The Effects of Medication Strategies on Cardiovascular Outcomes in Patients with Marfan Syndrome: A Consistency Model Analysis
Huang, S.-H.; Lin, C.-Y.; Wu, M.-H.; Kang, Y.-N.; Chiu, H.-H.
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BACKGROUNDDrug options for the treatment of Marfan syndrome (MFS) still warrant further investigation. Beta-blockers (BB) have long been considered the standard treatment, though several medications other than BB and angiotensin receptor blockers (ARB) have entered clinical practice and trials. However, no synthesis gathers evidence on medications other than BB or ARB. Therefore, this study aimed to investigate the effects and safety of medication strategies in managing MFS by synthesizing relevant randomized controlled trials (RCT). METHODSThree databases were searched for potential evidence using relevant keywords in both free-text and medical subject headings. Outcomes of interests were aortic root growth, aortic root Z score, aortic surgery, moderate-to-severe adverse events, cardiovascular and all-cause mortality. Quantitative data were pooled using frequentist-approach network meta-analysis in random-effects model. RESULTSSixteen reports derived from 13 RCTs contributed to a seven-node consistency model including no treatment, BB, ARB, ARB+BB, calcium channel blockers (CCB), angiotensin converting enzyme inhibitor (ACEI), and combination of BB and renin inhibitor (RI). As compared with no treatment, RI+BB showed significant protection of aortic root growth (standardized mean difference [SMD]= -1.90, 95% confidence interval [CI]: -2.91, -0.89), followed by ARB+BB (SMD= -1.75, 95%CI: -2.40, -1.10). Nevertheless, no significant findings were seen in other clinical outcomes. CONCLUSIONRI or ARB added on BB appear to be the optimal medication strategies to slow the progression of aortic root growth in MFS patients. However, we found no statistically significant difference in the risk of aortic surgeries, adverse effects, cardiovascular and all-cause mortality among medications. More RCTs with longer follow-up periods or bigger populations are needed to draw stronger evidence for clinical practices.
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