Contemporary Diagnosis and Management of Hypertrophic Cardiomyopathy: The Role of Echocardiography and Multimodality Imaging

Imaging cardiac hypertrophy in hypertrophic cardiomyopathy and its differential diagnosis

PURPOSE OF REVIEW

The aim of this study was to review imaging of myocardial hypertrophy in hypertrophic cardiomyopathy (HCM) and its phenocopies. The introduction of cardiac myosin inhibitors in HCM has emphasized the need for careful evaluation of the underlying cause of myocardial hypertrophy.

RECENT FINDINGS

Advances in imaging of myocardial hypertrophy have focused on improving precision, diagnosis, and predicting prognosis. From improved assessment of myocardial mass and function, to assessing myocardial fibrosis without the use of gadolinium, imaging continues to be the primary tool in understanding myocardial hypertrophy and its downstream effects. Advances in differentiating athlete's heart from HCM are noted, and the increasing rate of diagnosis in cardiac amyloidosis using noninvasive approaches is especially highlighted due to the implications on treatment approach. Finally, recent data on Fabry disease are shared as well as differentiating other phenocopies from HCM.

SUMMARY

Imaging hypertrophy in HCM and ruling out other phenocopies is central to the care of patients with HCM. This space will continue to rapidly evolve, as disease-modifying therapies are under investigation and being advanced to the clinic.

Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy resulting from a mutation in one of several cardiac sarcomeric proteins [...]

The Establishment of Hypertrophic Cardiomyopathy Diagnosis Model via Artificial Neural Network and Random Decision Forest Method

Hypertrophic cardiomyopathy is a hereditary disease characterized by asymmetric ventricular hypertrophy as the key anatomical feature. Currently, there exists no effective method for the early diagnosis of hypertrophic cardiomyopathy. In this analysis, we incorporated multiple GEO datasets containing RNA profiles of hypertrophic cardiomyopathic patient tissues, identified 642 differentially expressed genes, and performed GO and KEGG analyses. Furthermore, we narrowed down 46 characteristic genes from these differentially expressed genes using random decision forests and conducted transcription factor regulation analysis on them. Using 40 genes that showed overlap between the training set and the verification set, the artificial neural network was trained, and the final MPS scoring model was constructed, and a receiver-operating characteristic (ROC) curve was drawn. We used the MPS model to predict the verification dataset and drew the ROC curve, which demonstrated the good prediction performance of the model. In conclusion, this study combines a random decision forest and artificial neural network to build a diagnostic model for hypertrophic cardiomyopathy to predict the disease, aiming at early detection and treatment, prolonging the survival time, and improving the quality of life of patients.