Abstract
BackgroundMost 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.MethodsWe 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.DiscussionWe 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 registrationClinicalTrials.gov ID: NCT04666662
Highlights
Depression is the leading cause of disability worldwide [1], and most people with depression are treated in primary care [2]
In order to increase the sample size available for model development, we identified further studies: first, by searching all the National Institute for Health Research (NIHR)-funded Randomised controlled trial (RCT) of primary care-based interventions for depression, and second, by reference to the search results from a recent individual participant data (IPD) meta-analysis of RCTs of depression interventions [28]
Nonpharmacological interventions are more likely to affect outcomes in a comparable way; Start-point There are three important time-points: baseline for the RCT; follow-up 1 (FU1; to diagnose remission; t=0 for our prediction model study and corresponds with a 4-month follow-up for RCTs) and follow-up 2 (FU2; the intended prediction time and occurs at either 6 or 8 months after t=0; patient either relapses or does not relapse)
Summary
The methods have been developed in accordance with those recommended by the PROGnosis RESearch Strategy (PROGRESS) initiative [19, 20], and the prognostic model will be published according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Statement [21]. End-point (relapse) Patients will be coded as relapsed if they fulfil the following criteria within 6 to 8 months post-remission: (i) PHQ-9 score above the diagnostic cut-off (10 or more) and (ii) ≥5 points greater than their symptom score at the time of remission As above, this is consistent with accepted criteria for reliable and clinically significant deterioration [33, 34]. While there is some weak evidence that the relapse risk increases with each successive depressive episode, the prognostic effect of previous episodes on recurrence is strongest when comparing any number of previous episodes to no previous episodes [37] This finding from the pre-existing literature is likely to be helpful for a primary care-based prognostic model, as there is potential difficulty in achieving a precise number of previous episodes in clinical practice. Gender and ethnicity are not well supported by the preexisting evidence as being associated with relapse [37, 43, 44], but are routinely collected during RCTs and
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