Clinical prediction of genotypes in hypertrophic cardiomyopathy: A systematic review. (25th May 2021)

• Record Type: Journal Article
• Authors: Aziz, Amir; Musiol, Szymon K.; Moody, William E.; Pickup, Luke; Cooper, Rob; Lip, Gregory Y. H.
• Is Part Of: European journal of clinical investigation. Volume 51:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-25
• Subjects: genotype -- hypertrophic cardiomyopathy; Pathology -- Periodicals; Medical research -- Periodicals; 616.075
• DOI: 10.1111/eci.13593
• Languages: English
• ISSNs: 0014-2972

Abstract

Abstract: Introduction: Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac condition and the most common cause of sudden cardiac death (SCD) in patients below the age of 35. Genetic testing is a vital part of HCM diagnostics, yet correlation with clinical phenotypes remains complex. Identifying clinical predictors of informative genetic testing may prevent unnecessary investigations and improve cost‐effectiveness of services. This article reviews the current literature pertinent to identifying such predictors. Methods: Five literature databases were screened using a suitably designed search strategy. Studies investigating the correlation between having a positive genetic test for HCM and a range of clinical and radiological parameters were included in the systematic review. Results: Twenty‐nine observational studies of a total of 9, 486 patients were included. The main predictors of informative genetic testing were younger age, higher septal thickness, reverse septal curvature, family history of HCM and SCD and the absence of hypertension. Two externally validated scoring systems have also been developed: the Mayo and Toronto scores. Novel imaging markers and complex algorithmic models are emerging predictors. Conclusion: Using clinical predictors to decide whom to test is a feasible alternative to investigating all comers. Nonetheless, currently there is not enough evidence to unequivocally recommend for or against this strategy. Further validation of current predictors and identification of new ones remain open research avenues.