A Left Ventricular Mechanical Dyssynchrony-Based Nomogram for Predicting Major Adverse Cardiac Events Risk in Patients With Ischemia and No Obstructive Coronary Artery Disease

Abstract

Background: The risk stratification of patients with ischemia and no obstructive coronary artery disease (INOCA) remains suboptimal. This study aims to establish a left ventricular mechanical dyssynchrony (LVMD)-based nomogram to improve the present situation. Methods: Patients with suspected coronary artery disease (CAD) were retrospectively enrolled and divided into three groups: normal (stenosis <50%, without myocardial ischemia), INOCA (stenosis <50%, summed stress score >4, summed difference score ≥2), and obstructive CAD (stenosis ≥50%). LVMD was defined by ROC analysis. INOCA group were followed up for the occurrence of major adverse cardiac events (MACEs: cardiovascular death, non-fatal myocardial infarction, revascularization, stroke, heart failure, and hospitalization for unstable angina). Nomogram was established using multivariate Cox regression analysis. Results: Among 334 patients (118 [35.3%] INOCA), LVMD parameters were significantly higher in INOCA group versus normal group but they did not differ between obstructive CAD groups. In INOCA group, 27 (22.9%) MACEs occurred during a 26-month median follow-up. Proportion of LVMD was significantly higher with MACEs under both stress (63.0% vs. 22.0%, P < 0.001) and rest (51.9% vs. 20.9%, P = 0.002). Kaplan–Meier analysis revealed significantly higher rate of MACEs (stress log-rank: P = 0.002; rest log-rank: P < 0.001) in LVMD patients. Multivariate Cox regression analysis showed that stress LVMD (HR: 3.82; 95% CI: 1.30–11.20; P = 0.015) was an independent predictor of MACEs. The internal bootstrap resampling approach indicates that the C-index of nomogram was 0.80 (95% CI: 0.71–0.89) and the AUC values for 1 and 3 years of risk prediction were 0.68 (95% CI: 0.46–0.89) and 0.84 (95% CI: 0.72–0.95), respectively. Conclusion: LVMD-based nomogram might provide incremental prognostic value and improve the risk stratification in INOCA patients.