What is the methodological quality and predictive performance of prognostic prediction models for long-term outcomes in psoriasis? A protocol for a systematic review and meta-analysis

13 Jul 20265 min read1 viewsJournal Feed

GIST (Key Takeaways)

Introduction The clinical course of psoriasis is characterised by marked heterogeneity. Despite the availability of standardised assessment instruments, long-term outcomes at the individual level - encompassing the durability of treatment response, the risk of relapse and the development of comorbidities - remain difficult to predict.
To address this uncertainty, numerous prognostic prediction models have been developed; however, their methodological quality, predictive performance and clinical applicability have not yet been systematically appraised. Methods and analysis A systematic search will be conducted across six databases - China National Knowledge Infrastructure, Wanfang Data, VIP Chinese Journal Database, PubMed, Cochrane Library and Embase - from inception to 31 December 2025, using a search strategy combining controlled vocabulary and free-text terms related to psoriasis, prognostic prediction models and relevant outcome measures.
Two reviewers will independently perform study selection, data extraction using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and risk-of-bias assessment using the Prediction model Risk Of Bias ASsessment Tool. Model performance metrics, including discrimination (eg, the C-statistic) and calibration, will be systematically extracted.

Clinical Editorial

BMJ Open published a clinical update in Research Highlights on 13 Jul 2026.

The item focuses on What is the methodological quality and predictive performance of prognostic prediction models for long-term outcomes in psoriasis?

A protocol for a systematic review and meta-analysis.

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