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网络荟萃分析随机对照试验的药理学、心理治疗、运动和协作护理干预对冠心病患者抑郁症状的影响:混合系统评价的系统评价方案。

Network meta-analysis of randomised trials of pharmacological, psychotherapeutic, exercise and collaborative care interventions for depressive symptoms in patients with coronary artery disease: hybrid systematic review of systematic reviews protocol.

机构信息

Department of Health Psychology, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Ireland.

School of Psychology, Queen's University Belfast, University Road, Belfast, BT71NN, Northern Ireland, UK.

出版信息

Syst Rev. 2019 Mar 16;8(1):71. doi: 10.1186/s13643-019-0985-9.

Abstract

BACKGROUND

Depression is common in patients with coronary artery disease (CAD) and is associated with poorer outcomes and higher costs. Several randomised controlled trials (RCTs) targeting depression, of various modalities (including pharmacological, psychotherapeutic and other approaches), have been conducted and summarised in pairwise meta-analytic reviews. However, no study has considered the cumulative evidence within a network, which can provide valuable indirect comparisons and information about the relative efficacy of interventions. Therefore, we will adopt a review of review methodology to develop a network meta-analysis (NMA) of depression interventions for depression in CAD.

METHODS

We will search relevant databases from inception for systematic reviews of RCTs of depression treatments for people with CAD, supplementing this with comprehensive searches for recent or ongoing studies. We will extract data from and summarise characteristics of individual RCTs, including participants, study characteristics, outcome measures and adverse events. Cochrane risk of bias ratings will also be extracted or if not present will be conducted by the authors. RCTs that compare depression treatments (grouped as pharmacological, psychotherapeutic, combined pharmacological/psychotherapeutic, exercise, collaborative care) to placebo, usual care, waitlist control or attention controls, or directly in head-to-head comparisons, will be included. Primary outcomes will be the change in depressive symptoms (summarised with a standardised mean difference) and treatment acceptability (treatment discontinuation: % of people who withdrew). Secondary outcomes will include change in 6-month depression outcomes, health-related quality of life (HRQoL), mortality, cardiovascular morbidity, health services use and adverse events. Secondary analyses will form further networks with individual anti-depressants and psychotherapies. We will use frequentist, random effects multivariate network meta-analysis to synthesise the evidence for depression intervention and to achieve a ranking of treatments, using Stata. Rankograms and surface under the cumulative ranking curves will be used for treatment ranking. Local and global methods will evaluate consistency. GRADE will be used to assess evidence quality for primary outcomes.

DISCUSSION

The present review will address uncertainties about the evidence in terms of depression management in CAD and may allow for a ranking of treatments, including providing important information for future research efforts.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018108293.

摘要

背景

抑郁症在冠心病(CAD)患者中很常见,与较差的预后和更高的成本相关。已经进行了几项针对抑郁症的随机对照试验(RCT),并对其进行了汇总,这些试验采用了各种方式(包括药物治疗、心理治疗和其他方法),并以两两荟萃分析综述的形式呈现。然而,尚无研究考虑网络内的累积证据,这可以提供有价值的间接比较和有关干预措施相对疗效的信息。因此,我们将采用综述综述的方法,对 CAD 患者抑郁症的抑郁症干预措施进行网络荟萃分析(NMA)。

方法

我们将从一开始就搜索相关数据库,以系统地综述针对 CAD 患者抑郁症治疗的 RCT,同时全面搜索最近或正在进行的研究。我们将从个体 RCT 中提取数据并总结其特征,包括参与者、研究特征、结局测量和不良事件。还将提取 Cochrane 偏倚风险评分,或者如果不存在,则由作者进行评分。将纳入比较抑郁症治疗(分为药物治疗、心理治疗、药物联合心理治疗、运动、协作护理)与安慰剂、常规护理、等待名单对照或注意对照,或直接进行头对头比较的 RCT。主要结局将是抑郁症状的变化(用标准化均数差来总结)和治疗可接受性(治疗中断:退出人数的百分比)。次要结局将包括 6 个月抑郁结局、健康相关生活质量(HRQoL)、死亡率、心血管发病率、卫生服务使用和不良事件的变化。二次分析将使用个体抗抑郁药和心理疗法形成进一步的网络。我们将使用频率论、随机效应多变量网络荟萃分析来综合抑郁症干预措施的证据,并使用 Stata 对治疗方法进行排名。排名图和累积排名曲线下的曲面将用于治疗排名。局部和全局方法将评估一致性。GRADE 将用于评估主要结局的证据质量。

讨论

本综述将解决 CAD 中抑郁症管理证据方面的不确定性,并可能对治疗方法进行排名,为未来的研究工作提供重要信息。

系统综述注册

PROSPERO CRD42018108293。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a4f1/6420728/0d9263e33c95/13643_2019_985_Fig1_HTML.jpg

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