Data-driven analysis of technological biomarkers and functional myocardial ischemia in stable coronary artery disease using advanced statistical modeling.

BackgroundFunctional myocardial ischemia (FMI) in stable coronary artery disease (SCAD) remains a critical challenge in cardiovascular care. While fractional flow reserve (FFR) is a gold-standard diagnostic technology, its clinical adoption is limited by cost and complexity. Integrating technological biomarkers and advanced analytics could enhance risk stratification and guide precision interventions.ObjectiveThis study leverages data-driven methodologies to identify and validate technological biomarkers associated with FMI in SCAD, aiming to optimize clinical decision-making through predictive modeling.MethodsA systematic search across PubMed, Embase, and Web of Science (inception-October 2023) identified studies evaluating SCAD and FMI. Inclusion criteria: cohort/case-control studies (n ≥ 100) using FFR or angiographic technologies. Meta-analyses were conducted via RevMan 5.4 and Stata 16.0, employing fixed/random-effects models. Heterogeneity was assessed using I² statistics.ResultsAnalysis of 15 studies (n = 4854) revealed that anatomical biomarkers-stenosis severity (DS%: SMD = 0.95,  < 0.0001), minimal lumen diameter (SMD = -1.33,  < 0.0001), and lesion length (SMD = 0.72,  < 0.0001)-were strongly linked to FMI. Diabetes (OR = 1.31,  = 0.003) and smoking (OR = 1.47,  < 0.0001) emerged as significant modifiable risks, while hypertension showed no association ( = 0.14). Age and gender disparities highlighted the need for personalized risk algorithms.ConclusionTechnological biomarkers and data-driven modeling provide actionable insights into FMI risk in SCAD, bridging gaps between anatomical assessments and functional outcomes. Future integration of machine learning and predictive analytics could refine risk stratification, enabling tailored therapeutic strategies.