The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study

Andrew Moriarty*, Lewis Paton, Kym IE Snell, Richard D Riley, Joshua E J Buckman, Simon Gilbody, Carolyn A Chew-Graham, Shehzad Ali , Stephen Pilling, Nick Meader, Bob Phillips, Peter A Coventry, Jaime Delgadillo, David A Richards, Chris Salisbury, Dean McMillan

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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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
Original languageEnglish
Article number12 (2021)
Number of pages13
JournalDiagnostic and Prognostic Research
Volume5
Early online date2 Jul 2021
DOIs
Publication statusE-pub ahead of print - 2 Jul 2021

Keywords

  • RELAPSE
  • RECURRENCE
  • DEPRESSION
  • PROGNOSIS
  • PROGNOSTIC MODEL
  • PREDICTIVE MODEL

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