Late gadolinium-enhanced cardiac MRI and C-reactive protein for predicting clinical prognosis of acute myocarditis in

Xiayun Shi1, Yunfei Wang2, Zhongman Zhang3

  • 1Department of Radiology, Southeast University Affiliated Nantong First People's Hospital, Nantong, Jiangsu, PR China.

Insights

Cardiovascular magnetic resonance (CMR) shows that higher late gadolinium enhancement (LGE) in fulminant myocarditis (FM) with mechanical circulatory support (MCS) predicts worse outcomes. This imaging technique aids risk stratification for acute myocarditis patients.

Area of Science:

  • Cardiovascular imaging
  • Myocardial diseases
  • Magnetic resonance imaging

Background:

  • The prognostic implications of acute myocardial injury in acute myocarditis (AM) remain incompletely understood.
  • Cardiovascular magnetic resonance (CMR) offers advanced tissue characterization for evaluating myocardial damage.

Purpose of the Study:

  • To assess myocardial injury characteristics using CMR tissue parameters in AM patients.
  • To determine the impact of these CMR-derived parameters on adverse clinical outcomes.

Main Methods:

  • Retrospective analysis of 76 AM patients (41 fulminant myocarditis [FM], 35 non-FM).
  • CMR included cine, T1/T2 mapping, and late gadolinium enhancement (LGE).
  • Adverse outcomes included major adverse cardiovascular events (MACEs) and functional decline (NYHA classes II-IV).

Main Results:

  • FM patients with mechanical circulatory support (MCS) showed significantly higher native T1, extracellular volume (ECV), and LGE compared to non-FM.
  • LGE > 8.4% was independently associated with adverse outcomes after adjusting for CRP levels.
  • The addition of LGE improved the predictive model's accuracy (C-statistic from 0.74 to 0.82).

Conclusions:

  • The extent of necrosis and fibrosis, indicated by LGE, is greatest in FM with MCS.
  • Higher LGE percentage provides incremental prognostic value for AM patients.
  • A combined approach using CRP and LGE enhances prediction of adverse outcomes, improving risk stratification.

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