Personalizing rehabilitation for individuals with musculoskeletal impairments: Feasibility of implementation of the Measures Associated to Prognostic (MAPS) tool.

INTRODUCTION: The Measures Associated to PrognoStic (MAPS) tool is a standardized questionnaire that integrates validated prognostic tools to detect the presence of biopsychosocial prognostic factors in patients consulting for musculoskeletal disorders. PURPOSE: The objectives were to assess the: 1) feasibility of implementation of the MAPS tool, 2) clinicians' acceptability of the dashboard, and 3) patients' acceptability of the MAPS tool. METHODS: Twenty physiotherapists and two occupational therapists from seven outpatient musculoskeletal clinics were recruited to implement the MAPS tool during a 3-month timeframe, where new patients completed the questionnaire upon initial assessment. The results were presented to the clinicians via a dashboard. Surveys and semi-structured interviews were conducted to measure feasibility and acceptability. RESULTS: Six out of 11 feasibility criteria (55%) and 21 out of 24 acceptability criteria (88%) reached the a priori threshold for success. The interviews allowed us to identify three main themes to facilitate implementation: 1) limiting the burden, 2) ensuring patients' understanding of the tool's purpose, and 3) integrating the dashboard as a clinical information tool. CONCLUSION: Our quantitative and qualitative results support the feasibility of implementation and acceptability of the MAPS tool pending minor adjustments. Depicting the patients' prognostic profile has the potential to help clinicians optimize their interventions for patients presenting with musculoskeletal disorders.

Establishing the prognostic profile of patients with work-related musculoskeletal disorders: Development and acceptability of the MAPS questionnaire.

PURPOSE: Work-related musculoskeletal disorders (WRMD) are the most common causes of disability worldwide and are associated with significant use of healthcare. One way to optimize the clinical outcomes of injured workers receiving rehabilitation is to identify and address individual prognostic factors (PF), which can facilitate the personalization of the treatment plan. As there is no pragmatic and systematic method to collect prognostic-related data, the purpose of the study was to develop and assess the acceptability of a set of questionnaires to establish the "prognostic profile" of workers with WRMD. METHODS: We utilized a multistep process to inform the acceptability of the Measures Associated to PrognoStic (MAPS) questionnaire. During STEP-1, a preliminary version of the was developed through a literature search followed by an expert consensus including a patient-advisor. During STEP-2, future users (rehabilitation professionals, healthcare administrators and compensation officers) were consulted through an online survey and were asked to rate the relevance of each content item; items that obtained ≥80% of "totally agree" answers were included. They were also asked to prioritize PF according to their usefulness for clinical decision-making, as well as perceived efficacy to enhance the treatment plan. RESULTS: The questionnaire was developed with three categories: the outcome predicted, the unique PF, and prognostic tools. Personal PF (i.e.: coping strategies, fear-avoidance beliefs), pain related PF (i.e.: pain intensity/severity, duration of pain), and work-related PF (i.e.: work physical demands, work accommodations) were identified to be totally relevant and included in the questionnaire. 84% of the respondents agreed that their patients could complete the MAPS questionnaire in their clinical setting, while 75% totally agreed that the questionnaire is useful to personalize rehabilitation interventions. CONCLUSION: The MAPS questionnaire was deemed acceptable to establish the "prognostic profile" of injured workers and help the clinicians in the treatment decision-making process.

Prognostic factors specific to work-related musculoskeletal disorders: An overview of recent systematic reviews.

PURPOSE: Work-related injuries affect a considerable number of people each year and represent a significant burden for society. To reduce this burden, optimizing rehabilitation care by integrating prognostic factors (PF) into the clinical decision-making process is a promising way to improve clinical outcomes. The aim of this study was to identify PF specific to work-related musculoskeletal disorders. METHODS: We performed an overview of systematic reviews reporting on PF that had the following outcomes of interest: Return to work, pain, disability, functional status, or poor outcomes. Each extracted PF was categorized according to its level of evidence (grade A or B) and whether it was modifiable or not. The risk of bias of each study was assessed with the ROBIS tool. RESULTS: We retrieved 757 citations from 3 databases. After removing 307 duplicates, 450 records were screened, and 20 studies were retained. We extracted a total of 20 PF with a Grade A recommendation, where 7 were deemed modifiable, 11 non-modifiable and 2 were index test. For example, return to work expectations, previous sick leave, delay in referral and pain intensity were found to be predictors of return-to-work outcomes. We also identified 17 PF with a Grade B recommendation, where 11 were deemed modifiable. For example, poor general health, negative recovery expectations, coping and fear-avoidance beliefs, pain severity, and particularly physical work were found to predict return to work outcomes. CONCLUSION: We found numerous modifiable PFs that can help clinicians personalize their treatment plan beyond diagnostic-related information for work-related musculoskeletal disorders.

Six Externally Validated Prognostic Models Have Potential Clinical Value to Predict Patient Health Outcomes in the Rehabilitation of Musculoskeletal Conditions: A Systematic Review.

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 (MSK) conditions. METHODS: We systematically reviewed 8 databases and reported our findings according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020. An information specialist designed a search strategy to identify externally validated prognostic models for MSK conditions. Paired reviewers independently screened the title, abstract, and full text and conducted data extraction. We extracted characteristics of included studies (eg, country and study design), prognostic models (eg, performance measures and type of model) and predicted clinical outcomes (eg, pain and disability). We assessed the risk of bias and concerns of applicability using the prediction model risk of bias assessment tool. 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 MSK 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 was often lacking. We found 6 externally validated models with adequate measures, which could be deemed clinically valuable [ie, (1) STart Back Screening Tool, (2) Wallis Occupational Rehabilitation RisK 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. CONCLUSION: We found 6 externally validated prognostic models developed to predict patients' health outcomes that were clinically relevant to the physical rehabilitation of MSK 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.

Mastering Prognostic Tools: An Opportunity to Enhance Personalized Care and to Optimize Clinical Outcomes in Physical Therapy.

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. 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, because 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. IMPACT: Prognostic research can be applied to clinical rehabilitation; this Perspective proposes solutions to develop high-quality prognostic models to optimize patient outcomes.