ArticleEcho research and practice2024
Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study.
Article in Echo research and practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- miR-4443 alleviates myocardial injury in acute myocardial infarction by targeting and regulating TIMP2.Journal of thrombosis and thrombolysis · 2026Article
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Ultrasonic Texture Analysis for Predicting Acute Myocardial Infarction.JACC. Cardiovascular imaging · 2025Article
- Clinical research and trials in echocardiography: rationale, requirements and future opportunities.Echo research and practice · 2025Review
- Analysis of Cardiotoxicity and Risk Factors of Breast Cancer Chemotherapy Drugs: A Five-Year Single-Centre Retrospective Study in Early Breast Cancer.Drug design, development and therapy · 2025Article
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Authors and funding
7 authors.
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Abstract
backgroundCurrent 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.
resultsThe 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. 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 < 0.01) and global longitudinal strain (P = 0.03) and increased mortality at 1-year (log rank P = 0.05). Ultrasomics features alone (C-Index: 0.74 vs. 0.70, P = 0.04) and combined with global longitudinal strain (C-Index: 0.81 vs. 0.70, P < 0.01) increased prediction of mortality beyond the GRACE 2.0 score. In the DTU-STEMI clinical trial, Cluster A was associated with larger infarct size (> 10% LV mass, P < 0.01), compared to remaining clusters.
conclusionsUltrasomics-based phenogroup clustering, augmented by TDA and supervised machine learning, provides a novel approach for AMI risk stratification.
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