Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Division of Psychiatry, University College London, London, UK.
J Affect Disord. 2018 Feb;227:164-182. doi: 10.1016/j.jad.2017.10.008. Epub 2017 Oct 10.
Predictor analyses of late-life depression can be used to identify variables associated with outcomes of treatments, and hence ways of tailoring specific treatments to patients. The aim of this review was to systematically identify, review and meta-analyse predictors of outcomes of any type of treatment for late-life depression.
Pubmed, Embase, CINAHL, Web of Science and PsycINFO were searched for studies published up to December 2016. Primary and secondary studies reported treatment predictors from randomised controlled trials of any treatment for patients with major depressive disorder aged over 60 were included. Treatment outcomes included response, remission and change in depression score.
Sixty-seven studies met the inclusion criteria. Of 65 identified statistically significant predictors, only 7 were reported in at least 3 studies. Of these, 5 were included in meta-analyses, and only 3 were statistically significant. Most studies were rated as being of moderate to strong quality and satisfied key quality criteria for predictor analyses.
The searches were limited to randomised controlled trials and most of the included studies were secondary analyses.
Baseline depression severity, co-morbid anxiety, executive dysfunction, current episode duration, early improvement, physical illnesses and age were reported as statistically significant predictors of treatment outcomes. Only the first three were significant in meta-analyses. Subgroup analyses showed differences in predictor effect between biological and psychosocial treatment. However, high heterogeneity and small study numbers suggest a cautious interpretation of results. These predictors were associated with various mechanisms including brain pathophysiology, perceived social support and proposed distinct types of depressive disorder. Further investigation of the clinical utility of these predictors is suggested.
对老年期抑郁症的预测因素分析可用于识别与治疗结果相关的变量，从而为患者定制特定的治疗方法。本研究旨在系统地识别、评价和荟萃分析任何类型的老年期抑郁症治疗结果的预测因素。
检索 Pubmed、Embase、CINAHL、Web of Science 和 PsycINFO 数据库，时间截至 2016 年 12 月，纳入报道了 60 岁以上患有重性抑郁障碍患者的任何治疗方法的随机对照试验中治疗预测因素的原始研究和二次研究。治疗结局包括反应、缓解和抑郁评分变化。
共 67 项研究符合纳入标准。在 65 项确定有统计学意义的预测因素中，仅有 7 项在至少 3 项研究中报道。其中，5 项被纳入荟萃分析，但仅有 3 项具有统计学意义。大多数研究的质量被评为中等或偏强，满足预测分析的关键质量标准。
该检索仅局限于随机对照试验，且大部分纳入研究为二次分析。
基线抑郁严重程度、共病焦虑、执行功能障碍、当前发作持续时间、早期改善、躯体疾病和年龄被报道为治疗结局的有统计学意义的预测因素。只有前三个在荟萃分析中具有统计学意义。亚组分析显示生物治疗和心理治疗的预测因素效应存在差异。然而，由于异质性高和研究数量少，建议谨慎解释结果。这些预测因素与各种机制相关，包括脑病理生理学、感知到的社会支持和提出的不同类型的抑郁障碍。建议进一步研究这些预测因素的临床实用性。
