The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study.
Depression
Predictive model
Prognosis
Prognostic model
Recurrence
Relapse
Journal
Diagnostic and prognostic research
ISSN: 2397-7523
Titre abrégé: Diagn Progn Res
Pays: England
ID NLM: 101718985
Informations de publication
Date de publication:
02 Jul 2021
02 Jul 2021
Historique:
received:
14
01
2021
accepted:
19
05
2021
entrez:
3
7
2021
pubmed:
4
7
2021
medline:
4
7
2021
Statut:
epublish
Résumé
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. 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. 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. ClinicalTrials.gov ID: NCT04666662.
Sections du résumé
BACKGROUND
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
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
CONCLUSIONS
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
BACKGROUND
ClinicalTrials.gov ID: NCT04666662.
Identifiants
pubmed: 34215317
doi: 10.1186/s41512-021-00101-x
pii: 10.1186/s41512-021-00101-x
pmc: PMC8254312
doi:
Banques de données
ClinicalTrials.gov
['NCT04666662']
Types de publication
Journal Article
Langues
eng
Pagination
12Subventions
Organisme : Wellcome Trust
ID : 201292/Z/16/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0800472
Pays : United Kingdom
Organisme : National Institute for Health Research (GB)
ID : DRF-2018-11-ST2-044
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