Abstract
AbstractBackgroundCurrent 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.MethodsThe 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.ResultsThree 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.ConclusionsUltrasomics-based phenogroup clustering, augmented by TDA and supervised machine learning, provides a novel approach for AMI risk stratification.
Publisher
Cold Spring Harbor Laboratory