Angiotensin II receptor blockers is associated a decreased left ventricular fibrosis in patients with obstructive hypertrophic cardiomyopathy

Background Myocardial fibrosis (MF) was implicated as a major risk factor for sudden cardiac death (SCD), which can lead to fatal ventricular arrhythmias. Angiotensin II receptor blockers (ARBs) have been reported to reduce MF in patients with nonobstructive HCM, whereas results were inconsistent. Here, we aimed to investigate the association between ARB use and the severity of MF in patients with obstructive HCM (oHCM). Method We analyzed data from 1324 consecutive hospitalized oHCM patients from January 2012 to December 2019. All patients underwent gadolinium-based contrast agent-enhanced cardiac magnetic resonance (CMR) examinations prior to surgery. The extent of late gadolinium enhancement (LGE%) was compared between patients with and without ARBs treatment (termed ARB+ group and ARB- group). Results Of 1324 included patients, 86 (6.5%) patients had ARBs treatment. Compared to patients in ARB- group, patients in ARB+ group seemed much older, had a higher body mass index (BMI), a higher proportion of hypertension, a smaller intraventricular septal thickness (IVST), and a lower LGE% (3.6 [0.86, 6.68] vs. 4.4 [1.38, 9.74], p= 0.023). After propensity score matching with confounding variables at baseline, 86 paired patients were included in the matched cohort. The LGE% was still lower in ARB+ group than ARB - group (3.6 [0.86, 6.68] vs. 4.5 [1.28, 9.45], p = 0.043), respectively (Figure 1). The multiple linear analysis revealed that ARB use was independently associated with a lower LGE% (β= -2.12, 95% confidence interval (CI) [-4.19, -0.05], p=0.044) after adjusting for age, gender, BMI, hypertension, IVST and left ventricular outflow gradient (Figure2). Conclusion Our study suggested that ARB use was associated with a decreased left ventricular fibrosis in patients with oHCM, which may help SCD prevention in those patients. The extent of LGE% between groups Multiple linear regression analysis