Background: Current risk stratification tools for acute myocardial infarction (AMI) have limitations, particularly in predicting mortality. This study utilizes cardiac ultrasound radiomics (i.e., ultrasomics) to risk stratify AMI patients when predicting all cause mortality. Methods: The study included 197 patients: a) retrospective internal cohort (n=155) of non ST elevation myocardial infarction (n=63) and ST elevation myocardial infarction (n=92) patients, and b) external cohort from the multicenter Door To Unload in ST segment elevation myocardial infarction [DTU STEMI] Pilot Trial (n=42). Echocardiography images of apical 2, 3, and 4 chamber were processed through an automated deep learning pipeline to extract ultrasomic features. Unsupervised machine learning (topological data analysis) generated AMI clusters followed by a supervised classifier to generate individual predicted probabilities. Validation included assessing the incremental value of predicted probabilities over the Global Registry of Acute Coronary Events (GRACE) risk score 2.0 to predict 1 year all cause mortality in the internal cohort and infarct size in the external cohort. Results: Three phenogroups were identified: Cluster A (high risk), Cluster B (intermediate risk), and Cluster C (low risk). Cluster A patients had decreased LV ejection fraction (P=0.004) and global longitudinal strain (P=0.027) and increased mortality at 1 year (log rank P=0.049). Ultrasomics features alone (C Index: 0.74 vs. 0.70, P=0.039) and combined with global longitudinal strain (C Index: 0.81 vs. 0.70, P<0.001) increased prediction of mortality beyond the GRACE 2.0 score. In the DTU STEMI clinical trial, Cluster A was associated with larger infarcts size (>10% LV mass, P=0.003), compared to remaining clusters. Conclusions: Ultrasomics based phenogroup clustering, augmented by TDA and supervised machine learning, provides a novel approach for AMI risk stratification.

Dr. Sengupta is a consultant for RCE Technologies, Echo IQ. Dr. Yanamala is an advisor to Turnkey Learning, LLC and Turnkey Learning (P) Ltd, Pittsburgh, PA, USA. All other authors have no reported disclosures relevant to the contents of this paper to disclose.

This study was funded by: NSF: # 2125872

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