In health care, clinical decision making is typically based on diagnostic findings. Rehabilitation clinicians commonly rely on pathoanatomical diagnoses to guide treatment and define prognosis. Targeting prognostic factors is a promising way for rehabilitation clinicians to enhance treatment decision-making processes, personalize rehabilitation approaches, and ultimately improve patient outcomes. This can be achieved by using prognostic tools that provide accurate estimates of the probability of future outcomes for a patient in clinical practice. Most literature reviews of prognostic tools in rehabilitation have focused on prescriptive clinical prediction rules (pCPR). These studies highlight notable methodological issues and conclude that these tools are neither valid nor useful for clinical practice. This has raised the need to open the scope of research to understand what makes a quality prognostic tool that can be used in clinical practice. Methodological guidance in prognosis research has emerged in the last decade, encompassing exploratory studies on the development of prognosis and prognostic models. Methodological rigor is essential to develop prognostic tools, as only prognostic models developed and validated through a rigorous methodological process should guide clinical decision making. This Perspective argues that rehabilitation clinicians need to master the identification and use of prognostic tools to enhance their capacity to provide personalized rehabilitation. It is time for prognosis research to look for prognostic models that were developed and validated following a comprehensive process before being simplified into suitable tools for clinical practice. New models, or rigorous validation of current models, are needed. The approach discussed in this Perspective offers a promising way to overcome the limitations of most models and provide clinicians with quality tools for personalized rehabilitation approaches.

Objective The purpose of this systematic review was to identify and appraise externally validated prognostic models to predict a patient’s health outcomes relevant to physical rehabilitation of musculoskeletal conditions. Methods We systematically reviewed 8 databases and reported our findings according to PRISMA 2020. An information specialist designed a search strategy to identify externally validated prognostic models for musculoskeletal conditions. Paired reviewers independently screened the title, abstract, full-text and conducted data extraction. We extracted characteristics of included studies (eg, country, study design), prognostic models (eg, performance measures, type of model) and predicted clinical outcomes (eg, pain, disability). We assessed the risk of bias and concerns of applicability using the Prediction model Risk of Bias Assessment Tool (PROBAST). We proposed and used a 5-step method to determine which prognostic models were clinically valuable. Results We found 4896 citations, read 300 full-text articles, and included 46 papers (37 distinct models). Prognostic models were externally validated for the spine, upper limb, lower limb conditions, and musculoskeletal trauma, injuries, and pain. All studies presented a high risk of bias. Half of the models showed low concerns for applicability. Reporting of calibration and discrimination performance measures were often lacking. We found 6 externally validated models with adequate measures which could be deemed clinically valuable [ie, 1) STart Back Screening Tool, 2) WORRK model, 3) Da Silva model, 4) PICKUP model, 5) Schellingerhout rule and 6) Keene model]. Despite having a high risk of bias, which is mostly explained by the very conservative properties of the PROBAST tool, the 6 models remain clinically relevant. Conclusions We found 6 externally validated prognostic models developed to predict patients’ health outcomes that were clinically relevant to the physical rehabilitation of musculoskeletal conditions. Impact Our results provide clinicians with externally validated prognostic models to help them better predict patients’ clinical outcomes and facilitate personalized treatment plans. Incorporating clinically valuable prognostic models could inherently improve the value of care provided by physical therapists.

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