Key result
Adding AI-derived echocardiographic parameters to clinical variables improved 1-year mortality (AUC 0.85 vs 0.81; p=0.018) and MACE (AUC 0.80 vs 0.74; p<0.001) prediction post-AMI.
Why the study?
Novel echocardiographic markers offer prognostic utility beyond LVEF after AMI, but time constraints and limited expertise restrict their widespread use. This study evaluated the utility of adding fully automated AI-derived echocardiographic parameters to clinical variables for predicting post-AMI outcomes.
Does adding AI-derived echocardiographic parameters to clinical variables improve the prediction of one-year all-cause mortality and MACE in patients after acute myocardial infarction?
Cohort (n=1,001)
Does adding AI-derived echocardiographic parameters to clinical variables improve the prediction of one-year all-cause mortality and MACE in patients after acute myocardial infarction?
Combining automated AI-derived echocardiographic parameters with traditional clinical risk factors significantly improves risk stratification for one-year mortality and MACE after acute myocardial infarction.
Background Early risk stratification in patients after acute myocardial infarction (AMI) is critical for guiding therapy and resource allocation. While left ventricular ejection fraction (LVEF) is routinely assessed by echocardiography, novel markers offer additional prognostic utility but are not widely assessed due to time constraints or limited expertise. Artificial intelligence (AI) enables rapid, fully automated analysis of echocardiograms, producing standardized, comprehensive measurements. Aim We evaluated the utility of AI-derived echocardiographic parameters on top of clinical variables in predicting outcomes post-AMI. Methods Consecutive AMI patients undergoing invasive coronary angiogram were included. Echocardiograms were analyzed using Us2.ai software. Independent predictors of one-year all-cause mortality and major adverse cardiac events (MACE) were assessed using Cox regression. Clinical, echocardiographic, and combined models were compared. Results Among 1,001 patients, aged 64 years (54, 72) and predominantly male (78.1%), 161 (16.1%) died during follow-up. AI-echocardiographic markers independently associated with one-year all-cause mortality or MACE included lower LVEF, greater LV wall thickness, lower LV mass, greater LA area, lower LA reservoir strain and lower aortic valve area. For one-year mortality, the combined model demonstrated superior discrimination compared with the clinical model alone (AUC 0.85 vs. 0.81; p = 0.018). Similarly, for one-year MACE, the combined model showed improved discrimination compared with the clinical model (AUC 0.80 vs. 0.74; p < 0.001) and yielded the lowest Akaike’s and Bayesian Informations. Conclusion Combining AI-derived echocardiographic parameters, together with traditional clinical risk factors, provides incremental prognostic value post-AMI. AI tools that automate complex assessments accurately and reproducibly may enhance risk stratification.
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Lenselink et al. (2026) conducted a cohort in Acute myocardial infarction (n=1,001). Combined AI-derived echocardiographic (Us2.ai) and clinical risk model vs. Clinical risk model alone was evaluated on One-year all-cause mortality and major adverse cardiac events (MACE) (AUC 0.85 vs. 0.81 (mortality); AUC 0.80 vs. 0.74 (MACE), p=0.018). Adding AI-derived echocardiographic parameters to clinical variables improved 1-year mortality (AUC 0.85 vs 0.81; p=0.018) and MACE (AUC 0.80 vs 0.74; p<0.001) prediction post-AMI.
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