据使用多层感知器进行解析。
在2258例接受ICI治疗并进行肌钙蛋白检测的癌症患者中（平均年龄68.5±11.5岁，59.7%为男性），包括ICI相关心肌炎和主要不良心血管事件在内的心血管临床不良事件复合结局发生在264例（11.7%）不同患者中，共发生428起事件（包括59例[3%]ICI相关心肌炎事件和59例[3%]心源性死亡）。所提出的联合融合模型优于单独的ECG及基线电子病历数据和实验室检查值模型，其操作特征曲线下面积为0.72（真阳性率为0.64，阴性预测值为0.98）。
用于预测开始ICI治疗的癌症患者心肌炎和不良心血管事件的多模态融合AI模型具有良好的预后性能。它可能在识别可能受益于密切监测的高危患者方面具有临床应用价值。
