Deep Learning Decodes Latent ECG Signatures of Stress Cardiomyopathy

BackgroundStress cardiomyopathy (SCM) shares features with acute myocardial infarction (AMI) which may lead to misdiagnosis and misaligned management decisions. We hypothesize that features derived from the 12-lead ECG can identify cases of SCM and differentiate them from AMI. MethodsUsing data from a large registry of critically ill patients, we trained a deep learning algorithm to perform two classification tasks: (1) SCM vs. Non-SCM and (2) SCM vs. AMI. Model training was accomplished with 3 different sets of input features: raw ECG waveforms, clinical features from the electronic health record (EHR), and a fusion model combining ECG with clinical data. SHapley Additive exPlanations (SHAP) analysis was performed to identify the most influential predictive features. ResultsAmong 71,479 patients admitted to ICU, 349 (0.48%) were diagnosed with SCM while 4,507 (6.31%) had AMI. The clinical-ECG fusion model achieved best performance, with area under the precision-recall curve (AUPRC) values of 0.191 (0.146-0.243) for SCM vs. Non-SCM and 0.430 (0.371-0.488) for SCM vs. AMI, outperforming baseline AUPRCs of 0.0048 and 0.0625, respectively. The fusion model outperformed both the waveform model (p < 0.001) and the EHR model (p < 0.001) for both SCM vs. Non-SCM and SCM vs. AMI. The waveform model achieved AUPRC values of 0.089 (0.062-0.125) for SCM vs. Non-SCM and 0.309 (0.257-0.374) for SCM vs. AMI. There was no statistically significant difference in performance between the waveform model and the EHR model for both tasks. SHAP analysis highlighted female gender as well as congestive heart failure (CHF) and hypertension as the features most influential in predicting SCM. ConclusionFindings indicate that ECG waveforms contain latent information which supports the detection of SCM in patients admitted to the ICU. ECG-based deep learning screening could enable early identification and treatment of SCM and might be particularly valuable in resource-constrained environments.