Why the study?
Does the GRACE 3.0 machine learning score improve risk stratification for mortality in patients with NSTE-ACS compared to previous GRACE models?
Does the GRACE 3.0 machine learning score improve risk stratification for mortality in patients with NSTE-ACS compared to previous GRACE models?
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GRACE 3.0 leverages machine learning to refine risk stratification and address the limitations of previous GRACE models in contemporary NSTE-ACS cohorts.
This comment refers to ‘Extension of the GRACE score for non-ST-elevation acute coronary syndrome: a development and validation study in ten countries’ which was published in the Lancet Digit Health, https://doi.org/10.1016/j.landig.2025.100907. Risk stratification remains a cornerstone of ACS management, particularly for patients with NSTE-ACS, given the heterogeneity in clinical presentation, comorbidities, extent of coronary disease, procedural timing, and evolving therapies. Although the GRACE risk score has long represented the gold standard for estimating in-hospital and post-discharge mortality—and it is integrated into international ACS management guidelines2—its clinical implementation has lagged behind its prognostic strength.3 Several limitations, in fact, have become evident over time. Many derivation cohorts predated current invasive and pharmacologic standards; most models assumed fixed linear relationships between covariates and outcomes; sex- and region-specific performance was under-examined; dynamic changes in biomarkers and therapies over time have reduced calibration in contemporary cohorts. The original GRACE risk models used eight routinely available clinical variables for predicting in-hospital mortality.3 Over time, temporal declines in short-term mortality4 have shifted attention towards long-term outcomes.5 The GRACE 2.0 model introduced modest refinements, including substitution rules for missing data and improved digital usability through online and mobile calculators,6 but retained several limitations—most notably, overestimation of absolute risk in contemporary cohorts and suboptimal calibration in women, who remain systematically underrepresented in validation datasets. A modification incorporating high-sensitivity troponin as a continuous covariate enhanced predictive accuracy for both in-hospital and one-year mortality.7
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Liuzzo et al. (2025) studied this question.
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