Moriarty, AS, Paton, LW, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Buckman, JEJ, Gilbody, S, Chew-Graham, CA ORCID: https://orcid.org/0000-0002-9722-9981, Ali, S, Pilling, S, Meader, N, Phillips, B, Coventry, PA, Delgadillo, J, Richards, DA, Salisbury, C and McMillan, D (2021) The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study. Diagnostic and Prognostic Research, 5 (1). 12 - ?.

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Abstract

BACKGROUND: Most patients who present with depression are treated in primary care by general practitioners (GPs). Relapse of depression is common (at least 50% of patients treated for depression will relapse after a single episode) and leads to considerable morbidity and decreased quality of life for patients. The majority of patients will relapse within 6 months, and those with a history of relapse are more likely to relapse in the future than those with no such history. GPs see a largely undifferentiated case-mix of patients, and once patients with depression reach remission, there is limited guidance to help GPs stratify patients according to risk of relapse. We aim to develop a prognostic model to predict an individual's risk of relapse within 6-8 months of entering remission. The long-term objective is to inform the clinical management of depression after the acute phase. METHODS: We will develop a prognostic model using secondary analysis of individual participant data drawn from seven RCTs and one longitudinal cohort study in primary or community care settings. We will use logistic regression to predict the outcome of relapse of depression within 6-8 months. We plan to include the following established relapse predictors in the model: residual depressive symptoms, number of previous depressive episodes, co-morbid anxiety and severity of index episode. We will use a "full model" development approach, including all available predictors. Performance statistics (optimism-adjusted C-statistic, calibration-in-the-large, calibration slope) and calibration plots (with smoothed calibration curves) will be calculated. Generalisability of predictive performance will be assessed through internal-external cross-validation. Clinical utility will be explored through net benefit analysis. DISCUSSION: We will derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. Assuming the model has sufficient predictive performance, we outline the next steps including independent external validation and further assessment of clinical utility and impact. STUDY REGISTRATION: ClinicalTrials.gov ID: NCT04666662.

Item Type: Article
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords: relapse; recurrence; depression; prognosis; prognostic model; predictive model
Subjects: R Medicine > R Medicine (General)
R Medicine > RC Internal medicine > RC435 Psychiatry
Divisions: Faculty of Medicine and Health Sciences > School of Medicine
Related URLs:
Depositing User: Symplectic
Date Deposited: 06 Jul 2021 14:01
Last Modified: 06 Jul 2021 14:01
URI: https://eprints.keele.ac.uk/id/eprint/9802

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