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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".