Comparative efficacy and acceptability of antidepressant treatment in patients with post-stroke depression: a multiple-treatments meta-analysis. (December 2017)

• Record Type: Journal Article
• Authors: Sun, Yefei; Liang, Yifan; Jiao, Yang; Lin, Jueying; Qu, Huiling; Xu, Junjie; Zhao, Chuansheng
• Is Part Of: Lancet. Volume 390(2017)Supplement 4
• Page Start: S3
• Publication Date: 2017-12
• Subjects: Medicine -- Periodicals; Medicine -- Periodicals; Medicine; Medicine; Electronic journals; Periodicals; 610.5
• DOI: 10.1016/S0140-6736(17)33141-0
• Languages: English
• ISSNs: 0140-6736

Abstract

Abstract: Background: The question of which antidepressants should be preferred for the treatment of post-stroke depression is controversial, and pairwise meta-analyses cannot provide a hierarchy of these interventions. We aimed to integrate the available evidence to create hierarchies of the comparative efficacy and acceptability of treatments. Methods: We did a multiple-treatments meta-analysis (which uses both direct and indirect comparisons) of randomised controlled trials to compare antidepressants and placebo in the acute treatment of post-stroke depression. Electronic databases (MEDLINE, Embase, PsycINFO, Cochrane Central Register of Controlled Trials [CENTRAL], Web of Science) were searched up to Dec 31, 2016. We included only randomised controlled trials that compared antidepressants as monotherapy in patients with post-stroke depression. The patients had to have had a stroke diagnosed clinically or by CT scan or MRI and have been subsequently diagnosed with depression. We applied no inclusion restrictions on the basis of patient and study characteristics, such as age, sex, study dates, and class of antidepressants. The primary outcome was overall efficacy, defined as the mean change of the total depression score between baseline and 6 weeks. If 6-week data were not available, we used data from between 4 and 12 weeks (the datapoint closest to 6 weeks was given preference). The secondary outcome was acceptability, defined as risk of all-cause discontinuation. We tested for inconsistency with the design-by-treatment interaction model that provides a single inference, using the χ 2 test. This study has been registered with PROSPERO, number CRD42017054741. Findings: We identified 12 suitable trials, with data from 707 participants. 11 antidepressants were assessed in the trials, including placebo. All drugs were significantly more effective than placebo apart from sertraline (odds ratio [OR] −0·61, 95% CI −1·47 to 0·25), nefiracetam (OR 0·51, −0·17 to 1·19), and fluoxetine (OR 0·46, −0·35 to 1·27). For efficacy, standardised mean differences, compared with placebo, varied from −6·54 (95% CI −8·42 to −4·65) for the best drug (reboxetine) to 0·51 (−0·17 to 1·19) for the worst drug (nefiracetam). Reboxetine, paroxetine, and doxepin were the most efficacious treatments (ranked in order), the cumulative probabilities of which were 100%, 85·7%, and 83·2%, respectively. For acceptability, ORs compared with placebo ranged from 0·09 (95% CI 0·00 to 1·83) for paroxetine to 3·42 (0·73 to 15·91) for citalopram. Patients assigned to paroxetine had significantly lower all-cause discontinuation than those assigned to doxepin (OR 0·04, 95% CI 0·00 to 0·73), citalopram (OR 0·03, 0·00 to 0·78), and fluoxetine (OR 0·04, 0·00 to 0·89); all other comparisons were not significant. Paroxetine (cumulative probability 92·4%), placebo (63·5%), and sertraline (57·3%) were the most acceptable treatments. Comparison-adjusted funnel plots for efficacy and acceptability were largely symmetrical. For acceptability (all-cause discontinuation), we found no evidence of inconsistency (p=0·91). For efficacy, we detected some inconsistency (p=0·0230). Interpretation: After weighing up the efficacy and acceptability, we conclude that paroxetine might be the best choice in the acute treatment of post-stroke depression, and fluoxetine might be the worst choice. More large-scale and high-quality studies are needed to support these findings. Funding: The Program of Liaoning Province Education Administration (number LK201605) to YS, and the Shenyang Population and Health Technical Critical Special Project (number F16-206-9-01) and Program of the Distinguished Professor of Liaoning Province, Neurology, to CZ.