The 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
Notice bibliographique
Résumé
ABSTRACT 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 Develop and validate a prognostic model to predict risk of relapse of depression in primary care. 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 Residual depressive symptoms [Odds ratio (OR): 1.13 (95% CI: 1.07-1.20), p<0.001] and baseline depression severity [OR: 1.07 (1.04-1.11), 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 We cannot currently predict risk of depression relapse with sufficient accuracy in a primary care setting, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse. Clinical implications 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 (e.g. residual depressive symptoms) and targeted towards these. What is already known on this topic Relapse contributes to the morbidity and burden associated with depression and, while there is robust research confirming predictors of relapse, individualised risk prediction is a challenge. What this study adds We found that it is not possible to accurately predict individualised risk of relapse using prognostic factors that are routinely collected and available in primary care. We found evidence to suggest that relationship status (not being in a relationship) is associated with increased risk of relapse and warrants confirmatory prognostic factor research. How this study might affect research, practice or policy Future prognosis research in this area should focus on exploring the feasibility of routinely measuring and documenting additional prognostic factors in primary care (for example, adverse childhood events, relationship status and social support) and including these in prognostic models. Until we can more accurately identify individuals at increased risk of relapse, commonly used acute-phase treatments could be optimised to better prepare for and mitigate the risk of relapse and there is a need for brief, scalable relapse prevention interventions that could be provided more widely.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,091 | 0,113 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,011 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».