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Feller D, Wingbermuhle R, Berg B, Vigdal ØN, Innocenti T, Grotle M, Ostelo R, Chiarotto A. Improvements Are Needed in the Adherence to the TRIPOD Statement for Clinical Prediction Models for Patients With Spinal Pain or Osteoarthritis: A Metaresearch Study. THE JOURNAL OF PAIN 2024; 25:104624. [PMID: 39002741 DOI: 10.1016/j.jpain.2024.104624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 06/26/2024] [Accepted: 07/01/2024] [Indexed: 07/15/2024]
Abstract
This metaresearch study aimed to evaluate the completeness of reporting of prediction model studies in patients with spinal pain or osteoarthritis (OA) in terms of adherence to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. We searched for prognostic and diagnostic prediction models in patients with spinal pain or OA in MEDLINE, Embase, Web of Science, and CINAHL. Using a standardized assessment form, we assessed the adherence to the TRIPOD of the included studies. Two independent reviewers performed the study selection and data extraction phases. We included 66 studies. Approximately 35% of the studies declared to have used the TRIPOD. The median adherence to the TRIPOD was 59% overall (interquartile range (IQR): 21.8), with the items of the methods and results sections having the worst reporting. Studies on neck pain had better adherence to the TRIPOD than studies on back pain and OA (medians of 76.5%, 59%, and 53%, respectively). External validation studies had the highest total adherence (median: 79.5%, IQR: 12.8) of all the study types. The median overall adherence was 4 points higher in studies that declared TRIPOD use than those that did not. Finally, we did not observe any improvement in adherence over the years. The adherence to the TRIPOD of prediction models in the spinal and OA fields is low, with the methods and results sections being the most poorly reported. Future studies on prediction models in spinal pain and OA should follow the TRIPOD to improve their reporting completeness. PERSPECTIVE: This article provides data about adherence to the TRIPOD statement in 66 prediction model studies for spinal pain or OA. The adherence to the TRIPOD statement was found to be low (median adherence of 59%). This inadequate reporting may negatively impact the effective use of the models in clinical practice.
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Affiliation(s)
- Daniel Feller
- Department of Rehabilitation, Provincial Agency for Health of the Autonomous Province of Trento, Trento, Italy; Department of Human Resources, Provincial Agency for Health of the Autonomous Province of Trento, Trento, Italy; Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.
| | - Roel Wingbermuhle
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; Department of Physiotherapy and Rehabilitation sciences, SOMT University of Physiotherapy, Amersfoort, the Netherlands
| | - Bjørnar Berg
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | - Ørjan Nesse Vigdal
- Department of Rehabilitation Science and Health Technology, Faculty of Health Science, OsloMet - Oslo Metropolitan University, Oslo, Norway
| | - Tiziano Innocenti
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, the Netherlands; GIMBE Foundation, Bologna, Italy
| | - Margreth Grotle
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway; Division of Clinical Neuroscience, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Raymond Ostelo
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, the Netherlands; Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit & Amsterdam Movement Sciences, Musculoskeletal Health, Amsterdam, the Netherlands
| | - Alessandro Chiarotto
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
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Mišić M, Lee N, Zidda F, Sohn K, Usai K, Löffler M, Uddin MN, Farooqi A, Schifitto G, Zhang Z, Nees F, Geha P, Flor H. Brain white matter pathways of resilience to chronic back pain: a multisite validation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.30.578024. [PMID: 38352359 PMCID: PMC10862888 DOI: 10.1101/2024.01.30.578024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Chronic back pain (CBP) is a global health concern with significant societal and economic burden. While various predictors of back pain chronicity have been proposed, including demographic and psychosocial factors, neuroimaging studies have pointed to brain characteristics as predictors of CBP. However, large-scale, multisite validation of these predictors is currently lacking. In two independent longitudinal studies, we examined white matter diffusion imaging data and pain characteristics in patients with subacute back pain (SBP) over six- and 12-month periods. Diffusion data from individuals with CBP and healthy controls (HC) were analyzed for comparison. Whole-brain tract-based spatial statistics analyses revealed that a cluster in the right superior longitudinal fasciculus (SLF) tract had larger fractional anisotropy (FA) values in patients who recovered (SBPr) compared to those with persistent pain (SBPp), and predicted changes in pain severity. The SLF FA values accurately classified patients at baseline and follow-up in a third publicly available dataset (Area under the Receiver Operating Curve ~ 0.70). Notably, patients who recovered had FA values larger than those of HC suggesting a potential role of SLF integrity in resilience to CBP. Structural connectivity-based models also classified SBPp and SBPr patients from the three data sets (validation accuracy 67%). Our results validate the right SLF as a robust predictor of CBP development, with potential for clinical translation. Cognitive and behavioral processes dependent on the right SLF, such as proprioception and visuospatial attention, should be analyzed in subacute stages as they could prove important for back pain chronicity.
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Affiliation(s)
- Mina Mišić
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Noah Lee
- Department of Psychiatry, University of Rochester Medical Center, 14642 Rochester, NY, USA
| | - Francesca Zidda
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Kyungjin Sohn
- Department of Statistics and Operations Research, University of North Carolina, 27599 Chapel Hill, NC, USA
| | - Katrin Usai
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Martin Löffler
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
- Department of Experimental Psychology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Md Nasir Uddin
- Department of Neurology, University of Rochester Medical Center, 14642 Rochester, NY, USA
| | - Arsalan Farooqi
- Department of Psychiatry, University of Rochester Medical Center, 14642 Rochester, NY, USA
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester Medical Center, 14642 Rochester, NY, USA
| | - Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina, 27599 Chapel Hill, NC, USA
| | - Frauke Nees
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, 24105 Kiel, Germany
| | - Paul Geha
- Department of Psychiatry, University of Rochester Medical Center, 14642 Rochester, NY, USA
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
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Chamoro M, Heymans MW, Oei EH, Bierma-Zeinstra SM, Koes BW, Chiarotto A. Diagnostic models to predict structural spinal osteoarthritis on lumbar radiographs in older adults with back pain: Development and internal validation. OSTEOARTHRITIS AND CARTILAGE OPEN 2024; 6:100506. [PMID: 39183945 PMCID: PMC11342188 DOI: 10.1016/j.ocarto.2024.100506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 07/21/2024] [Indexed: 08/27/2024] Open
Abstract
Objective It is difficult for health care providers to diagnose structural spinal osteoarthritis (OA), because current guidelines recommend against imaging in patients with back pain. Therefore, the aim of this study was to develop and internally validate multivariable diagnostic prediction models based on a set of clinical and demographic features to be used for the diagnosis of structural spinal OA on lumbar radiographs in older patients with back pain. Design Three diagnostic prediction models, for structural spinal OA on lumbar radiographs (i.e. multilevel osteophytes, multilevel disc space narrowing (DSN), and both combined), were developed and internally validated in the 'Back Complaints in Older Adults' (BACE) cohort (N = 669). Model performance (i.e. overall performance, discrimination and calibration) and clinical utility (i.e. decision curve analysis) were assessed. Internal validation was performed by bootstrapping. Results Mean age of the cohort was 66.9 years (±7.6 years) and 59% were female. All three models included age, gender, back pain duration and duration of spinal morning stiffness as predictors. The combined model additionally included restricted lateral flexion and spinal morning stiffness severity, and exhibited the best model performance (optimism adjusted c-statistic 0.661; good calibration with intercept -0.030 and slope of 0.886) and acceptable clinical utility. The other models showed suboptimal discrimination, good calibration and acceptable decision curves. Conclusion All three models for structural spinal OA displayed lesuboptimal discrimination and need improvement. However, these internally validated models have potential to inform primary care clinicians about a patient with risk of having structural spinal OA on lumbar radiographs. External validation before implementation in clinical care is recommended.
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Affiliation(s)
- Mirna Chamoro
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Martijn W. Heymans
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Edwin H.G. Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Sita M.A. Bierma-Zeinstra
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Orthopedics and Sports Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Bart W. Koes
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Research Unit of General Practice, Department of Public Health & Center for Muscle and Joint Health, University of Southern Denmark, Odense, Denmark
| | - Alessandro Chiarotto
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
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Croft P, Hill JC, Foster NE, Dunn KM, van der Windt DA. Stratified health care for low back pain using the STarT Back approach: Holy Grail or doomed to fail? Pain 2024:00006396-990000000-00658. [PMID: 39037849 DOI: 10.1097/j.pain.0000000000003319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 05/23/2024] [Indexed: 07/24/2024]
Abstract
ABSTRACT There have been at least 7 separate randomised controlled trials published between 2011 and 2023 that have examined primary care for nonspecific low back pain informed by the STarT Back approach to stratified care based on risk prediction, compared with care not informed by this approach. The results, across 4 countries, have been contrasting-some demonstrating effectiveness and/or efficiency of this approach, others finding no benefits over comparison interventions. This review considers possible explanations for the differences, particularly whether this is related to poor predictive performance of the STarT Back risk-prediction tool or to variable degrees of success in implementing the whole STarT Back approach (subgrouping and matching treatments to predicted risk of poor outcomes) in different healthcare systems. The review concludes that although there is room for improving and expanding the predictive value of the STarT Back tool, its performance in allocating individuals to their appropriate risk categories cannot alone explain the variation in results of the trials to date. Rather, the learning thus far suggests that challenges in implementing stratified care in clinical practice and in changing professional practice largely explain the contrasting trial results. The review makes recommendations for future research, including greater focus on studying facilitators of implementation of stratified care and developing better treatments for patients with nonspecific low back pain at high risk of poor outcomes.
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Affiliation(s)
- Peter Croft
- School of Medicine, Primary Care Centre Versus Arthritis, Keele University, Keele, United Kingdom
| | - Jonathan C Hill
- School of Medicine, Primary Care Centre Versus Arthritis, Keele University, Keele, United Kingdom
| | - Nadine E Foster
- School of Medicine, Primary Care Centre Versus Arthritis, Keele University, Keele, United Kingdom
- STARS Education and Research Alliance, Surgical Treatment and Rehabilitation Service (STARS), The University of Queensland and Metro North Health, Brisbane, Queensland, Australia
| | - Kate M Dunn
- School of Medicine, Primary Care Centre Versus Arthritis, Keele University, Keele, United Kingdom
| | - Danielle A van der Windt
- School of Medicine, Primary Care Centre Versus Arthritis, Keele University, Keele, United Kingdom
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Elabd OM, Oakley PA, Elabd AM. Prediction of Back Disability Using Clinical, Functional, and Biomechanical Variables in Adults with Chronic Nonspecific Low Back Pain. J Clin Med 2024; 13:3980. [PMID: 38999544 PMCID: PMC11242843 DOI: 10.3390/jcm13133980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 06/28/2024] [Accepted: 07/05/2024] [Indexed: 07/14/2024] Open
Abstract
Background: Researchers are focusing on understanding the etiology and predisposing factors of chronic nonspecific low back pain (CNSLBP), a costly prevalent and disabling disorder. Related clinical, functional, and biomechanical variables are often studied, but in isolation. We aimed to identify key factors for managing CNSLBP by examining the relationship between back disability and related clinical, functional, and biomechanical variables and developed prediction models to estimate disability using various variables. Methods: We performed a cross-sectional correlational study on 100 recruited patients with CNSLBP. Clinical variables of pain intensity (visual analog score), back extensor endurance (Sorenson test), functional variables of the back performance scale, 6 min walk test, and the biomechanical variable C7-S1 sagittal vertical axis were analyzed to predict disability (Oswestry disability index). Results: All variables independently, as well as in multi-correlation, were significantly correlated to disability (p < 0.05). The bivariate regression models were significant between back disability and pain intensity (Y = 11.24 + 2.189x), Sorensen results (Y = 105.48 - 0.911x), the back performance scale (Y = 6.65 + 2.486x), 6 min walk test (Y = 49.20 - 0.060x), and sagittal vertical axis (Y = 0.72 + 4.23x). The multi-regression model showed significant contributions from pain (p = 0.001) and Sorensen results (p = 0.028) in predicting back disability, whereas no significant effect was found for other variables. Conclusions: A multidisciplinary approach is essential not only for the management of but also for the assessment of chronic nonspecific low back pain, including its clinical, functional, and biomechanical characteristics. However, special emphasis should be placed on clinical characteristics, including the intensity of pain and back extensor endurance.
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Affiliation(s)
- Omar M. Elabd
- Department of Orthopedics and Its Surgeries, Faculty of Physical Therapy, Delta University for Science and Technology, Gamasa 35712, Egypt;
- Department of Physical Therapy, Aqaba University of Technology, Aqaba 771111, Jordan
| | - Paul A. Oakley
- Private Practice, Newmarket, ON L3Y 8Y8, Canada;
- Kinesiology and Health Science, York University, Toronto, ON M3J 1P3, Canada
| | - Aliaa M. Elabd
- Basic Science Department, Faculty of Physical Therapy, Benha University, Benha 13511, Egypt
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Vigdal ØN, Storheim K, Killingmo RM, Rysstad T, Pripp AH, van der Gaag W, Chiarotto A, Koes B, Grotle M. External validation and updating of prognostic prediction models for nonrecovery among older adults seeking primary care for back pain. Pain 2023; 164:2759-2768. [PMID: 37490100 DOI: 10.1097/j.pain.0000000000002974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/23/2023] [Indexed: 07/26/2023]
Abstract
ABSTRACT Prognostic prediction models for 3 different definitions of nonrecovery were developed in the Back Complaints in the Elders study in the Netherlands. The models' performance was good (optimism-adjusted area under receiver operating characteristics [AUC] curve ≥0.77, R2 ≥0.3). This study aimed to assess the external validity of the 3 prognostic prediction models in the Norwegian Back Complaints in the Elders study. We conducted a prospective cohort study, including 452 patients aged ≥55 years, seeking primary care for a new episode of back pain. Nonrecovery was defined for 2 outcomes, combining 6- and 12-month follow-up data: Persistent back pain (≥3/10 on numeric rating scale) and persistent disability (≥4/24 on Roland-Morris Disability Questionnaire). We could not assess the third model (self-reported nonrecovery) because of substantial missing data (>50%). The models consisted of biopsychosocial prognostic factors. First, we assessed Nagelkerke R2 , discrimination (AUC) and calibration (calibration-in-the-large [CITL], slope, and calibration plot). Step 2 was to recalibrate the models based on CITL and slope. Step 3 was to reestimate the model coefficients and assess if this improved performance. The back pain model demonstrated acceptable discrimination (AUC 0.74, 95% confidence interval: 0.69-0.79), and R2 was 0.23. The disability model demonstrated excellent discrimination (AUC 0.81, 95% confidence interval: 0.76-0.85), and R2 was 0.35. Both models had poor calibration (CITL <0, slope <1). Recalibration yielded acceptable calibration for both models, according to the calibration plots. Step 3 did not improve performance substantially. The recalibrated models may need further external validation, and the models' clinical impact should be assessed.
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Affiliation(s)
- Ørjan Nesse Vigdal
- Department of Rehabilitation Science and Health Technology, Faculty of Health Science, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - Kjersti Storheim
- Department of Rehabilitation Science and Health Technology, Faculty of Health Science, OsloMet-Oslo Metropolitan University, Oslo, Norway
- Research and Communication Unit for Musculoskeletal Health (FORMI), Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Rikke Munk Killingmo
- Department of Rehabilitation Science and Health Technology, Faculty of Health Science, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - Tarjei Rysstad
- Department of Rehabilitation Science and Health Technology, Faculty of Health Science, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - Are Hugo Pripp
- Department of Rehabilitation Science and Health Technology, Faculty of Health Science, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - Wendelien van der Gaag
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Alessandro Chiarotto
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Bart Koes
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Center for Muscle and Health, University of Southern Denmark, Odense, Denmark
| | - Margreth Grotle
- Department of Rehabilitation Science and Health Technology, Faculty of Health Science, OsloMet-Oslo Metropolitan University, Oslo, Norway
- Research and Communication Unit for Musculoskeletal Health (FORMI), Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
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Kamper SJ. Systematic Reviews 1 - Gathering the Evidence: Linking Evidence to Practice. J Orthop Sports Phys Ther 2023; 53:490–491. [PMID: 37470360 DOI: 10.2519/jospt.2023.0701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Affiliation(s)
- Steven J Kamper
- School of Health Sciences, University of Sydney, Camperdown, Australia
- Nepean Blue Mountains Local Health District, Penrith, Australia
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Naye F, Décary S, Houle C, LeBlanc A, Cook C, Dugas M, Skidmore B, Tousignant-Laflamme Y. Six Externally Validated Prognostic Models Have Potential Clinical Value to Predict Patient Health Outcomes in the Rehabilitation of Musculoskeletal Conditions: A Systematic Review. Phys Ther 2023; 103:7066982. [PMID: 37245218 DOI: 10.1093/ptj/pzad021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/21/2022] [Accepted: 01/06/2023] [Indexed: 05/30/2023]
Abstract
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.
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Affiliation(s)
- Florian Naye
- School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Clinical Research of the Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, Quebec, Canada
| | - Simon Décary
- Department of Family Medicine and Emergency Medicine, Pavillon Ferdinand-Vandry, Université Laval, Quebec, Quebec, Canada
- Tier 1 Canada Research Chair in Shared Decision Making and Knowledge Translation, Centre de recherche sur les soins et les services de première ligne de l'Université Laval (CERSSPL-UL), Quebec, Quebec, Canada
| | - Catherine Houle
- School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Clinical Research of the Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, Quebec, Canada
| | - Annie LeBlanc
- Department of Family Medicine and Emergency Medicine, Pavillon Ferdinand-Vandry, Université Laval, Quebec, Quebec, Canada
| | - Chad Cook
- Physical Therapy Division, Duke University, Durham, North Carolina, USA
| | - Michèle Dugas
- VITAM Research Center, Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale, Quebec, Quebec, Canada
| | - Becky Skidmore
- Independent Information Specialist, Ottawa, Ontario, Canada
| | - Yannick Tousignant-Laflamme
- School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Clinical Research of the Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, Quebec, Canada
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Pocovi NC, Kent P, Lin CWC, French SD, de Campos TF, da Silva T, Hancock MJ. Recurrence of low back pain: A difficult outcome to predict. Development and validation of a multivariable prediction model for recurrence in patients recently recovered from an episode of non-specific low back pain. Musculoskelet Sci Pract 2023; 64:102746. [PMID: 36948043 DOI: 10.1016/j.msksp.2023.102746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/16/2023] [Accepted: 03/10/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND Recurrence of low back pain (LBP) is common. If clinicians could identify an individual's risk of recurrence, this would enhance clinical decision-making and tailored patient care. OBJECTIVE/DESIGN To develop and validate a simple tool to predict the probability of a recurrence of LBP by 3- or 12-months following recovery. METHODS Data utilised for the prediction model development came from a prospective inception cohort study of participants (n = 250) recently recovered from LBP, who had sought care from chiropractic or physiotherapy services. The outcome measure was a recurrence of activity-limiting LBP. Candidate predictor variables (e.g., basic demographics, LBP history, levels of physical activity, etc) collected at baseline were considered for inclusion in a multivariable Cox model. The model's performance was tested in a separate validation dataset of participants (n = 261) involved in a randomised controlled trial investigating exercise for the prevention of LBP recurrences. RESULTS The final model included the number of previous episodes, total sitting time, and level of education. In the development sample, discrimination was acceptable (Harrell's C-statistic = 0.61, 95% CI, 0.59-0.62), but in the validation sample, discrimination was poor (0.56, 95% CI, 0.54-0.58). Calibration of the model in the validation dataset was acceptable at 3 months but was less precise at 12 months. CONCLUSION The developed prediction model, which included number of previous episodes, total sitting time, and level of education, did not perform adequately in the validation sample to recommend its use in clinical practice. Predicting recurrence of LBP in clinical practice remains challenging.
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Affiliation(s)
- N C Pocovi
- Department of Health Sciences, Macquarie University, Sydney, Australia.
| | - P Kent
- School of Allied Health, Curtin University, Perth, Australia
| | - C-W C Lin
- Institute for Musculoskeletal Health, The University of Sydney, Sydney, Australia
| | - S D French
- Department of Chiropractic, Macquarie University, Sydney, Australia
| | - T F de Campos
- Department of Health Sciences, Macquarie University, Sydney, Australia; St Vincent's Private Allied Health Services, St Vincent's Private Hospital, Sydney, Australia
| | - T da Silva
- Masters and Doctoral Programs in Physiotherapy, Universidade Cidade de São Paulo, São Paulo, Brazil
| | - M J Hancock
- Department of Health Sciences, Macquarie University, Sydney, Australia
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