Psychosocial and psychological interventions for relapse prevention in schizophrenia: a systematic review and network meta-analysis

作者：Irene Bighelli, Alessandro Rodolico, Helena García‐Mieres, Gabi Pitschel‐Walz, Wulf‐Peter Hansen, Johannes Schneider‐Thoma, Spyridon Siafis, Hui Wu, Dongfang Wang, Georgia Salanti, Toshi A. Furukawa, Corrado Barbui, Stefan Leucht · 发表于：The Lancet Psychiatry · 年份：2021 · DOI：10.1016/s2215-0366(21)00243-1 · 被引用次数：285 · 研究领域：Schizophrenia research and treatment、Meta-analysis and systematic reviews、Health Policy Implementation Science

BACKGROUND: Many psychosocial and psychological interventions are used in patients with schizophrenia, but their comparative efficacy in the prevention of relapse is not known. We aimed to evaluate the efficacy, acceptability, and tolerability of psychosocial and psychological interventions for relapse prevention in schizophrenia. METHODS: To conduct this systematic review and network meta-analysis we searched for published and unpublished randomised controlled trials that investigated psychosocial or psychological interventions aimed at preventing relapse in patients with schizophrenia. We searched EMBASE, MEDLINE, PsycINFO, BIOSIS, Cochrane Library, WHO International Clinical Trials Registry Platform, and ClinicalTrials.gov up to Jan 20, 2020, and searched PubMed up to April 14, 2020. We included open and masked studies done in adults with schizophrenia or related disorders. We excluded studies in which all patients were acutely ill, had a concomitant medical or psychiatric disorder, or were prodromal or "at risk of psychosis". Study selection and data extraction were done by two reviewers independently based on published and unpublished reports, and by contacting study authors. Data were extracted about efficacy, tolerability, and acceptability of the interventions; potential effect moderators; and study quality and characteristics. The primary outcome was relapse measured with operationalised criteria or psychiatric hospital admissions. We did random-effects network meta-...