Cardiac magnetic resonance imaging for discrimination of hypertensive heart disease and hypertrophic cardiomyopathy: a systematic review and meta-analysis

Introduction: Differentiating hypertensive heart disease (HHD) from hypertrophic cardiomyopathy (HCM) is crucial yet challenging due to overlapping clinical and morphological features. Recent studies have explored the use of various cardiac magnetic resonance (CMR) parameters to distinguish between these conditions, but findings have remained inconclusive. This study aims to identify which CMR parameters effectively discriminate between HHD and HCM and to investigate their underlying pathophysiological mechanisms through a meta-analysis. Methods: The researchers conducted a systematic and comprehensive search for all studies that used CMR to discriminate between HHD and HCM and calculated the Hedges'g effect size for each of the included studies, which were then pooled using a random-effects model and tested for the effects of potential influencing variables through subgroup and regression analyses. Results: In this review, 26 studies encompassing 1,349 HHD and 1,581 HCM cases were included for meta-analysis. Analysis revealed that HHD showed a significant lower in T1 mapping (g = -0.469, P < 0.001), extracellular volume (g = -0.417, P = 0.024), left ventricular mass index (g = -0.437, P < 0.001), and maximal left ventricular wall thickness (g = -2.076, P < 0.001), alongside a significant higher in end-systolic volume index (g = 0.993, P < 0.001) and end-diastolic volume index (g = 0.553, P < 0.001), compared to HCM. Conclusion: This study clearly demonstrates that CMR parameters can effectively differentiate between HHD and HCM. HHD is characterized by significantly lower diffuse interstitial fibrosis and myocardial hypertrophy, along with better-preserved diastolic function but lower systolic function, compared to HCM. The findings highlight the need for standardized CMR protocols, considering the significant influence of MRI machine vendors, post-processing software, and study regions on diagnostic parameters. These insights are crucial for improving diagnostic accuracy and optimizing treatment strategies for patients with HHD and HCM.