AI Article Synopsis
- High disease burden from mood, anxiety, and psychotic disorders can be partly addressed through clinical prediction models, but many lack external validation, affecting their reliability in real-world settings.
- The study systematically reviewed 28 externally validated prediction models, mainly focused on mood disorders, highlighting the importance of clinical predictors like symptom severity while finding concerns about the methodological quality of some studies.
- Overall, the research found fair discrimination performance of models, suggesting that more external validation and careful consideration of clinical contexts are needed before implementing these prediction models in practice.
Article Abstract
Background: Suboptimal treatment outcomes contribute to the high disease burden of mood, anxiety or psychotic disorders. Clinical prediction models could optimise treatment allocation, which may result in better outcomes. Whereas ample research on prediction models is performed, model performance in other clinical contexts (i.e. external validation) is rarely examined. This gap hampers generalisability and as such implementation in clinical practice.
Aims: Systematically appraise studies on externally validated clinical prediction models for estimated treatment outcomes for mood, anxiety and psychotic disorders by (1) reviewing methodological quality and applicability of studies and (2) investigating how model properties relate to differences in model performance.
Method: The review and meta-analysis protocol was prospectively registered with PROSPERO (registration number CRD42022307987). A search was conducted on 8 November 2021 in the databases PubMED, PsycINFO and EMBASE. Random-effects meta-analysis and meta-regression were conducted to examine between-study heterogeneity in discriminative performance and its relevant influencing factors.
Results: Twenty-eight studies were included. The majority of studies ( = 16) validated models for mood disorders. Clinical predictors (e.g. symptom severity) were most frequently included ( = 25). Low methodological and applicability concerns were found for two studies. The overall discrimination performance of the meta-analysis was fair with wide prediction intervals (0.72 [0.46; 0.89]). The between-study heterogeneity was not explained by number or type of predictors but by disorder diagnosis.
Conclusions: Few models seem ready for further implementation in clinical practice to aid treatment allocation. Besides the need for more external validation studies, we recommend close examination of the clinical setting before model implementation.
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| http://dx.doi.org/10.1192/bjo.2024.789 | DOI Listing |
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698186 | PMC |
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