Background: Osteoarthritis is the most common degenerative joint disease diagnosed in clinical practice. It is associated with significant socioeconomic burden and poor quality of life, a large proportion of which is due to knee osteoarthritis (KOA), mainly driven by total knee arthroplasty (TKA). As the difficulty of being detected early and deficiency of disease-modifying drug, the focus of KOA is shifting to disease prevention and the treatment to delay its rapid progression. Thus, the

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... stic prediction models are called for, to stratify individuals to guide clinical decision making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA which concern about three clinical concerns: (1) the risk of developing KOA in general population; (2) the risk of receiving TKA in KOA patients; and (3) the outcome of TKA in KOA patients who plan to receive TKA.Methods: Studies will be identified by searching seven electronic databases. Title and abstract screening and full-text review will be accomplished by two independent reviewers. Data extraction instrument and critical appraisal instrument will be developed before formal assessment, and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and Prediction model Risk Of Bias ASsessment Tool (PROBAST). Prognostic prediction models will be summarized qualitatively. Quantitative metrics on predictive performance of these models will be synthesized with meta-analyses if appropriate.Discussion: Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation or impact assessment will be made.Systematic review registration: PROSPERO (registered, waiting for assessment, ID 203543)