Dataset Information

Cardiac magnetic resonance features and outcomes of patients with non-compaction cardiomyopathy – A retrospective follow-up from Pakistan

ABSTRACT:

Background

The exact prevalence of left ventricle non-compaction cardiomyopathy (LVNC) in south Asians is not known and phenotypic CMR characteristics, clinical features, and outcomes of LVNC remain unknown for the SA population.

Objective

To evaluate clinical characteristics, cardiac magnetic resonance imaging features, and outcomes of patients with left ventricle non-compaction.

Methods

This was a retrospective study of 294 patients undergoing cardiac MRI (CMR) for evaluation of cardiomyopathy from 2011 to 2020. Patients were stratified based on the presence or absence of left ventricle non-compaction (LVNC). Clinical characteristics, CMR features, and outcomes were evaluated.

Results

Out of 294 patients, 18 patients had LVNC, with a prevalence of 6.1%. The mean age was 32 ± 13 years, and the majority were males (78%). The mean EF by echo was 36 ± 14 and by CMR was 31 ± 16 and the mean LV mass was 151 g. The mean LVEDV was 290 ± 154 and the mean LVESV was 211 ± 126. LGE was present in 33% of patients. The majority had uniform LV non-compaction (56%) followed by predominantly anterolateral and apical involvement (28%). Mitral regurgitation was the most common valvular pathology (33%). On follow-up of 37 months, the majority experienced at least one all-cause MACE (69%), while 14% of patients experienced mortality on follow-up. When compared with dilated cardiomyopathy patients without LVNC, the subjects were younger (p = 0.002) and had higher EF by an echocardiogram (0.001) and a lower arrhythmia hospitalization (p = 0.039). No difference was observed in overall MACE outcomes, mortality, and CMR features.

Conclusion

The prevalence of LVNC is low in the studied population. Patients with LVNC have younger age, higher EF by echocardiogram, and lower arrhythmia hospitalization when compared with patients with dilated cardiomyopathy without evidence of LV non-compaction. The presence of LVNC does not confer an increased risk of MACE. Highlights • Patients with LVNC have younger age, higher EF by echocardiogram, and lower arrhythmia hospitalization when compared with patients with dilated cardiomyopathy without evidence of LV non-compaction.• The presence of LVNC does not confer an increased risk of MACE.

SUBMITTER: Shams P

PROVIDER: S-EPMC9283803 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Left Ventricular (LV) Non-compaction (LVNC), Hypertrophic Cardiomyopathy (HCM), and Dilated Cardiomyopathy (DCM) share morphological and functional traits that increase the diagnosis complexity. Additional clinical information, besides imaging data such as cardiovascular magnetic resonance (CMR), is usually required to reach a definitive diagnosis, including electrocardiography (ECG), family history, and genetics. Alternatively, indices of hypertrabeculation have been introduced, but they require tedious and time-consuming delineations of the trabeculae on the CMR images. In this paper, we propose a radiomics approach to automatically encode differences in the underlying shape, gray-scale and textural information in the myocardium and its trabeculae, which may enhance the capacity to differentiate between these overlapping conditions. A total of 118 subjects, including 35 patients with LVNC, 25 with HCM, 37 with DCM, as well as 21 healthy volunteers (NOR), underwent CMR imaging. A comprehensive radiomics characterization was applied to LV short-axis images to quantify shape, first-order, co-occurrence matrix, run-length matrix, and local binary patterns. Conventional CMR indices (LV volumes, mass, wall thickness, LV ejection fraction-LVEF-), as well as hypertrabeculation indices by Petersen and Jacquier, were also analyzed. State-of-the-art Machine Learning (ML) models (one-vs.-rest Support Vector Machine-SVM-, Logistic Regression-LR-, and Random Forest Classifier-RF-) were used for one-vs.-rest classification tasks. The use of radiomics models for the automated diagnosis of LVNC, HCM, and DCM resulted in excellent one-vs.-rest ROC-AUC values of 0.95 while generating these results without the need for the delineation of the trabeculae. First-order and texture features resulted to be among the most discriminative features in the obtained radiomics signatures, indicating their added value for quantifying relevant tissue patterns in cardiomyopathy differential diagnosis.