Consensus on the definitions and descriptions of the domains of the OMERACT Core Outcome Set for shared decision making interventions in rheumatology trials.

OBJECTIVE: To gain consensus on the definitions and descriptions of the domains of the Outcome Measures in Rheumatology (OMERACT) core domain set for rheumatology trials evaluating shared decision making (SDM) interventions. METHODS: Following the OMERACT Handbook methods, our Working Group (WG), comprised of 90 members, including 17 patient research partners (PRPs) and 73 clinicians and researchers, had six virtual meetings in addition to email exchanges to develop draft definitions and descriptions. The WG then conducted an international survey of its members to gain consensus on the definitions and descriptions. Finally, the WG members had virtual meetings and e-mail exchanges to review survey results and finalize names, definitions and descriptions of the domains. RESULTS: WG members contributed to developing the definitions. Fifty-two members representing four continents and 13 countries completed the survey, including 15 PRPs, 33 clinicians and 37 researchers. PRPs and clinicians/researchers agreed with all definitions and descriptions with agreements ranging from 87% to 100%. Respondents suggested wording changes to the names, definitions and descriptions to better reflect the domains. Discussions led to further simplification and clarification to address common questions/concerns about the domains. CONCLUSION: Our WG reached consensus on the definitions and descriptions of the domains of the core domain set for rheumatology trials of SDM interventions. This step is crucial to understand each domain and provides the foundation to identify instruments to measure each domain for inclusion in the Core Outcome Measurement Set. CLINICAL SIGNIFICANCE: The current study provides consensus-based definitions and descriptions for the domains of the OMERACT core domain set for shared decision making interventions from patients/caregivers, clinicians and researchers. This is a crucial step to understand each domain and provides the foundation to identify instruments to measure each domain for inclusion in the Core Outcome Measurement Set for trials of SDM interventions.

OMERACT Core outcome measurement set for shared decision making in rheumatic and musculoskeletal conditions: a scoping review to identify candidate instruments.

OBJECTIVES: Shared decision making (SDM) is a central tenet in rheumatic and musculoskeletal care. The lack of standardization regarding SDM instruments and outcomes in clinical trials threatens the comparative effectiveness of interventions. The Outcome Measures in Rheumatology (OMERACT) SDM Working Group is developing a Core Outcome Set for trials of SDM interventions in rheumatology and musculoskeletal health. The working group reached consensus on a Core Outcome Domain Set in 2020. The next step is to develop a Core Outcome Measurement Set through the OMERACT Filter 2.2. METHODS: We conducted a scoping review (PRISMA-ScR) to identify candidate instruments for the OMERACT Filter 2.2 We systematically reviewed five databases (Ovid MEDLINE®, Embase, Cochrane Library, CINAHL and Web of Science). An information specialist designed search strategies to identify all measurement instruments used in SDM studies in adults or children living with rheumatic or musculoskeletal diseases or their important others. Paired reviewers independently screened titles, abstracts, and full text articles. We extracted characteristics of all candidate instruments (e.g., measured construct, measurement properties). We classified candidate instruments and summarized evidence gaps with an adapted version of the Summary of Measurement Properties (SOMP) table. RESULTS: We found 14,464 citations, read 239 full text articles, and included 99 eligible studies. We identified 220 potential candidate instruments. The five most used measurement instruments were the Decisional Conflict Scale (traditional and low literacy versions) (n=38), the Hip/Knee-Decision Quality Instrument (n=20), the Decision Regret Scale (n=9), the Preparation for Decision Making Scale (n=8), and the CollaboRATE (n=8). Only 44 candidate instruments (20%) had any measurement properties reported by the included studies. Of these instruments, only 57% matched with at least one of the 7-criteria adapted SOMP table. CONCLUSION: We identified 220 candidate instruments used in the SDM literature amongst people with rheumatic and musculoskeletal diseases. Our classification of instruments showed evidence gaps and inconsistent reporting of measurement properties. The next steps for the OMERACT SDM Working Group are to match candidate instruments with Core Domains, assess feasibility and review validation studies of measurement instruments in rheumatic diseases or other conditions. Development and validation of new instruments may be required for some Core Domains.

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.

Decisional needs assessment for patient-centred pain care in Canada: the DECIDE-PAIN study protocol.

INTRODUCTION: The 2021 Action Plan for Pain from the Canadian Pain Task Force advocates for patient-centred pain care at all levels of healthcare across provinces. Shared decision-making is the crux of patient-centred care. Implementing the action plan will require innovative shared decision-making interventions, specifically following the disruption of chronic pain care during the COVID-19 pandemic. The first step in this endeavour is to assess current decisional needs (ie, decisions most important to them) of Canadians with chronic pain across their care pathways. METHODS AND ANALYSIS: DesignGrounded in patient-oriented research approaches, we will perform an online population-based survey across the ten Canadian provinces. We will report methods and data following the CROSS reporting guidelines.SamplingThe Léger Marketing company will administer the online population-based survey to its representative panel of 500 000 Canadians to recruit 1646 adults (age ≥18 years old) with chronic pain according to the definition by the International Association for the Study of Pain (eg, pain ≥12 weeks). ContentBased on the Ottawa Decision Support Framework, the self-administered survey has been codesigned with patients and contain six core domains: (1) healthcare services, consultation and postpandemic needs, (2) difficult decisions experienced, (3) decisional conflict, (4) decisional regret, (5) decisional needs and (6) sociodemographic characteristics. We will use several strategies such as random sampling to improve survey quality. AnalysisWe will perform descriptive statistical analysis. We will identify factors associated with clinically significant decisional conflict and decision regret using multivariate analyses. ETHICS AND DISSEMINATION: Ethics was approved by the Research Ethics Board at the Research Centre of the Centre Hospitalier Universitaire de Sherbrooke (project #2022-4645). We will codesign knowledge mobilisation products with research patient partners (eg, graphical summaries and videos). Results will be disseminated via peer-reviewed journals and national and international conferences to inform the development of innovative shared decision-making interventions for Canadians with chronic pain.

Scoping review of rehabilitation care models for post COVID-19 condition.

Objective: To systematically map the current evidence about the characteristics of health systems, providers and patients to design rehabilitation care for post coronavirus disease 2019 (COVID-19) condition. Methods: We conducted a scoping review by searching the databases: MEDLINE®, Embase®, Web of Science, Cochrane COVID-19 Registry and Cochrane Central Register of Controlled Trials, from inception to 22 April 2022. The search strategy included terms related to (i) post COVID-19 condition and other currently known terminologies; (ii) care models and pathways; and (iii) rehabilitation. We applied no language or study design restrictions. Two pairs of researchers independently screened title, abstracts and full-text articles and extracted data. We charted the evidence according to five topics: (i) care model components and functions; (ii) safe delivery of rehabilitation; (iii) referral principles; (iv) service delivery settings; and (v) health-care professionals. Findings: We screened 13 753 titles and abstracts, read 154 full-text articles, and included 37 articles. The current evidence is conceptual and expert based. Care model components included multidisciplinary teams, continuity or coordination of care, people-centred care and shared decision-making between clinicians and patients. Care model functions included standardized symptoms assessment, telehealth and virtual care and follow-up system. Rehabilitation services were integrated at all levels of a health system from primary care to tertiary hospital-based care. Health-care workers delivering services within a multidisciplinary team included mostly physiotherapists, occupational therapists and psychologists. Conclusion: Key policy messages include implementing a multilevel and multiprofessional model; leveraging country health systems' strengths and learning from other conditions; financing rehabilitation research providing standardized outcomes; and guidance to increase patient safety.

Development and content validity of a rating scale for the pain and disability drivers management model.

BACKGROUND: Establishing the biopsychosocial profile of patients with low back pain (LBP) is essential to personalized care. The Pain and Disability Drivers Management model (PDDM) has been suggested as a useful framework to help clinicians establish this biopsychosocial profile. Yet, there is no tool to facilitate its integration into clinical practice. Thus, the aim of this study is to develop a rating scale and validate its content, to rapidly establish the patient's biopsychosocial profile, based on the five domains of the PDDM. METHODS: The tool was developed in accordance with the principles of the COSMIN methodology. We conducted three steps: 1) item generation from a comprehensive review, 2) refinement of the scale with clinicians' feedback, and 3) statistical analyses to assess content validity. To validate the item assessing with Likert scales, we performed Item level-Content Validity Index (I-CVI) analyses on three criteria (clarity, presentation and clinical applicability) with an a priori threshold of > 0.78. We conducted Average-Content Validity Index (Ave-CVI) analyses to validate the overall scale with a threshold of > 0.9. RESULTS: In accordance with the PDDM, we developed a 5-item rating scale (1 per domain) with 4 score options. We selected clinical instruments to screen for the presence or absence of problematic issues within each category of the 5 domains. Forty-two participants provided feedback to refine the scale's clarity, presentation, and clinical applicability. The statistical analysis of the latest version presented I-CVI above the threshold for each item (I-CVI ranged between 0.94 and 1). Analysis of the overall scale supported its validation (Ave-CVI = 0.96 [0.93;0.98]). CONCLUSION: From the 51 biopsychosocial elements contained within the 5 domains of the PDDM, we developed a rating scale that allows to rapidly screen for problematic issues within each category of the PDDM's 5 domains. Involving clinicians in the process allowed us to validate the content of the first scale to establish the patient's biopsychosocial profile for people with low back pain. Future steps will be necessary to continue the psychometric properties analysis of this rating scale.

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.

Inter-rater agreement of the Pain and Disability Drivers Management rating scale.

BACKGROUND: A framework to establish the biopsychosocial patient profile for persons with low back pain has been recently proposed and validated: The Pain and Disability Drivers Management model (PDDM). In order to facilitate its clinical integration, we developed the PDDM rating scale. OBJECTIVES: To determine the inter-rater agreement of the PDDM rating scale. A second objective was to determine if this inter-rater agreement varies according to the complexity of patients' clinical presentation. METHODS: We recruited physiotherapists during one-day workshops on the PDDM. We asked each participant to assess two clinical vignettes using the rating scale. One vignette presented a typical clinical presentation (moderate level of difficulty) and one presented an atypical presentation (complex level of difficulty). We determined inter-rater agreement with the proportion of participants who gave the same answer for each PDDM domain. RESULTS: For the typical vignette, the inter-rater agreement per domain was moderate to good (between 0.54 and 0.97). For the complex vignette, the inter-rater agreement per domain was poor to good (between 0.49 and 0.81). The comparison between the two vignettes showed a significant difference (p< 0.01) for nociceptive and cognitive-emotional domains. CONCLUSION: Overall performance indicates that the rating scale present adequate agreement for clinical use, but specific domains require further development.

How to objectively assess and observe maladaptive pain behaviors in clinical rehabilitation: a systematic search and review.

BACKGROUND: Cognitive-affective factors influence the perception of pain and disability. These factors can lead to pain behaviors (PB) that can persist and become maladaptive. These maladaptive PB will further increase the risk of chronicity or persistence of symptoms and disability. Thus, clinicians must be prepared to recognize maladaptive PB in a clinical context. To date, in the context of assessment in a rehabilitation setting, PB in clinical settings are poorly documented. The main objective of this study was to identify direct observation methods and critically appraise them in order to propose recommendations for practice. As a secondary objective, we explored and extracted the different observable PB that patients could exhibit and that clinicians could observe. METHODS: We conducted a comprehensive review on four databases with a generic search strategy in order to obtain the largest range of PB. For the first objective, a two-step critical appraisal used clinical criteria (from qualitative studies on barriers to implement routine measures) and psychometric criteria (from Brink and Louw critical appraisal tool) to determine which observation methods could be recommended for clinical practice. For the second objective, we extracted PB found in the literature to list potential PB that patients could exhibit, and clinicians could observe. RESULTS: From the 3362 retrieved studies, 47 met the inclusion criteria for the first objective. The clinical criteria allowed us to select three observation methods. After the psychometric step, two observation methods were retained and recommended for clinical practice: the Behavioral Avoidance Test-Back Pain (BAT-Back) and the Pain Behaviour Scale (PaBS). For the second objective, 107 studies met the inclusion criteria. The extraction of the PB allowed us to list a large range of PB and classify the data in 7 categories of PB. CONCLUSION: Our results allowed us to recommend two observation methods for clinical practice. However, these methods have limitations and are validated only in chronic low back pain populations. With the extraction of PB presented in the literature, we contribute to better prepare clinicians to recognize PB in all patients who are experiencing pain.

Optimizing management of low back pain through the pain and disability drivers management model: A feasibility trial.

INTRODUCTION: Self-reported levels of disability in individuals with low back pain (LBP) have not improved in the last decade. A broader perspective and a more comprehensive management framework may improve disability outcomes. We recently developed and validated the Low Back Pain and Disability Drivers Management (PDDM) model, which aims to identify the domains driving pain and disability to guide clinical decisions. The objectives of this study were to determine the applicability of the PDDM model to a LBP population and the feasibility of conducting a pragmatic trial, as well as to explore clinicians' perceived acceptability of the PDDM model's use in clinical settings. METHODS: This study was an one-arm prospective feasibility trial. Participants included physiotherapists working with a population suffering from LBP and their patients aged 18 years or older presenting with a primary complaint of LBP that sought a new referral and deemed fit for rehabilitation from private and public clinical settings. Clinicians participated in a one-day workshop on the integration of the PDDM model into their clinical practice, and were asked to report various LBP-related outcomes via self-reported questionnaires (i.e., impact of pain on physical function, nervous system dysfunctions, cognitive-emotional factors, work disabilities) at baseline and at six-week follow-up. Physiotherapists' acceptability of the use of the PDDM model and appreciation of the training were assessed via semi-structured phone interviews. Analyses focused on a description of the model's applicability to a LBP population, feasibility outcomes and acceptability measures. RESULTS: Applicablity of the PDDM model was confirmed since it successfully established the profile of patients according to the elements of each categories, and each of the 5 domains of the model was represented among the study sample. Trial was deemed feasible contingent upon few modifications as our predefined success criteria for the feasibility outcomes were met but feasibility issues pertaining to data collection were highlighted. Twenty-four (24) clinicians and 61 patients were recruited within the study's timeframe. Patient's attrition rate (29%) and clinicians' compliance to the study protocol were adequate. Clinicians' perceived acceptability of the use of the model in clinical settings and their appreciation of the training and online resources were both positive. Recommendations to improve the model's integration in clinical practice, content of the workshop and feasibility of data collection methods were identified for future studies. A positive effect for all patients' reported outcome measures were also observed. All outcome measures except for the PainDetect questionnaire showed a statistically significant reduction post-intervention (p<0.05). CONCLUSION: These findings provide preliminary evidence of the potential of the PDDM model to optimize LBP management as well as conducting a future larger-scale pragmatic trial to determine its effectiveness. TRIAL REGISTRATION: Clinicaltrial.gov: NCT03949179.

Driving the Musculoskeletal Diagnosis Train on the High-Value Track.

Clinicians who provide high-value musculoskeletal care offer evidence-based management that can improve pain, function, and quality of life. However, the current approach to improving outcomes emphasizes treatment strategies at the expense of accurate diagnosis. Guidelines rarely provide quality information on differential diagnosis and prognosis. The disconnect is worrying, because accurate diagnosis can lead to more appropriate care and improved patient outcomes. In this Viewpoint, we reflect on how the clinician can refine musculoskeletal diagnoses to provide high-value care. We (1) argue that the link between musculoskeletal diagnosis and patient outcomes must be strengthened, (2) introduce a diagnostic framework to help clinicians go beyond "special tests," and (3) present new methods for researchers to move forward from diagnostic accuracy studies. J Orthop Sports Phys Ther 2020;50(3):118-120. doi:10.2519/jospt.2020.0603.

Operationalization of the new Pain and Disability Drivers Management model: A modified Delphi survey of multidisciplinary pain management experts.

BACKGROUND: We recently proposed the Pain and Disability Drivers Management (PDDM) model, which was designed to outline comprehensive factors driving pain and disability in low back pain (LBP). Although we have hypothesized and proposed 41 elements, which make up the model's five domains, we have yet to assess the external validity of the PDDM's elements by expert consensus. RESEARCH OBJECTIVES: This study aimed to reach consensus among experts regarding the different elements that should be included in each domain of the PDDM model. RELEVANCE: The PDDM may assist clinicians and researchers in the delivery of targeted care and ultimately enhance treatment outcomes in LBP. METHODS: Using a modified Delphi survey, a two-round online questionnaire was administered to a group of experts in musculoskeletal pain management. Participants were asked to rate the relevance of each element proposed within the model. Participants were also invited to add and rate new elements. Consensus was defined by a greater than or equal to 75% level of agreement. RESULTS: A total of 47 (round 1) and 33 (round 2) participants completed the survey. Following the first round, 38 of 41 of the former model elements reached consensus, and 10 new elements were proposed and later rated in the second round. Following this second round, consensus was reached for all elements (10 new + 3 from first round), generating a final model composed of 51 elements. CONCLUSION: This expert consensus-derived list of clinical elements related to the management of LBP represents a first step in the validation of the PDDM model.