Published in:
Open Access
01-12-2023 | Duchenne Muscular Dystrophy | Research
Derivation and validation of diagnostic models for myocardial fibrosis in duchenne muscular dystrophy: assessed by multi-parameter cardiovascular magnetic resonance
Authors:
Zi-qi Zhou, Hua-yan Xu, Hang Fu, Ke Xu, Rong Xu, Xiao-tang Cai, Ying-kun Guo
Published in:
Orphanet Journal of Rare Diseases
|
Issue 1/2023
Login to get access
Abstract
Background
Gadolinium-enhanced cardiovascular magnetic resonance (CMR) is the most widely used approach for diagnosing myocardial fibrosis with late gadolinium enhancement (LGE) in cardiomyopathy associated with Duchenne muscular dystrophy. Given the limitations and safety of gadolinium use, we wanted to develop and evaluate multi-parametric pre-contrast CMR models for the diagnosis of LGE and investigate whether they could be utilised as surrogates for LGE in DMD patients.
Methods
A total of 136 DMD patients were prospectively recruited and separated into LGE − and LGE + groups. In the first subset of patients (derivation cohort), regression models for the diagnosis of LGE were built by logistic regression using pre-contrast sequence parameters. In a validation cohort of other patients, the models’ performances were evaluated.
Results
EF, native T1 and longitudinal strain alone, as well as their combinations form seven models. The model that included EF, native T1 and longitudinal strain had the best diagnostic value, but there was no significant difference in diagnostic accuracy among the other models except EF. In the validation cohort, the diagnosis outcomes of models were moderate consistent with the existence of LGE. The longitudinal strain outperformed the other models in terms of diagnostic value (sensitivity: 83.33%, specificity: 54.55%).
Conclusions
Pre-contrast sequences have a moderate predictive value for LGE. Thus, pre-contrast parameters may be considered only in a specific subset of DMD patients who cannot cooperate for long-time examinations and have contradiction of contrast agent to help predict the presence of LGE.
Trial registration number (TRN)
ChiCTR1800018340
Date of registration
20180107