Keele Research Repository

OBJECTIVE: To evaluate the performance and applicability of multivariable prediction models for osteoarthritis (OA). METHODS: Systematic review and narrative synthesis using three databases (EMBASE, PubMed, Web of Science; inception to December 2021). We included general population longitudinal studies reporting derivation, comparison, or validation of multivariable models to predict individual risk of OA incidence, defined by recognised clinical or imaging criteria. We excluded studies reporting prevalent OA and joint arthroplasty outcome. Paired reviewers independently performed article selection, data extraction, and risk of bias assessment. Model performance, calibration and retained predictors were summarised. RESULTS: 26 studies were included reporting 31 final multivariable prediction models for incident knee (23), hip (4), hand (3) and any-site OA (1), with a median of outcome events of 121.5 (range: 27-12,803), median prediction horizon of 8 years (2-41), and a median of 6 predictors (3-24). Age, body mass index, previous injury, and occupational exposures were among the most commonly included predictors. Model discrimination after validation was generally acceptable to excellent (Area Under the Curve = 0.70 to 0.85). Either internal or external validation processes were used in most models although risk of bias was often judged to be high with limited applicability to mass application in diverse populations. CONCLUSION: Despite growing interest in multivariable prediction models for incident OA, there remains a predominant focus on the knee, reliance on data from a small pool of appropriate cohort datasets, and concerns over general population applicability.