Can left ventricular entropy by cardiac magnetic resonance late gadolinium enhancement be a prognostic predictor in patients with left ventricular non-compaction?

PURPOSE Left ventricular non-compaction (LVNC) is considered rare; however, the use of cardiac magnetic resonance (CMR) has shown that its incidence is not uncommon, and its clinical presentation remains variable, with an uncertain prognosis. Risk stratification of major adverse cardiac events (MACE) in patients with LVNC remains complex. Therefore, this study aims to determine whether tissue heterogeneity from late gadolinium enhancement-derived entropy is associated with MACE in patients with LVNC. METHODS This study was registered in the Clinical Trial Registry (CTR2200062045). Consecutive patients who underwent CMR imaging and were diagnosed with LVNC were followed up for MACE, which was defined by heart failure, arrhythmias, systemic embolism, and cardiac death. The patients were divided into MACE and non-MACE groups. The CMR parameters included left ventricular (LV) entropy, LV ejection fraction (LVEF), LV end-diastolic volume, LV end-systolic volume (LVESV), and LV mass (LVM). RESULTS Eighty-six patients (age: 45.48 +/- 16.64 years; female: 62.7%; LVEF: 42.58 +/- 17.20%) were followed up for a median of 18 months and experienced 30 MACE events (34.9%). The MACE group showed higher LV entropy, LVESV, and LVM and lower LVEF than the non-MACE group. LV entropy [hazard ratio (HR): 1.710, 95% confidence interval (CI): 1.078-2.714, P = 0.023] and LVEF (HR: 0.961, 95% CI: 0.936-0.988, P = 0.004) were independent predictors of MACE (P <0.050) according to the Cox regression analysis. Receiver operating characteristic curve analysis revealed that the area under the curve of LV entropy was 0.789 (95% CI: 0.687-0.869, P < 0.001), LVEF was 0.804 (95% CI: 0.6990.878, P < 0.001), and the combined model of LV entropy and LVEF was 0.845 (95% CI: 0.751-0.914, P < 0.050). CONCLUSION LGE-derived LV entropy and LVEF are independent risk indicators of MACE in patients with LVNC. The combination of the two factors was more conducive to improving the prediction of MACE.