Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies.
Adult psychiatry
Data Interpretation, Statistical
Depression
Journal
BMJ mental health
ISSN: 2755-9734
Titre abrégé: BMJ Ment Health
Pays: England
ID NLM: 9918521385306676
Informations de publication
Date de publication:
28 Oct 2024
28 Oct 2024
Historique:
received:
27
06
2024
accepted:
09
10
2024
medline:
29
10
2024
pubmed:
29
10
2024
entrez:
28
10
2024
Statut:
epublish
Résumé
Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual's risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention. The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care. Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal-external cross-validation. Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55-0.65)) and miscalibration concerns (calibration slope 0.81 (0.31-1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28-0.67), p<0.001); this remained statistically significant after correction for multiple significance testing. We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse. Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these. NCT04666662.
Sections du résumé
BACKGROUND
BACKGROUND
Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual's risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention.
OBJECTIVE
OBJECTIVE
The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care.
METHODS
METHODS
Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal-external cross-validation.
FINDINGS
RESULTS
Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55-0.65)) and miscalibration concerns (calibration slope 0.81 (0.31-1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28-0.67), p<0.001); this remained statistically significant after correction for multiple significance testing.
CONCLUSIONS
CONCLUSIONS
We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse.
CLINICAL IMPLICATIONS
CONCLUSIONS
Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these.
TRIAL REGISTRATION NUMBER
BACKGROUND
NCT04666662.
Identifiants
pubmed: 39467616
pii: bmjment-2024-301226
doi: 10.1136/bmjment-2024-301226
pii:
doi:
Banques de données
ClinicalTrials.gov
['NCT04666662']
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.