Abstract

BACKGROUND: Marfan Syndrome (MFS) often leads to thoracic aortic aneurysm (TAA), for which angiotensin (II) receptor blockers (ARBs) are prescribed to reduce aneurysm growth. Although recent pooled analyses demonstrated a statistically significant reduction in aortic growth with ARBs under a frequentist framework, the clinical relevance of this effect remains uncertain. Moreover, ARB therapy is notably burdensome for patients due to significant side-effects. Therefore, this study re-analyses randomised ARB versus comparator trials in MFS patients, under a Bayesian statistical framework. METHODS: The trials included by the Marfan Treatment Trialists' Collaboration were re-analysed using fixed- and random-effects Bayesian models comparing ARBs to controls. The primary outcome was the mean difference in the annual rate of change of aortic root dimension, adjusted for body surface area (z-score). The minimal clinically important difference (MCID) was applied to assess the clinical relevance of the pooled posterior effect, based on previous consensus and available literature (at 0.12 adjusted z-scores/year). RESULTS: Four randomised trials, comprising 626 patients, were included. Under the fixed-effects model, the pooled mean difference was -0.07 z-score/year [95 % CrI, -0.12; -0.01] in favour of ARBs, though with a posterior probability of a clinically relevant treatment effect of only 7.3 %. Using a Bayesian random-effects model, the pooled mean difference was -0.06 z-score/year [95 % CrI, -0.22 to 0.11] in favour of ARBs, with a similarly low probability of achieving the MCID (18.1 %). CONCLUSIONS: The findings of this Bayesian analysis suggest that ARBs are unlikely to achieve clinically meaningful reductions in aortic growth for MFS patients.
Original languageEnglish
Article number133318
Number of pages4
JournalInternational Journal of Cardiology
Volume433
DOIs
Publication statusPublished - 25 Apr 2025

Keywords

  • Aneurysm
  • Angiotensin (II) receptor blocker (ARB)
  • Bayesian
  • Clinical trial
  • Marfan
  • Meta-analysis

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