2025-04-23 アリゾナ大学
<関連情報>
- https://news.arizona.edu/news/new-precision-mental-health-care-approach-depression-addresses-unique-patient-needs
- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322124
成人うつ病に対する経験的に支持された5つの主要な治療法の中から、個別化された選択を支援する多変量予測モデルを開発。システマティックレビューと個人参加者データネットワークメタ解析の研究計画書 Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis
Ellen Driessen ,Orestis Efthimiou,Frederik J. Wienicke,Jasmijn Breunese,Pim Cuijpers,Thomas P. A. Debray,David J. Fisher,Marjolein Fokkema,Toshiaki A. Furukawa,Steven D. Hollon,Anuj H. P. Mehta,Richard D. Riley,Madison R. Schmidt,Jos W. R. Twisk,Zachary D. Cohen
PLOS One Published: April 23, 2025
DOI:https://doi.org/10.1371/journal.pone.0322124
Abstract
Background
Various treatments are recommended as first-line options in practice guidelines for depression, but it is unclear which is most efficacious for a given person. Accurate individualized predictions of relative treatment effects are needed to optimize treatment recommendations for depression and reduce this disorder’s vast personal and societal costs.
Aims
We describe the protocol for a systematic review and individual participant data (IPD) network meta-analysis (NMA) to inform personalized treatment selection among five major empirically-supported depression treatments.
Method
We will use the METASPY database to identify randomized clinical trials that compare two or more of five treatments for adult depression: antidepressant medication, cognitive therapy, behavioral activation, interpersonal psychotherapy, and psychodynamic therapy. We will request IPD from identified studies. We will conduct an IPD-NMA and develop a multivariable prediction model that estimates individualized relative treatment effects from demographic, clinical, and psychological participant characteristics. Depressive symptom level at treatment completion will constitute the primary outcome. We will evaluate this model using a range of measures for discrimination and calibration, and examine its potential generalizability using internal-external cross-validation.
Conclusions
We describe a state-of-the-art method to predict personalized treatment effects based on IPD from multiple trials. The resulting prediction model will need prospective evaluation in mental health care for its potential to inform shared decision-making. This study will result in a unique database of IPD from randomized clinical trials around the world covering five widely used depression treatments, available for future research.
