Prognostic Value of Left Ventricular Standardized Mechanical Dispersion Derived From Cardiac Magnetic Resonance

Sisi Zhu1, Liumei Li1, Xiaoxia Liu1

  • 1Department of Radiology, Affiliated Hospital 2 of Nantong University, Nantong 226001, PR China (S.Z, L.L., X.L., D.W., J.J., T.W.).

Academic Radiology
|February 19, 2026
PubMed

Insights

Standardized left ventricular mechanical dispersion (sLVMD) predicts major adverse cardiovascular events (MACEs) in dilated cardiomyopathy (DCM) patients. This cardiac imaging metric offers valuable prognostic insights beyond traditional risk factors.

Area of Science:

  • Cardiology
  • Cardiovascular Imaging
  • Biomedical Engineering

Background:

  • Dilated cardiomyopathy (DCM) is a significant cause of heart failure and sudden cardiac death.
  • Accurate prognostic assessment in DCM is crucial for guiding clinical management and improving patient outcomes.
  • Current risk stratification models may not fully capture the complex pathophysiology of DCM.

Purpose of the Study:

  • To determine the prognostic capability of standardized left ventricular mechanical dispersion (sLVMD) for major adverse cardiovascular events (MACEs) in DCM patients.
  • To evaluate the added predictive value of sLVMD to existing risk models for MACEs.
  • To explore the incremental prognostic information provided by sLVMD in DCM.

Main Methods:

  • A cohort of DCM patients undergoing cardiac magnetic resonance (CMR) imaging was analyzed.
  • Left ventricular mechanical dispersion (LVMD) was quantified using CMR feature tracking in radial, circumferential, and longitudinal directions.
  • Standardized LVMD (sLVMD) was calculated by normalizing LVMD to cardiac cycle duration; MACEs included cardiac death, heart failure hospitalization, and malignant arrhythmia.

Main Results:

  • Elevated sLVMD levels were significantly associated with a higher incidence of MACEs during follow-up.
  • Multifactorial Cox regression identified sLVMD (sRMD, sCMD, sLMD) as an independent predictor of MACEs.
  • The predictive model incorporating sLVMD demonstrated a significant improvement in C-statistic compared to models without sLVMD.

Conclusions:

  • sLVMD, assessed via CMR, is a robust independent predictor of MACEs in patients with DCM.
  • sLVMD provides significant incremental prognostic information, enhancing risk assessment in DCM.
  • This imaging biomarker holds potential for improved clinical decision-making in DCM management.
Abstract

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