Medical treatment in acromegaly: a network meta-analysis
作者
Chrysi Kaparounaki,Mirela Diana Ilie,Dario De Alcubierre,Panagiotis Anagnostis,Anna‐Bettina Haidich,Andrea M. Isidori,Olaf M. Dekkers,Dimitrios G. Goulis,Gérald Raverot
Abstract Objective Acromegaly is a rare disorder caused by a growth hormone-secreting pituitary adenoma. Clinical trial evidence for its management is limited. This study compared medical treatments for acromegaly through a network meta-analysis, assessing biochemical and radiological responses. Design A systematic review and network meta-analysis were conducted following the preferred reporting items for systematic reviews and network meta-analyses guidelines and Cochrane Handbook recommendations (PROSPERO registration: CRD42023364373). Methods PubMed, Scopus and Web of Science were searched up to June 2024. Included studies were randomized controlled trials and non-randomized studies evaluating the efficacy or safety of acromegaly treatments. Primary outcomes were the percentage of adjusted insulin-like growth factor 1 (IGF-1) normalization and tumor shrinkage. Results Twenty-seven studies, involving 4131 patients and 11 treatments were included. Pegvisomant was the best treatment for IGF-1 normalization, followed by pasireotide LAR. Both outperformed first-generation somatostatin analogs (SRLs) combined with dopamine agonists (OR 1.83; 95% CIs 1.37-2.46 and OR 1.46; 95% CIs 1.02-2.08, respectively; I2=41%). Octreotide LAR was superior to oral octreotide capsules (OR 5.41; 95% CIs 1.89-15.52). For tumor shrinkage, pasireotide LAR was more effective than SRLs (n=1059; OR 11.47; 95% CIs 1.5-87.64; I2=0%). Methodological heterogeneity may have affected comparability. Conclusions Our findings suggest pasireotide LAR and pegvisomant as the most effective treatments for IGF-1 normalization. Pasireotide LAR was the best treatment for tumor shrinkage, though the evidence base was limited, requiring cautious interpretation. Their potential role as first-line options after surgery requires further research. Clinical decisions should consider cost, safety and patient-specific parameters to optimize outcomes.