To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with depression who meet criteria for remission.Secondary objectives
- To describe the characteristics of models identified, including predictors and method of derivation (e.g. regression, machine learning, neural networks etc.).
- To review the net benefit of identified models, where this has been reported.
- To summarise the value of updating or modifying an existing prognostic model or identify whether the development of a novel prognostic model to predict relapse and recurrence in depression is required. We will make this decision through discussion involving the whole team and will be guided by risk of bias assessment and applicability of methods as well as predictive performance.
We anticipate between-study heterogeneity in model performance. Sources of heterogeneity in this case are likely to relate to population/case mix (e.g. age of participants and multimorbidity), study setting of models (e.g. differences between models developed in primary and secondary care settings), study design (e.g. follow-up time, source of data, outcome definition and sample size). All of these could prove to be significant sources of heterogeneity in this review and we will take them into account in the event that a meta-analysis is undertaken.
This is a protocol.