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

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

A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

“…However, these have been hampered by methodological problems, including small sample sizes, inappropriate handling of missing data, and a lack of validation, and they have largely failed to make accurate predictions [44]. Other studies to develop more accurate models are planned [45], but no findings are available yet, and the clinical utility of any such models are yet to be established. Patient stratification techniques such as LPA offer an alternative means of predicting outcomes which may have greater face-validity for clinicians and patients because the characteristics of each profile can be described in more accessible terms [40].…”

Predicting Remission among Perinatal Women with Depression in Rural Pakistan: A Prognostic Model for Task-Shared Interventions in Primary Care Settings

“…The choice of the predictors included in the prognostic model building process was based on our literature review of previous key papers exploring prognostic models of depression [ 20 , 21 , 23 , 26 , 35 , 36 ]. This strategy was further augmented by consensus opinion by clinical experts in the team.…”

“…Demographic factors, such as older age and living alone, and psychosocial predictors, such as exposure to stressful life events, disability, poor social support network, and interpersonal difficulties, also predicted poorer prognosis. Finally, biochemical tests, such as higher serum levels of the corticotrophin-releasing hormones and higher scores on symptom checklists for depression, were also associated with a poorer prognosis requiring intensive treatment strategies [ 20 , 21 , 23 , 26 , 35 , 36 ].…”

Predicting Remission among Perinatal Women with Depression in Rural Pakistan: A Prognostic Model for Task-Shared Interventions in Primary Care Settings

“…Finally, biochemical tests such as higher serum levels of the corticotrophin-releasing hormones and higher scores on symptom checklists for depression were also associated with a poorer prognosis requiring intensive treatment strategies [20,21,23,26,35,36].…”

“…A minimum of 708 participants based on 248 events (assuming an outcome prevalence = . 35 and an EPP = 16.52), expected c-statistic of 0.75 and 15 parameters was found to be adequate [42].…”