1
|
Runhaar J, Özbulut Ö, Kloppenburg M, Boers M, Bijlsma JWJ, Bierma-Zeinstra S. Two-year clinical follow-up enhances the diagnosis of early-stage hip osteoarthritis: data from check cohort. RMD Open 2024; 10:e004208. [PMID: 38862243 PMCID: PMC11168179 DOI: 10.1136/rmdopen-2024-004208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 05/16/2024] [Indexed: 06/13/2024] Open
Abstract
OBJECTIVE To provide a set of diagnostic criteria for early-stage hip osteoarthritis (OA) in primary care, using signs and symptoms monitored over 2 years in individuals with hip pain and/or stiffness. Additionally, the study aimed to see whether these factors were additive to factors based on baseline signs and symptoms only. METHODS Data of the 543 persons with 735 symptomatic hips were collected from the prospective Cohort Hip and Cohort Knee cohort study. Using data from 5 to 10 years of follow-up, 24 experts (13 general practitioners, 11 secondary care physicians (6 rheumatologists and 5 orthopaedic surgeons)) inspected individuals' medical data on the presence of clinically relevant hip OA. Their diagnoses are used as reference standards. Backward selection method was used to provide models using the factors from baseline to 2 years of follow-up. Additionally, new models were combined with previously published models, using same selection method. Area under the curve (AUC) was calculated after each removal of factors in the final combined models. RESULTS Radiographic factors and high-sensitive C reactive protein did not end up in any model with change factors only. AUC value (SD) of the final obtained model of change factors was 0.70 (0.01). Adding newly defined factors to previously published models significantly (p<0.0001) increased the AUC value to 0.75 (0.01). CONCLUSION Final diagnostic criteria, consisting only of the factors obtained through history taking and physical examination, were able to detect early-stage hip OA associated with clinically relevant hip OA 5-10 years later, with 'moderate' precision.
Collapse
Affiliation(s)
- Jos Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Ömer Özbulut
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | | | - Maarten Boers
- Department of Epidemiology & Data Science, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | - Johannes W J Bijlsma
- Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Sita Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Orthopaedics & Sports Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| |
Collapse
|
2
|
van Buuren MMA, Riedstra NS, van den Berg MA, Boel FDEM, Ahedi H, Arbabi V, Arden NK, Bierma-Zeinstra SMA, Boer CG, Cicuttini F, Cootes TF, Crossley K, Felson D, Gielis WP, Heerey J, Jones G, Kluzek S, Lane NE, Lindner C, Lynch JA, Van Meurs J, Mosler AB, Nelson AE, Nevitt M, Oei E, Runhaar J, Tang J, Weinans H, Agricola R. Cohort profile: Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) - an international consortium of prospective cohort studies with individual participant data on hip osteoarthritis. BMJ Open 2024; 14:e077907. [PMID: 38637130 PMCID: PMC11029301 DOI: 10.1136/bmjopen-2023-077907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 02/20/2024] [Indexed: 04/20/2024] Open
Abstract
PURPOSE Hip osteoarthritis (OA) is a major cause of pain and disability worldwide. Lack of effective therapies may reflect poor knowledge on its aetiology and risk factors, and result in the management of end-stage hip OA with costly joint replacement. The Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) consortium was established to pool and harmonise individual participant data from prospective cohort studies. The consortium aims to better understand determinants and risk factors for the development and progression of hip OA, to optimise and automate methods for (imaging) analysis, and to develop a personalised prediction model for hip OA. PARTICIPANTS World COACH aimed to include participants of prospective cohort studies with ≥200 participants, that have hip imaging data available from at least 2 time points at least 4 years apart. All individual participant data, including clinical data, imaging (data), biochemical markers, questionnaires and genetic data, were collected and pooled into a single, individual-level database. FINDINGS TO DATE World COACH currently consists of 9 cohorts, with 38 021 participants aged 18-80 years at baseline. Overall, 71% of the participants were women and mean baseline age was 65.3±8.6 years. Over 34 000 participants had baseline pelvic radiographs available, and over 22 000 had an additional pelvic radiograph after 8-12 years of follow-up. Even longer radiographic follow-up (15-25 years) is available for over 6000 of these participants. FUTURE PLANS The World COACH consortium offers unique opportunities for studies on the relationship between determinants/risk factors and the development or progression of hip OA, by using harmonised data on clinical findings, imaging, biomarkers, genetics and lifestyle. This provides a unique opportunity to develop a personalised hip OA risk prediction model and to optimise methods for imaging analysis of the hip.
Collapse
Affiliation(s)
- Michiel M A van Buuren
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Noortje S Riedstra
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Myrthe A van den Berg
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Fleur D E M Boel
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Harbeer Ahedi
- Institute for Medical Research, University of Tasmania Menzies, Hobart, Tasmania, Australia
| | - Vahid Arbabi
- Department of Orthopedics, UMC Utrecht, Utrecht, Netherlands
- Orthopaedic-Biomechanics Research Group, Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Nigel K Arden
- Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford Nuffield, Oxford, Oxfordshire, UK
| | | | - Cindy G Boer
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Flavia Cicuttini
- Department of Epidemiology and Preventative Medicine, Monash University, Melbourne, Victoria, Australia
| | - Timothy F Cootes
- Centre for Imaging Sciences, The University of Manchester, Manchester, UK
| | - Kay Crossley
- La Trobe Sport and Exercise Medicine Research Centre, La Trobe University School of Allied Health Human Services and Sport, Melbourne, Victoria, Australia
| | - David Felson
- Boston University School of Medicine, Boston, Massachusetts, USA
| | - Willem Paul Gielis
- Department of Orthopedics, UMC Utrecht, Utrecht, Netherlands
- Department of Radiology, UMC Utrecht, Utrecht, Netherlands
| | - Joshua Heerey
- La Trobe Sport and Exercise Medicine Research Centre, La Trobe University School of Allied Health Human Services and Sport, Melbourne, Victoria, Australia
| | - Graeme Jones
- Institute for Medical Research, University of Tasmania Menzies, Hobart, Tasmania, Australia
| | - Stefan Kluzek
- Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford Nuffield, Oxford, Oxfordshire, UK
| | - Nancy E Lane
- Department of Medicine, University of California Davis School of Medicine, Sacramento, California, USA
| | - Claudia Lindner
- Centre for Imaging Sciences, The University of Manchester, Manchester, UK
| | - John A Lynch
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - J Van Meurs
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Andrea B Mosler
- La Trobe Sport and Exercise Medicine Research Centre, La Trobe University School of Allied Health Human Services and Sport, Melbourne, Victoria, Australia
| | - Amanda E Nelson
- Thurston Arthritis Research Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - M Nevitt
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Edwin Oei
- Department of Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Jos Runhaar
- Department of General Practice, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Jinchi Tang
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| | - Harrie Weinans
- Department of Orthopedics, UMC Utrecht, Utrecht, Netherlands
- Department of Biomechanical Engineering, TU Delft, Delft, Zuid-Holland, Netherlands
| | - Rintje Agricola
- Department of Orthopaedics and Sports Medicine, Erasmus Medical Center, Rotterdam, Zuid-Holland, Netherlands
| |
Collapse
|
3
|
Wang Q, Runhaar J, Kloppenburg M, Boers M, Bijlsma JWJ, Bierma-Zeinstra SMA. Evaluation of the Diagnostic Performance of American College of Rheumatology, EULAR, and National Institute for Health and Clinical Excellence Criteria Against Clinically Relevant Knee Osteoarthritis: Data From the CHECK Cohort. Arthritis Care Res (Hoboken) 2024; 76:511-516. [PMID: 37933434 DOI: 10.1002/acr.25270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 10/17/2023] [Accepted: 11/02/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE Our objective was to evaluate the diagnostic performance of the EULAR, American College of Rheumatology (ACR), and National Institute for Health and Care Excellence (NICE) criteria by using clinical experts' diagnosis of clinically relevant knee osteoarthritis (OA) as the outcome of interest. METHODS In a previous study, we recruited clinical experts to evaluate longitudinal (5-, 8-, and 10-year follow-up) clinical and radiographic data of symptomatic knees from the Cohort Hip and Cohort Knee (CHECK) study for the presence or absence of clinically relevant OA. In the current study, ACR, EULAR, and NICE criteria were applied to the same 5-, 8-, and 10-year follow-up data; then a knee was diagnosed with OA if fulfilling the criteria at one of the three time points (F1), two of the time points (F2), or at all three time points (F3). Using clinically relevant OA as the reference standard, the sensitivity, specificity, and positive and negative predictive values for the three criteria were assessed. RESULTS A total of 539 participants for a total of 833 examined knees were included. Thirty-six percent of knees were diagnosed with clinically relevant OA by experts. Sixty-seven percent to 74% of the knees received the same diagnosis (OA or non-OA) by the three criteria sets for the different definitions (F1 to F3). EULAR consistently (F1 through F3) had the highest specificity, and NICE consistently had the highest sensitivity. CONCLUSION The diagnoses only moderately overlapped among the three criteria sets. The EULAR criteria seemed to be more suitable for study enrollment (when aimed at recruiting clinically relevant OA knees), given the highest specificities. The NICE criteria, given the highest sensitivities, could be more useful for an initial diagnosis in clinical practice.
Collapse
Affiliation(s)
- Qiuke Wang
- Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands, and Shanghai Sixth People's Hospital, Shanghai, China
| | - Jos Runhaar
- Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands
| | | | - Maarten Boers
- Amsterdam University Medical Center, Amsterdam, The Netherlands
| | | | | |
Collapse
|
4
|
Salis Z, Sainsbury A. Association of long-term use of non-steroidal anti-inflammatory drugs with knee osteoarthritis: a prospective multi-cohort study over 4-to-5 years. Sci Rep 2024; 14:6593. [PMID: 38504099 PMCID: PMC10950850 DOI: 10.1038/s41598-024-56665-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/08/2024] [Indexed: 03/21/2024] Open
Abstract
This study examines the long-term impact of non-steroidal anti-inflammatory drugs (NSAIDs) on the progression of symptoms and structural deterioration of the joint in knee osteoarthritis. The study analyzes data from 4197 participants (8394 knees) across the Osteoarthritis Initiative (OAI), Multicenter Osteoarthritis Study (MOST), and Cohort Hip and Cohort Knee (CHECK) over 4-to-5 years. Adjustments were made for major covariates. We focussed on binary outcomes to assess the presence or absence of significant changes. We found that, relative to non-users, individuals using NSAIDs long-term were significantly more likely to experience aggravated symptoms exceeding the minimally clinically important difference, specifically, pain (OR: 2.04, 95% CI: 1.66-2.49), disability (OR: 2.21, 95% CI: 1.74-2.80), and stiffness (OR: 1.58, 95% CI: 1.29-1.93). Long-term users also faced a higher probability than non-users of having total knee replacement (OR: 3.13, 95% CI: 2.08-4.70), although no significant difference between long-term users and non-users was observed for structural deterioration in the knee joint (OR: 1.25, 95% CI: 0.94-1.65). While acknowledging the limitations of this study due to its observational design and the potential for bidirectional causality, these findings suggest that long-term NSAID use could accelerate the progression to total knee replacement by markedly exacerbating symptoms.
Collapse
Affiliation(s)
- Zubeyir Salis
- Division of Rheumatology, Geneva University Hospital and Faculty of Medicine, University of Geneva, HUG Av. de Beau-Séjour 26, 1206, Geneva, Switzerland.
- Centre for Big Data Research in Health, The University of New South Wales, Kensington, NSW, Australia.
- School of Human Sciences, The University of Western Australia, Perth, WA, Australia.
| | - Amanda Sainsbury
- School of Human Sciences, The University of Western Australia, Perth, WA, Australia
| |
Collapse
|
5
|
Salis Z. Investigation of the association of long-term NSAID use with radiographic hip osteoarthritis over four to five years: Data from the OAI and CHECK studies. OSTEOARTHRITIS AND CARTILAGE OPEN 2024; 6:100427. [PMID: 38187099 PMCID: PMC10770760 DOI: 10.1016/j.ocarto.2023.100427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/04/2023] [Accepted: 12/06/2023] [Indexed: 01/09/2024] Open
Abstract
Objective To examine the relationship between long-term use of non-steroidal anti-inflammatory drugs (NSAIDs) and the incidence and progression of radiographic hip osteoarthritis (RHOA), as well as the degeneration of individual radiographic features. Methods We analyzed data from the Osteoarthritis Initiative (OAI) and the Cohort Hip and Cohort Knee (CHECK) study. Our exposure was the number of years of NSAID use over a 4-to-5-year follow-up period. Our outcomes were the incidence and progression of RHOA over a 4-to-5-year follow-up as assessed using a modified Croft grade in OAI and the Kellgren-Lawrence (K/L) grade in CHECK. The incidence of RHOA was defined as having RHOA (grade ≥2) at follow-up and investigated in "incidence cohorts" of hips without RHOA at baseline (grade <2). The progression of RHOA was defined as an increase of ≥1 grade at follow-up from baseline and investigated in "progression cohorts" of hips with RHOA at baseline (grade ≥2). Additionally, we assessed the degeneration of nine specific radiographic features, such as joint space narrowing and osteophytes, defined by a grade increase of ≥1 at follow-up from baseline, in all cohorts. Results In the incidence cohorts, there were 5153 hips in OAI and 1011 in CHECK; in the progression cohorts, there were 285 and 106 hips, respectively. There was no association between NSAID use and the outcomes investigated. Conclusion Over 4-to-5 years, long-term NSAID use showed no association with the incidence or progression of RHOA, or with the degeneration of individual radiographic features.
Collapse
Affiliation(s)
- Zubeyir Salis
- Division of Rheumatology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- The University of Western Australia, School of Human Sciences, Perth, WA, Australia
- The University of New South Wales, Centre for Big Data Research in Health, Kensington, NSW, Australia
| |
Collapse
|
6
|
Salis Z. Investigation of the Associations of Smoking With Hip Osteoarthritis: A Baseline Cross-Sectional and Four- to Five-Year Longitudinal Multicohort Study. ACR Open Rheumatol 2024; 6:155-166. [PMID: 38174808 PMCID: PMC10933634 DOI: 10.1002/acr2.11644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 10/05/2023] [Accepted: 11/21/2023] [Indexed: 01/05/2024] Open
Abstract
OBJECTIVE This study aimed to investigate the effect of smoking on the prevalence, incidence, and progression of hip osteoarthritis (OA). We used data from the Osteoarthritis Initiative (OAI) and the Cohort Hip and Cohort Knee (CHECK) studies. METHODS We analyzed 9,386 hips from 4,716 participants (OAI cohort) and 1,954 hips from 977 participants (CHECK cohort). The primary exposure was smoking status at baseline, categorized as current, former, or never smoker. Outcomes of radiographic hip OA (RHOA) and symptomatic hip OA were evaluated both cross-sectionally at baseline and longitudinally over a 4- to 5-year follow-up, with adjustments for major covariates. RESULTS No significant differences were observed between current or former smokers and never smokers for any of the outcomes examined, either at baseline or at the 4- to 5-year follow-up. In the cross-sectional analysis, the odds ratios with 95% confidence intervals for the prevalence of RHOA for current and former smokers were 1.29 (0.68-2.46) and 0.99 (0.70-1.40) in the OAI cohort and 1.38 (0.78-2.44) and 0.85 (0.54-1.32) in the CHECK cohort, respectively. In the longitudinal analysis, odds ratio with 95% confidence intervals for the incidence of RHOA were 1.03 (0.23-4.50) and 0.92 (0.46-1.85) in the OAI cohort and 0.61 (0.34-1.11) and 1.00 (0.69-1.44) in the CHECK cohort, respectively. CONCLUSION Our study found no clear association between smoking and the prevalence, incidence, or progression of RHOA or symptomatic hip OA, either at baseline or over a 4- to 5-year period.
Collapse
Affiliation(s)
- Zubeyir Salis
- Geneva University Hospital and University of Geneva, Geneva, Switzerland, University of New South Wales, Kensington, New South Wales, Australia, and The University of Western AustraliaPerthWestern AustraliaAustralia
| |
Collapse
|
7
|
Ratna HVK, Jeyaraman M, Jeyaraman N, Nallakumarasamy A, Sharma S, Khanna M, Gupta A. Machine learning and deep neural network-based learning in osteoarthritis knee. World J Methodol 2023; 13:419-425. [PMID: 38229942 PMCID: PMC10789099 DOI: 10.5662/wjm.v13.i5.419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/14/2023] [Accepted: 09/28/2023] [Indexed: 12/20/2023] Open
Abstract
Osteoarthritis (OA) of the knee joint is considered the commonest musculoskeletal condition leading to marked disability for patients residing in various regions around the globe. Application of machine learning (ML) in doing research regarding OA has brought about various clinical advances viz, OA being diagnosed at preliminary stages, prediction of chances of development of OA among the population, discovering various phenotypes of OA, calculating the severity in OA structure and also discovering people with slow and fast progression of disease pathology, etc. Various publications are available regarding machine learning methods for the early detection of osteoarthritis. The key features are detected by morphology, molecular architecture, and electrical and mechanical functions. In addition, this particular technique was utilized to assess non-interfering, non-ionizing, and in-vivo techniques using magnetic resonance imaging. ML is being utilized in OA, chiefly with the formulation of large cohorts viz, the OA Initiative, a cohort observational study, the Multi-centre Osteoarthritis Study, an observational, prospective longitudinal study and the Cohort Hip & Cohort Knee, an observational cohort prospective study of both hip and knee OA. Though ML has various contributions and enhancing applications, it remains an imminent field with high potential, also with its limitations. Many more studies are to be carried out to find more about the link between machine learning and knee osteoarthritis, which would help in the improvement of making decisions clinically, and expedite the necessary interventions.
Collapse
Affiliation(s)
- Harish V K Ratna
- Department of Orthopaedics, Rathimed Speciality Hospital, Chennai 600040, Tamil Nadu, India
| | - Madhan Jeyaraman
- Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India
- Department of Orthopaedics, South Texas Orthopaedic Research Institute, Laredo, TX 78045, United States
| | - Naveen Jeyaraman
- Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India
| | - Arulkumar Nallakumarasamy
- Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India
| | - Shilpa Sharma
- Department of Paediatric Surgery, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Manish Khanna
- Department of Orthopaedics, Autonomous State Medical College, Ayodhya 224133, Uttar Pradesh, India
| | - Ashim Gupta
- Department of Orthopaedics, South Texas Orthopaedic Research Institute, Laredo, TX 78045, United States
- Department of Regenerative Medicine, Regenerative Orthopaedics, Noida 201301, Uttar Pradesh, India
- Department of Regenerative Medicine, Future Biologics, Lawrenceville, GA 30043, United States
- Department of Regenerative Medicine, BioIntegarte, Lawrenceville, GA 30043, United States
| |
Collapse
|
8
|
Widera P, Welsing PM, Danso SO, Peelen S, Kloppenburg M, Loef M, Marijnissen AC, van Helvoort EM, Blanco FJ, Magalhães J, Berenbaum F, Haugen IK, Bay-Jensen AC, Mobasheri A, Ladel C, Loughlin J, Lafeber FP, Lalande A, Larkin J, Weinans H, Bacardit J. Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study. OSTEOARTHRITIS AND CARTILAGE OPEN 2023; 5:100406. [PMID: 37649530 PMCID: PMC10463256 DOI: 10.1016/j.ocarto.2023.100406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 08/13/2023] [Indexed: 09/01/2023] Open
Abstract
Objectives To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P + S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81-0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52-0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P + S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.
Collapse
Affiliation(s)
- Paweł Widera
- School of Computing, Newcastle University, Newcastle, UK
| | - Paco M.J. Welsing
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | | | - Margreet Kloppenburg
- Department of Rheumatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Marieke Loef
- Department of Rheumatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Anne C. Marijnissen
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Eefje M. van Helvoort
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Francisco J. Blanco
- Institute of Biomedical Research, University Hospital of A Coruña, A Coruña, Spain
| | - Joana Magalhães
- Institute of Biomedical Research, University Hospital of A Coruña, A Coruña, Spain
| | | | - Ida K. Haugen
- Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, Norway
| | | | - Ali Mobasheri
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
- Department of Joint Surgery, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- World Health Organization Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Aging, Liege, Belgium
| | | | - John Loughlin
- Bioscience Institute, Newcastle University, International Centre for Life, Newcastle, UK
| | - Floris P.J.G. Lafeber
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Agnès Lalande
- Servier International Research Institute, Suresnes, France
| | - Jonathan Larkin
- Novel Human Genetics Research Unit, GlaxoSmithKline, Collegeville, United States
| | - Harrie Weinans
- Department of Orthopedics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Jaume Bacardit
- School of Computing, Newcastle University, Newcastle, UK
| |
Collapse
|
9
|
Rayegan H, Nguyen H, Weinans H, Gielis W, Ahmadi Brooghani S, Custers R, van Egmond N, Lindner C, Arbabi V. Automated Radiographic Measurements of Knee Osteoarthritis. Cartilage 2023; 14:413-423. [PMID: 37265053 PMCID: PMC10807738 DOI: 10.1177/19476035231166126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 12/27/2022] [Accepted: 03/12/2023] [Indexed: 06/03/2023] Open
Abstract
OBJECTIVE Herewith, we report the development of Orthopedic Digital Image Analysis (ODIA) software that is developed to obtain quantitative measurements of knee osteoarthritis (OA) radiographs automatically. Manual segmentation and measurement of OA parameters currently hamper large-cohort analyses, and therefore, automated and reproducible methods are a valuable addition in OA research. This study aims to test the automated ODIA measurements and compare them with available manual Knee Imaging Digital Analysis (KIDA) measurements as comparison. DESIGN This study included data from the CHECK (Cohort Hip and Cohort Knee) initiative, a prospective multicentre cohort study in the Netherlands with 1,002 participants. Knee radiographs obtained at baseline of the CHECK cohort were included and mean medial/lateral joint space width (JSW), minimal JSW, joint line convergence angle (JLCA), eminence heights, and subchondral bone intensities were compared between ODIA and KIDA. RESULTS Of the potential 2,004 radiographs, 1,743 were included for analyses. Poor intraclass correlation coefficients (ICCs) were reported for the JLCA (0.422) and minimal JSW (0.299). The mean medial and lateral JSW, eminence height, and subchondral bone intensities reported a moderate to good ICC (0.7 or higher). Discrepancies in JLCA and minimal JSW between the 2 methods were mostly a problem in the lateral tibia plateau. CONCLUSIONS The current ODIA tool provides important measurements of OA parameters in an automated manner from standard radiographs of the knee. Given the automated and computerized methodology that has very high reproducibility, ODIA is suitable for large epidemiological cohorts with various follow-up time points to investigate structural progression, such as CHECK or the Osteoarthritis Initiative (OAI).
Collapse
Affiliation(s)
- H. Rayegan
- Orthopaedic-BioMechanics Research Group, University of Birjand, Birjand, Iran
- Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - H.C. Nguyen
- Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
- 3D Lab, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - H. Weinans
- Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Delft University of Technology, Delft, The Netherlands
| | - W.P. Gielis
- Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - S.Y. Ahmadi Brooghani
- Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - R.J.H. Custers
- Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - N. van Egmond
- Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C. Lindner
- Division of Informatics, Imaging & Data Sciences, The University of Manchester, Manchester, UK
| | - V. Arbabi
- Orthopaedic-BioMechanics Research Group, University of Birjand, Birjand, Iran
- Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
- Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
10
|
Tang J, van Buuren MMA, Riedstra NS, Boel F, Runhaar J, Bierma-Zeinstra S, Agricola R. Cam morphology is strongly and consistently associated with development of radiographic hip osteoarthritis throughout 4 follow-up visits within 10 years. Osteoarthritis Cartilage 2023; 31:1650-1656. [PMID: 37598743 DOI: 10.1016/j.joca.2023.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 08/22/2023]
Abstract
OBJECTIVE To determine the association between cam morphology and the development of radiographic hip osteoarthritis (RHOA) at four time points within 10-year follow-up. DESIGN The nationwide prospective Cohort Hip and Cohort Knee study includes 1002 participants aged 45-65 years with 2-, 5-, 8-, and 10-year follow-ups. The associations of cam morphology (alpha angle >60°) and large cam morphology (alpha angle >78°) in hips free of osteoarthritis at baseline (Kellgren & Lawrence (KL) grade <2) with the development of both incident RHOA (KL grade≥2) and end-stage RHOA (KL grade≥3) were estimated using logistic regression with generalized estimating equation at each follow-up and using Cox regression over 10 years, adjusted for age, sex, and body mass index. RESULTS Both cam morphology and large cam morphology were associated with the development of incident RHOA at all follow-ups with adjusted Odd Ratios (aORs) ranging from 2.7 (95% Confidence interval 1.8-4.1) to 2.9 (95% CI 2.0-4.4) for cam morphology and ranging from 2.5 (95% CI 1.5-4.3) to 4.2 (95% CI 2.2-8.3) for large cam morphology. For end-stage RHOA, cam morphology resulted in aORs ranging from 4.9 (95% CI 1.8-13.2) to 8.5 (95% CI 1.1-64.4), and aORs for large cam morphology ranged from 6.7 (95% CI 3.1-14.7) to 12.7 (95% CI 1.9-84.4). CONCLUSIONS Cam morphology poses the hip at 2-13 times increased odds for developing RHOA within a 10-year follow-up. The association was particularly strong for large cam morphology and end-stage RHOA, while the strength of association was consistent over time.
Collapse
Affiliation(s)
- Jinchi Tang
- Department of Orthopedics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Michiel M A van Buuren
- Department of Orthopedics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Noortje S Riedstra
- Department of Orthopedics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Fleur Boel
- Department of Orthopedics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Jos Runhaar
- Department of General Practice, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Sita Bierma-Zeinstra
- Department of Orthopedics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of General Practice, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Rintje Agricola
- Department of Orthopedics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
| |
Collapse
|
11
|
van Erp JH, Gielis WP, Arbabi V, de Gast A, Weinans H, Kaas L, Castelein RM, Schlösser TP. Unravelling the hip-spine dilemma from the CHECK-cohort: is sagittal pelvic morphology linked to radiographic signs of femoroacetabular impingement? Hip Int 2023; 33:1079-1085. [PMID: 36571206 DOI: 10.1177/11207000221145670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
INTRODUCTION To date the aetiology of femoroacetabular impingement (FAI) is still not completely understood. There are mechanical theories that suggest symptomatic FAI is linked to sagittal pelvic morphology and spinopelvic-femoral dynamics. The aim of this study is to evaluate the relation of sagittal pelvic morphology and orientation to radiographic signs of FAI. Additionally, we test whether the relation between FAI and spinopelvic parameters differs in osteoarthritic hips. METHODS From a prospective, observational cohort study, 1002 patients between 45 and 65 years old with a first episode of knee or hip pain were followed for 8 years. All patients who had lateral lumbar radiographs and clinical and radiographic follow-up of the hips were included in the present study. Range of internal rotation of the hip as well as radiographic signs of FAI (alpha and Wiberg angle) and presence of hip osteoarthritis (Kellgren and Lawrence) were systematically measured at baseline. Pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS)) were measured at 8-year follow-up. Associations between PI, PT, SS and FAI parameters were tested using generalised estimating equations. RESULTS 421 subjects, 842 hips, were included. No significant relations between PI, PT or SS and alpha or Wiberg angle were found. Comparison of hips with and without radiological sign(s) of FAI showed no differences in PI, PT or SS. There was no relation between range of internal rotation of the hip and spinopelvic parameters. CONCLUSION Sagittal pelvic morphology and orientation are not related to the presence of radiological signs of FAI in this study population.
Collapse
Affiliation(s)
- Joost Hj van Erp
- Clinical Orthopaedic Research Center - mN, Zeist, The Netherlands
- Department of Orthopaedics, Diakonessenhuis, Utrecht, The Netherlands
- Department of Orthopaedic Surgery, University Medical Centre Utrecht, The Netherlands
| | - Willem-Paul Gielis
- Department of Orthopaedic Surgery, University Medical Centre Utrecht, The Netherlands
| | - Vahid Arbabi
- Department of Orthopaedic Surgery, University Medical Centre Utrecht, The Netherlands
- Orthopedic-BioMechanics Research Group, Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Iran
| | - Arthur de Gast
- Clinical Orthopaedic Research Center - mN, Zeist, The Netherlands
- Department of Orthopaedics, Diakonessenhuis, Utrecht, The Netherlands
| | - Harrie Weinans
- Department of Orthopaedic Surgery, University Medical Centre Utrecht, The Netherlands
- Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands
| | - Laurens Kaas
- Department of Orthopaedic Surgery, St. Antonius hospital, Utrecht, The Netherlands
| | - René M Castelein
- Department of Orthopaedic Surgery, University Medical Centre Utrecht, The Netherlands
| | - Tom Pc Schlösser
- Department of Orthopaedic Surgery, University Medical Centre Utrecht, The Netherlands
| |
Collapse
|
12
|
Kim JS, Agricola R, Kim YJ, Lane NE, Millis MB, Nelson AE, Runhaar J, Shefelbine SJ, Bostrom MP. Arthritis Foundation/HSS Workshop on Hip Osteoarthritis, Part 1: Epidemiology, Early Development, and Cohorts From Around the World. HSS J 2023; 19:395-401. [PMID: 37937080 PMCID: PMC10626936 DOI: 10.1177/15563316231189748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 11/09/2023]
Abstract
Far more publications are available for osteoarthritis of the knee than of the hip. Recognizing this research gap, the Arthritis Foundation, in partnership with the Hospital for Special Surgery, convened an in-person meeting of thought leaders to review the state of the science of and clinical approaches to hip osteoarthritis. This article summarizes the recommendations and clinical research gaps gleaned from 5 presentations given in the "how hip osteoarthritis begins" session of the 2023 Hip Osteoarthritis Clinical Studies Conference, which took place on February 17 and 18, 2023, in New York City.
Collapse
Affiliation(s)
| | - Rintje Agricola
- Department of Orthopaedic Surgery, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Young-Jo Kim
- Department of Orthopedic Surgery, Boston Children's Hospital, Boston, MA, USA
| | - Nancy E Lane
- Department of Medicine and Rheumatology, University of California, Davis Medical Center, Sacramento, CA, USA
| | - Michael B Millis
- Department of Orthopedic Surgery, Boston Children's Hospital, Boston, MA, USA
| | - Amanda E Nelson
- Division of Rheumatology, Allergy, and Immunology, Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jos Runhaar
- Department of General Practice, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sandra J Shefelbine
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA
| | | |
Collapse
|
13
|
van Berkel AC, Schiphof D, Waarsing JH, Runhaar J, van Ochten JM, Bindels PJ, Bierma-Zeinstra SM. Nocturnal pain and fatigue in middle-aged persons with hip symptoms suspected to be osteoarthritis, is there a link in 10-year follow-up of the CHECK study? OSTEOARTHRITIS AND CARTILAGE OPEN 2023; 5:100363. [PMID: 37214788 PMCID: PMC10192639 DOI: 10.1016/j.ocarto.2023.100363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 04/11/2023] [Indexed: 05/24/2023] Open
Abstract
Objective To explore the prevalence of nocturnal pain and fatigue in participants with hip symptoms suspected to be early osteoarthritis (OA) and to test the mediating effect of nocturnal pain on the association between hip OA pain and fatigue. Methods We included participants with hip pain but no knee pain at baseline, from the Cohort Hip and Cohort Knee (CHECK)-study. Severity of hip OA pain was determined using the Numeric-Rating-Scale-pain-score last week. Fatigue was assessed using the SF-36 Fatigue subscale. Nocturnal pain was determined using the WOMAC-question: "How much pain have you experienced in the last 48 h at night while in bed?". Hip OA pain, nocturnal pain and fatigue were measured repeatedly during 10-year follow-up. Path analysis were used per time point to determine the direct effect of OA pain on fatigue and the indirect effect through nocturnal pain. Results In 170 participants (female: 76%; mean age: 55.7 years; mean BMI: 25.5 kg/m2) the prevalence of nocturnal pain varied between 22 and 35% and the prevalence of fatigue ranged between 14 and 18%. Hip OA pain was associated with nocturnal pain and fatigue. The direct effect of hip OA pain on fatigue was significant at all-time points. No significant mediating effect of nocturnal pain was found. Conclusion In this cohort of participants suspected to have early hip OA, the prevalence of fatigue remained stable and the prevalence of nocturnal pain decreased slightly over 10-year follow-up. We did not find a mediating effect of nocturnal pain in the pathway between hip OA pain and fatigue.
Collapse
Affiliation(s)
- Annemaria C. van Berkel
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Dieuwke Schiphof
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jan H. Waarsing
- Department of Orthopaedics, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jos Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - John M. van Ochten
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Patrick J.E. Bindels
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sita M.A. Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Orthopaedics, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| |
Collapse
|
14
|
Wang Q, Runhaar J, Kloppenburg M, Boers M, Bijlsma JWJ, Bacardit J, Bierma-Zeinstra SMA. A machine learning approach reveals features related to clinicians' diagnosis of clinically relevant knee osteoarthritis. Rheumatology (Oxford) 2023; 62:2732-2739. [PMID: 36534939 DOI: 10.1093/rheumatology/keac707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/09/2022] [Indexed: 08/03/2023] Open
Abstract
OBJECTIVES To identify highly ranked features related to clinicians' diagnosis of clinically relevant knee OA. METHODS General practitioners (GPs) and secondary care physicians (SPs) were recruited to evaluate 5-10 years follow-up clinical and radiographic data of knees from the CHECK cohort for the presence of clinically relevant OA. GPs and SPs were gathered in pairs; each pair consisted of one GP and one SP, and the paired clinicians independently evaluated the same subset of knees. A diagnosis was made for each knee by the GP and SP before and after viewing radiographic data. Nested 5-fold cross-validation enhanced random forest models were built to identify the top 10 features related to the diagnosis. RESULTS Seventeen clinician pairs evaluated 1106 knees with 139 clinical and 36 radiographic features. GPs diagnosed clinically relevant OA in 42% and 43% knees, before and after viewing radiographic data, respectively. SPs diagnosed in 43% and 51% knees, respectively. Models containing top 10 features had good performance for explaining clinicians' diagnosis with area under the curve ranging from 0.76-0.83. Before viewing radiographic data, quantitative symptomatic features (i.e. WOMAC scores) were the most important ones related to the diagnosis of both GPs and SPs; after viewing radiographic data, radiographic features appeared in the top lists for both, but seemed to be more important for SPs than GPs. CONCLUSIONS Random forest models presented good performance in explaining clinicians' diagnosis, which helped to reveal typical features of patients recognized as clinically relevant knee OA by clinicians from two different care settings.
Collapse
Affiliation(s)
- Qiuke Wang
- Department of General Practice, Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands
| | - Jos Runhaar
- Department of General Practice, Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands
| | - Margreet Kloppenburg
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Maarten Boers
- Department of Epidemiology and Biostatistics, Amsterdam UMC, Amsterdam, The Netherlands
| | - Johannes W J Bijlsma
- Department of Rheumatology and Clinical Immunology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Jaume Bacardit
- School of Computing, Newcastle University, Newcastle, UK
| | - Sita M A Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands
- Department of Orthopaedics and Sport Medicine, Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands
| |
Collapse
|
15
|
Altamirano S, Jansen MP, Oberski DL, Eijkemans MJC, Mastbergen SC, Lafeber FPJG, van Spil WE, Welsing PMJ. Identifying multivariate disease trajectories and potential phenotypes of early knee osteoarthritis in the CHECK cohort. PLoS One 2023; 18:e0283717. [PMID: 37450467 PMCID: PMC10348540 DOI: 10.1371/journal.pone.0283717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/15/2023] [Indexed: 07/18/2023] Open
Abstract
OBJECTIVE To gain better understanding of osteoarthritis (OA) heterogeneity and its predictors for distinguishing OA phenotypes. This could provide the opportunity to tailor prevention and treatment strategies and thus improve care. DESIGN Ten year follow-up data from CHECK (1002 early-OA subjects with first general practitioner visit for complaints ≤6 months before inclusion) was used. Data were collected on WOMAC (pain, function, stiffness), quantitative radiographic tibiofemoral (TF) OA characteristics, and semi-quantitative radiographic patellofemoral (PF) OA characteristics. Using functional data analysis, distinctive sets of trajectories were identified for WOMAC, TF and PF characteristics, based on model fit and clinical interpretation. The probabilities of knee membership to each trajectory were used in hierarchical cluster analyses to derive knee OA phenotypes. The number and composition of potential phenotypes was selected again based on model fit (silhouette score) and clinical interpretation. RESULTS Five trajectories representing different constant levels or changing WOMAC scores were identified. For TF and PF OA, eight and six trajectories respectively were identified based on (changes in) joint space narrowing, osteophytes and sclerosis. Combining the probabilities of knees belonging to these different trajectories resulted in six clusters ('phenotypes') of knees with different degrees of functional (WOMAC) and radiographic (PF) parameters; TF parameters were found not to significantly contribute to clustering. Including baseline characteristics as well resulted in eight clusters of knees, dominated by sex, menopausal status and WOMAC scores, with only limited contribution of PF features. CONCLUSIONS Several stable and progressive trajectories of OA symptoms and radiographic features were identified, resulting in phenotypes with relatively independent symptomatic and radiographic features. Sex and menopausal status may be especially important when phenotyping knee OA patients, while radiographic features contributed less. Possible phenotypes were identified that, after validation, could aid personalized treatments and patients selection.
Collapse
Affiliation(s)
- Sara Altamirano
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mylène P Jansen
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Daniel L Oberski
- Department of Methodology and Statistics, Faculty of Social and Behavioral Sciences, Utrecht University, Utrecht, the Netherlands
- Department of Data Science and Biostatistics, Julius Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marinus J C Eijkemans
- Department of Data Science and Biostatistics, Julius Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Simon C Mastbergen
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Floris P J G Lafeber
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Willem E van Spil
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Rheumatology, Dijklander Hospital, Hoorn, The Netherlands
| | - Paco M J Welsing
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
16
|
Salis Z, Sainsbury A. Association of Change in Body Mass Index With Incidence and Progression of the Structural Defects of Hip Osteoarthritis: Data From the Osteoarthritis Initiative and the Cohort Hip and Cohort Knee study. Arthritis Care Res (Hoboken) 2023; 75:1527-1537. [PMID: 36354244 PMCID: PMC10952232 DOI: 10.1002/acr.25057] [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: 03/15/2022] [Revised: 10/14/2022] [Accepted: 11/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To define the association between change in body mass index (BMI) and the incidence and progression of structural defects of hip osteoarthritis as assessed by radiography. METHODS We used data from 2 independent cohort studies: the Osteoarthritis Initiative (OAI) and the Cohort Hip and Cohort Knee (CHECK) study. Our exposure was change in BMI from baseline to 4-5 years' follow-up. Our outcomes were the incidence and progression of structural defects of hip osteoarthritis as assessed using a modified Croft grade in OAI and the Kellgren/Lawrence grade in the CHECK study. To study incidence, we created incidence cohorts of hips without definite overall structural defects at baseline (i.e., grade <2) and then investigated the odds of hips having definite overall structural defects at follow-up (i.e., grade ≥2). To study progression, we created progression cohorts of hips with definite overall structural defects at baseline (i.e., grade ≥2) and then investigated the odds of having a grade increase of ≥1 from baseline to follow-up. RESULTS There was a total of 5,896 and 1,377 hips in the incidence cohorts, and 303 and 129 hips in the progression cohorts for the OAI and CHECK study, respectively. Change in BMI (decrease or increase) was not associated with any change in odds of the incidence or progression of definite structural defects of hip osteoarthritis in either the OAI or CHECK cohorts. CONCLUSION Weight loss may not be an effective strategy for preventing, slowing, or delaying the structural defects of hip osteoarthritis over 4-5 years.
Collapse
Affiliation(s)
- Zubeyir Salis
- University of New South WalesKensingtonNew South WalesAustralia
| | | |
Collapse
|
17
|
Salis Z, Sainsbury A. Association Between Change in Body Mass Index and Knee and Hip Replacements: A Survival Analysis of Seven to Ten Years Using Multicohort Data. Arthritis Care Res (Hoboken) 2023; 75:1340-1350. [PMID: 36106942 PMCID: PMC10953021 DOI: 10.1002/acr.25021] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/01/2022] [Accepted: 09/13/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVE To define the association between change in body mass index (BMI) and the risk of knee and hip replacement. METHODS We used data from 3 independent cohort studies: the Osteoarthritis Initiative (OAI), the Multicenter Osteoarthritis Study (MOST), and the Cohort Hip and Cohort Knee (CHECK) study, which collected data from adults (45-79 years of age) with or at risk of clinically significant knee osteoarthritis. We conducted Cox proportional hazards regression analysis with clustering of both knees and hips per person to determine the association between change in BMI (our exposure of interest) and the incidence of primary knee and hip replacement over 7-10 years' follow-up. Change in BMI (in kg/m2 ) was calculated between baseline and the last follow-up visit before knee or hip replacement, or for knees and hips that were not replaced, the last follow-up visit. RESULTS A total of 16,362 knees from 8,181 participants, and 16,406 hips from 8,203 participants, were eligible for inclusion in our knee and hip analyses, respectively. Change in BMI was positively associated with the risk of knee replacement (adjusted hazard ratio [HRadj ] 1.03 [95% confidence interval (95% CI) 1.00-1.06]) but not hip replacement (HRadj 1.00 [95% CI 0.95-1.04]). The association between change in BMI and knee replacement was independent of participants' BMI category at baseline (i.e., normal, overweight, or obese). CONCLUSION Public health strategies incorporating weight loss interventions could reduce the burden of knee but not hip replacement surgery.
Collapse
Affiliation(s)
- Zubeyir Salis
- University of New South Wales Centre for Big Data Research in HealthKensingtonNew South WalesAustralia
| | - Amanda Sainsbury
- The University of Western AustraliaCrawleyWestern AustraliaAustralia
| |
Collapse
|
18
|
Heiss R, Laredo JD, Wirth W, Jansen MP, Marijnissen ACA, Lafeber F, Lalande A, Weinans HH, Blanco FJ, Berenbaum F, Kloppenburg M, Haugen IK, Engelke K, Roemer FW. Quantitative CT of the knee in the IMI-APPROACH osteoarthritis cohort: Association of bone mineral density with radiographic disease severity, meniscal coverage and meniscal extrusion. Bone 2023; 168:116673. [PMID: 36623756 DOI: 10.1016/j.bone.2023.116673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/16/2022] [Accepted: 01/04/2023] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Osteoarthritis (OA) is a highly prevalent chronic condition. The subchondral bone plays an important role in onset and progression of OA making it a potential treatment target for disease-modifying therapeutic approaches. However, little is known about changes of periarticular bone mineral density (BMD) in OA and its relation to meniscal coverage and meniscal extrusion at the knee. Thus, the aim of this study was to describe periarticular BMD in the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) cohort at the knee and to analyze the association with structural disease severity, meniscal coverage and meniscal extrusion. DESIGN Quantitative CT (QCT), MRI and radiographic examinations were acquired in 275 patients with knee osteoarthritis (OA). QCT was used to assess BMD at the femur and tibia, at the cortical bone plate (Cort) and at the epiphysis at three locations: subchondral (Sub), mid-epiphysis (Mid) and adjacent to the physis (Juxta). BMD was evaluated for the medial and lateral compartment separately and for subregions covered and not covered by the meniscus. Radiographs were used to determine the femorotibial angle and were evaluated according to the Kellgren and Lawrence (KL) system. Meniscal extrusion was assessed from 0 to 3. RESULTS Mean BMD differed significantly between each anatomic location at both the femur and tibia (p < 0.001) in patients with KL0. Tibial regions assumed to be covered with meniscus in patients with KL0 showed lower BMD at Sub (p < 0.001), equivalent BMD at Mid (p = 0.07) and higher BMD at Juxta (p < 0.001) subregions compared to regions not covered with meniscus. Knees with KL2-4 showed lower Sub (p = 0.03), Mid (p = 0.01) and Juxta (p < 0.05) BMD at the medial femur compared to KL0/1. Meniscal extrusion grade 2 and 3 was associated with greater BMD at the tibial Cort (p < 0.001, p = 0.007). Varus malalignment is associated with significant greater BMD at the medial femur and at the medial tibia at all anatomic locations. CONCLUSION BMD within the epiphyses of the tibia and femur decreases with increasing distance from the articular surface. Knees with structural OA (KL2-4) exhibit greater cortical BMD values at the tibia and lower BMD at the femur at the subchondral level and levels beneath compared to KL0/1. BMD at the tibial cortical bone plate is greater in patients with meniscal extrusion grade 2/3.
Collapse
Affiliation(s)
- Rafael Heiss
- Department of Radiology, Universitätsklinikum Erlangen & Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Jean-Denis Laredo
- Service de Radiologie, Institut Mutualiste Montsouris, 42 Bd Jourdan, 75014 Paris, France; Bioimagerie Ostéo-articulaires (B3OA), UMR, CNRS, 7052 INSERM U1271,10 Avenue de Verdun, 75010 Paris, France
| | - Wolfgang Wirth
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria; Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria; Chondrometrics GmbH, Ludwig-Zeller-Straße 12, 83395 Freilassing, Germany
| | - Mylène P Jansen
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Heidelberglaan 100, 3584, CX, Utrecht, the Netherlands
| | - Anne C A Marijnissen
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Heidelberglaan 100, 3584, CX, Utrecht, the Netherlands
| | - Floris Lafeber
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Heidelberglaan 100, 3584, CX, Utrecht, the Netherlands
| | - Agnes Lalande
- Servier, 50 rue Carnot, 92284 Suresnes cedex, France
| | - Harrie H Weinans
- Department of Orthopaedics, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Francisco J Blanco
- Grupo de Investigación de Reumatología (GIR), INIBIC - Complejo Hospitalario Universitario de A Coruña, SERGAS, Centro de Investigación CICA, Departamento de Fisioterapia y Medicina, Universidad de A Coruña, A Coruña, Spain; Servicio de Reumatologia, INIBIC- Universidade de A Coruña, As Xubias 84, 15006 A Coruña, Spain
| | - Francis Berenbaum
- Sorbonne University, Inserm, APHP Hôpital Saint-Antoine, 75571 Paris cedex 12, France
| | - Margreet Kloppenburg
- Departments of Rheumatology, Clinical Epidemiology, Leiden University Medical Center, Albinusdreef 2, 2333, ZA, Leiden, Netherlands
| | - Ida K Haugen
- Diakonhjemmet Hospital, Diakonveien 12, 0370 Oslo, Norway
| | - Klaus Engelke
- Department of Immunology and Rheumatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Ulmenweg 18, Erlangen, Germany; Institute of Medical Physics, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Henkestr. 91, 91052 Erlangen, Germany
| | - Frank W Roemer
- Department of Radiology, Universitätsklinikum Erlangen & Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Maximiliansplatz 3, 91054 Erlangen, Germany; Boston University School of Medicine, 72 E Concord St, Boston, MA, 02118, MA, USA
| |
Collapse
|
19
|
Wirth W, Maschek S, Marijnissen ACA, Lalande A, Blanco FJ, Berenbaum F, van de Stadt LA, Kloppenburg M, Haugen IK, Ladel CH, Bacardit J, Wisser A, Eckstein F, Roemer FW, Lafeber FPJG, Weinans HH, Jansen M. Test-retest precision and longitudinal cartilage thickness loss in the IMI-APPROACH cohort. Osteoarthritis Cartilage 2023; 31:238-248. [PMID: 36336198 DOI: 10.1016/j.joca.2022.10.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 09/22/2022] [Accepted: 10/30/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE To investigate the test-retest precision and to report the longitudinal change in cartilage thickness, the percentage of knees with progression and the predictive value of the machine-learning-estimated structural progression score (s-score) for cartilage thickness loss in the IMI-APPROACH cohort - an exploratory, 5-center, 2-year prospective follow-up cohort. DESIGN Quantitative cartilage morphology at baseline and at least one follow-up visit was available for 270 of the 297 IMI-APPROACH participants (78% females, age: 66.4 ± 7.1 years, body mass index (BMI): 28.1 ± 5.3 kg/m2, 55% with radiographic knee osteoarthritis (OA)) from 1.5T or 3T MRI. Test-retest precision (root mean square coefficient of variation) was assessed from 34 participants. To define progressor knees, smallest detectable change (SDC) thresholds were computed from 11 participants with longitudinal test-retest scans. Binary logistic regression was used to evaluate the odds of progression in femorotibial cartilage thickness (threshold: -211 μm) for the quartile with the highest vs the quartile with the lowest s-scores. RESULTS The test-retest precision was 69 μm for the entire femorotibial joint. Over 24 months, mean cartilage thickness loss in the entire femorotibial joint reached -174 μm (95% CI: [-207, -141] μm, 32.7% with progression). The s-score was not associated with 24-month progression rates by MRI (OR: 1.30, 95% CI: [0.52, 3.28]). CONCLUSION IMI-APPROACH successfully enrolled participants with substantial cartilage thickness loss, although the machine-learning-estimated s-score was not observed to be predictive of cartilage thickness loss. IMI-APPROACH data will be used in subsequent analyses to evaluate the impact of clinical, imaging, biomechanical and biochemical biomarkers on cartilage thickness loss and to refine the machine-learning-based s-score. CLINICALTRIALS GOV IDENTIFICATION NCT03883568.
Collapse
Affiliation(s)
- W Wirth
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; Chondrometrics GmbH, Freilassing, Germany.
| | - S Maschek
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; Chondrometrics GmbH, Freilassing, Germany.
| | - A C A Marijnissen
- University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
| | - A Lalande
- Institut de Recherches Internationales Servier, Suresnes, France.
| | - F J Blanco
- Grupo de Investigación de Reumatología (GIR), INIBIC - Complejo Hospitalario Universitario de A Coruña, SERGAS. Centro de Investigación CICA, Departamento de Fisioterapia y Medicina, Universidad de A Coruña, A Coruña, Spain.
| | - F Berenbaum
- Department of Rheumatology, AP-HP Saint-Antoine Hospital, Paris, France; INSERM, Sorbonne University, Paris, France.
| | - L A van de Stadt
- Rheumatology, Leiden University Medical Center, Leiden, the Netherlands.
| | - M Kloppenburg
- Rheumatology, Leiden University Medical Center, Leiden, the Netherlands; Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - I K Haugen
- Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, Norway.
| | - C H Ladel
- CHL4special consultancy, Darmstadt, Germany.
| | - J Bacardit
- School of Computing, Newcastle University, Newcastle, United Kingdom.
| | - A Wisser
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; Chondrometrics GmbH, Freilassing, Germany.
| | - F Eckstein
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; Chondrometrics GmbH, Freilassing, Germany.
| | - F W Roemer
- Quantitative Imaging Center, Department of Radiology, Boston University School of Medicine, Boston, MA, USA; Department of Radiology, Universitätsklinikum Erlangen and Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.
| | - F P J G Lafeber
- University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
| | - H H Weinans
- University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
| | - M Jansen
- University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
| |
Collapse
|
20
|
van Helvoort EM, Jansen MP, Marijnissen ACA, Kloppenburg M, Blanco FJ, Haugen IK, Berenbaum F, Bay-Jensen ACC, Ladel C, Lalande A, Larkin J, Loughlin J, Mobasheri A, Weinans HH, Widera P, Bacardit J, Welsing PMJ, Lafeber FPJG. Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort. Rheumatology (Oxford) 2022; 62:147-157. [PMID: 35575381 PMCID: PMC9788822 DOI: 10.1093/rheumatology/keac292] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVES The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores. METHODS Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden's index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors. RESULTS Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively). CONCLUSION The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors. TRIAL REGISTRATION ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568.
Collapse
Affiliation(s)
- Eefje M van Helvoort
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht
| | - Mylène P Jansen
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht
| | - Anne C A Marijnissen
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht
| | - Margreet Kloppenburg
- Department of Rheumatology.,Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Francisco J Blanco
- Grupo de Investigación de Reumatologia (GIR), INIBIC-Complejo Hospitalario Universitario de A Coruña, SERGAS, Centro de Investigación CICA, Departamento de Fisiotherapia y Medicina, Universidad de A Coruña, A Coruña, Spain
| | - Ida K Haugen
- Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Francis Berenbaum
- Department of Rheumatology, AP-HP Saint-Antoine Hospital.,INSERM, Centre de Recherche Saint-Antoine, Sorbonne University, Paris, France
| | | | | | - Agnes Lalande
- Institut de Recherches Internationales Servier, Suresnes, France
| | | | - John Loughlin
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Ali Mobasheri
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulo, Oulo, Finland.,Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania.,Department of Joint Surgery, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,World Health Organization Collaborating Centre for Public Health Aspects of Musculoskeletal Health and ging, Liege, Belgium.,Department of Orthopedics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Harrie H Weinans
- Department of Orthopedics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Pawel Widera
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Jaume Bacardit
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Paco M J Welsing
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht
| | - Floris P J G Lafeber
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht
| |
Collapse
|
21
|
van Berkel AC, van Spil WE, Schiphof D, Runhaar J, van Ochten JM, Bindels PJE, Bierma-Zeinstra SMA. Associations between biomarkers of matrix metabolism and inflammation with pain and fatigue in participants suspected of early hip and or knee osteoarthritis: data from the CHECK study. Osteoarthritis Cartilage 2022; 30:1640-1646. [PMID: 36115527 DOI: 10.1016/j.joca.2022.08.013] [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: 04/28/2022] [Revised: 08/22/2022] [Accepted: 08/24/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVES To assess the associations of biomarkers in serum [highsensitivity C-reactive protein (hs-CRP), serum cartilage oligomeric protein (sCOMP), serum propeptide of type I procollagen (sPINP) and serum osteocalcin (sOC)] and urine [urinary type II collagen telopeptide (uCTX-2)] with the extent and progression of nocturnal pain, pain while walking, and fatigue in participants with hip and/or knee pain suspected to be early stage osteoarthritis (OA). METHODS hs-CRP, uCTX-2, sCOMP, sPINP and sOC were measured at baseline in 1,002 participants of the Cohort Hip and Cohort Knee (CHECK). Nocturnal pain, pain while walking and fatigue were assessed by self-reported questionnaires at baseline and 2-year follow-up. Associations between these biomarkers and symptoms were examined using logistic and linear regression analyses. RESULTS hs-CRP was significantly associated with mild nocturnal pain (OR 1.18 95% CI 1.01-1.37), with mild and moderate pain while walking (OR 1.17 95% CI 1.01-1.35 and OR 1.56 95% CI 1.29-1.90, respectively) and with progression of nocturnal pain (OR 1.25 95% CI 1.07-1.46). uCTX-2 was associated with mild nocturnal pain (OR 1.40 95% CI 1.05-1.85) and with mild and severe-extreme pain while walking (OR 1.35 95% CI 1.04-1.75 and OR 2.55 95% CI 1.03-6.34, respectively). sPINP was associated with severe-extreme nocturnal pain (OR 0.45 95% CI 0.25-0.82). No significant associations were found for sCOMP and sOC, nor for any of the biomarkers and fatigue. CONCLUSION This study of biomarkers in a large cohort of participants with hip and/or knee pain suspected to reflect early stage hip and/or knee OA suggests that inflammation and cartilage matrix degeneration play a role in pain, but not in fatigue.
Collapse
Affiliation(s)
- A C van Berkel
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - W E van Spil
- Department of Rheumatology & Clinical Immunology, UMC Utrecht, Utrecht, the Netherlands; Department of Rheumatology, Dijklander Hospital, Hoorn, the Netherlands
| | - D Schiphof
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - J Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - J M van Ochten
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - P J E Bindels
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - S M A Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Orthopaedics, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| |
Collapse
|
22
|
van Berkel AC, Schiphof D, Waarsing JH, Runhaar J, van Ochten JM, Bindels PJE, Bierma-Zeinstra SMA. Course of pain and fluctuations in pain related to suspected early hip osteoarthritis: the CHECK study. Fam Pract 2022; 39:1041-1048. [PMID: 35365995 PMCID: PMC9680658 DOI: 10.1093/fampra/cmac030] [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] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES To evaluate the prevalence during a 10-year follow-up of clinically relevant fluctuations in pain and the course of hip pain in participants with hip complaints suspected to be early stage hip osteoarthritis (OA). To distinguish between participants with relevant fluctuations in pain and those without based on baseline characteristics. METHODS Data were collected at baseline and after 2, 5, 8, and 10 years on 495 participants from the Cohort Hip and Cohort Knee Study (CHECK) with hip pain at baseline. Baseline demographic, anamnestic, and physical-examination characteristics were assessed. The primary outcome was levels of pain in the past week (scored using 0-10 Numeric Rating Scale) at follow-up assessments. Relevant fluctuation was defined as average absolute residuals greater than 1 after fitting a straight line to the participant's pain scores over time. RESULTS The majority of the participants (76%) had stable or decreasing pain. Relevant fluctuations were found in 37% of the participants. The following baseline variables were positively associated with the presence of relevant fluctuations: higher levels of pain in the past week, use of pain transformation as a coping style, higher number of comorbidities, use of pain medication, and higher levels of high-sensitivity C-reactive protein. No associations were found for baseline radiographic hip OA or clinical hip OA. CONCLUSION During a 10-year follow-up, the majority of participants had stable or decreasing pain levels. In those participants with relevant fluctuation (37%), a limited number of baseline variables were associated with increased odds of having relevant fluctuations in pain.
Collapse
Affiliation(s)
- Annemaria C van Berkel
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Dieuwke Schiphof
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jan H Waarsing
- Department of Orthopaedics, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jos Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - John M van Ochten
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Patrick J E Bindels
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sita M A Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Orthopaedics, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| |
Collapse
|
23
|
Roemer FW, Jansen M, Marijnissen ACA, Guermazi A, Heiss R, Maschek S, Lalande A, Blanco FJ, Berenbaum F, van de Stadt LA, Kloppenburg M, Haugen IK, Ladel CH, Bacardit J, Wisser A, Eckstein F, Lafeber FPJG, Weinans HH, Wirth W. Structural tissue damage and 24-month progression of semi-quantitative MRI biomarkers of knee osteoarthritis in the IMI-APPROACH cohort. BMC Musculoskelet Disord 2022; 23:988. [DOI: 10.1186/s12891-022-05926-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/27/2022] [Indexed: 11/18/2022] Open
Abstract
Abstract
Background
The IMI-APPROACH cohort is an exploratory, 5-centre, 2-year prospective follow-up study of knee osteoarthritis (OA). Aim was to describe baseline multi-tissue semiquantitative MRI evaluation of index knees and to describe change for different MRI features based on number of subregion-approaches and change in maximum grades over a 24-month period.
Methods
MRIs were acquired using 1.5 T or 3 T MRI systems and assessed using the semi-quantitative MRI OA Knee Scoring (MOAKS) system. MRIs were read at baseline and 24-months for cartilage damage, bone marrow lesions (BML), osteophytes, meniscal damage and extrusion, and Hoffa- and effusion-synovitis. In descriptive fashion, the frequencies of MRI features at baseline and change in these imaging biomarkers over time are presented for the entire sample in a subregional and maximum score approach for most features. Differences between knees without and with structural radiographic (R) OA are analyzed in addition.
Results
Two hundred eighty-nine participants had readable baseline MRI examinations. Mean age was 66.6 ± 7.1 years and participants had a mean BMI of 28.1 ± 5.3 kg/m2. The majority (55.3%) of included knees had radiographic OA. Any change in total cartilage MOAKS score was observed in 53.1% considering full-grade changes only, and in 73.9% including full-grade and within-grade changes. Any medial cartilage progression was seen in 23.9% and any lateral progression on 22.1%. While for the medial and lateral compartments numbers of subregions with improvement and worsening of BMLs were very similar, for the PFJ more improvement was observed compared to worsening (15.5% vs. 9.0%). Including within grade changes, the number of knees showing BML worsening increased from 42.2% to 55.6%. While for some features 24-months change was rare, frequency of change was much more common in knees with vs. without ROA (e.g. worsening of total MOAKS score cartilage in 68.4% of ROA knees vs. 36.7% of no-ROA knees, and 60.7% vs. 21.8% for an increase in maximum BML score per knee).
Conclusions
A wide range of MRI-detected structural pathologies was present in the IMI-APPROACH cohort. Baseline prevalence and change of features was substantially more common in the ROA subgroup compared to the knees without ROA.
Trial Registration
Clinicaltrials.gov identification: NCT03883568.
Collapse
|
24
|
Salis Z, Gallego B, Nguyen TV, Sainsbury A. Association of Decrease in Body Mass Index With Reduced Incidence and Progression of the Structural Defects of Knee Osteoarthritis: A Prospective Multi-Cohort Study. Arthritis Rheumatol 2022; 75:533-543. [PMID: 35974435 DOI: 10.1002/art.42307] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/10/2022] [Accepted: 07/13/2022] [Indexed: 01/31/2023]
Abstract
OBJECTIVE To define the association between change in body mass index (BMI) and the incidence and progression of the structural defects of knee osteoarthritis as assessed by radiography. METHODS Radiographic analyses of knees at baseline and at 4-5 years of follow-up were obtained from the following 3 independent cohort studies: the Osteoarthritis Initiative (OAI) study, the Multicenter Osteoarthritis Study (MOST), and the Cohort Hip and Cohort Knee (CHECK) study. Logistic regression analyses using generalized estimating equations, with clustering of both knees within individuals, were used to investigate the association between change in BMI from baseline to 4-5 years of follow-up and the incidence and progression of knee osteoarthritis. RESULTS A total of 9,683 knees (from 5,774 participants) in an "incidence cohort" and 6,075 knees (from 3,988 participants) in a "progression cohort" were investigated. Change in BMI was positively associated with both the incidence and progression of the structural defects of knee osteoarthritis. The adjusted odds ratio (OR) for osteoarthritis incidence was 1.05 (95% confidence interval [95% CI] 1.02-1.09), and the adjusted OR for osteoarthritis progression was 1.05 (95% CI 1.01-1.09). Change in BMI was also positively associated with degeneration (i.e., narrowing) of the joint space and with degeneration of the femoral and tibial surfaces (as indicated by osteophytes) on the medial but not on the lateral side of the knee. CONCLUSION A decrease in BMI was independently associated with lower odds of incidence and progression of the structural defects of knee osteoarthritis and could be a component in preventing the onset or worsening of knee osteoarthritis.
Collapse
Affiliation(s)
- Zubeyir Salis
- Centre for Big Data Research in Health, The University of New South Wales, Kensington, Australia
| | - Blanca Gallego
- Centre for Big Data Research in Health, The University of New South Wales, Kensington, Australia
| | - Tuan V Nguyen
- Centre for Health Technologies, University of Technology Sydney, Ultimo, New South Wales, Australia, and School of Population Health, UNSW Medicine & Health, The University of New South Wales, Kensington, New South Wales, Australia
| | - Amanda Sainsbury
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
| |
Collapse
|
25
|
Rondas GA, Macri EM, Oei EH, Bierma-Zeinstra SM, Rijkels-Otters HB, Runhaar J. Association between hip pain and radiographic hip osteoarthritis in primary care: the CHECK cohort. Br J Gen Pract 2022; 72:BJGP.2021.0547. [PMID: 36127152 PMCID: PMC9512408 DOI: 10.3399/bjgp.2021.0547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 06/08/2022] [Indexed: 10/31/2022] Open
Abstract
BACKGROUND The diagnosis of hip osteoarthritis (OA) is often based on clinical symptoms, such as pain and stiffness, and radiographic features. However, the association between hip pain and hip radiographic OA (ROA) remains uncertain. AIM To examine the association between hip pain and hip ROA. DESIGN AND SETTING Cross-sectional analysis of a Dutch cohort, the Cohort Hip and Cohort Knee (CHECK) study. METHOD The participants (aged 45-65 years) had all experienced hip and/or knee pain for which they had not had a prior consultation or were within 6 months of their first consultation with a GP. Using weight-bearing anteroposterior pelvis radiographs, definite and early-stage hip ROA were defined as Kellgren and Lawrence grade ≥2 and ≥1, respectively. Presence of ROA and pain was assessed in the hips of all participants. The association between hip pain and ROA was assessed using generalised estimating equations. RESULTS The prevalence of definite ROA was 11.0% (n = 218/1982 hips), with prevalence in painful and pain-free hips of 13.3% (n = 105/789) and 9.5% (n = 113/1193), respectively. Prevalence of early-stage hip ROA was 35.3% (n = 700/1982), with prevalence in painful and pain-free hips of 41.2% (n = 325/789) and 31.4% (n = 375/1193), respectively. Compared with pain-free hips, the odds ratio painful hips was 1.51 (95% confidence interval [CI] = 1.16 to 1.98) for definite ROA and 1.47 (95% CI = 1.24 to 1.75) for early-stage ROA. CONCLUSION Hip pain was associated with definite and early-stage hip ROA, yet the overall ROA prevalence was modest and the prevalence among pain-free hips was substantial. Therefore, radiographs provided little assistance with help to identify patients with hip OA among patients who recently presented with hip or knee complaints.
Collapse
Affiliation(s)
| | - Erin M Macri
- Department of Orthopaedics and Sports Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam
| | - Edwin Hg Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam
| | - Sita Ma Bierma-Zeinstra
- Department of General Practice and Department of Orthopedics & Sports Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam
| | | | - Jos Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam
| |
Collapse
|
26
|
Sedaghati-Khayat B, Boer CG, Runhaar J, Bierma-Zeinstra SMA, Broer L, Ikram MA, Zeggini E, Uitterlinden AG, van Rooij JGJ, van Meurs JBJ. Risk assessment for hip and knee osteoarthritis using polygenic risk scores. Arthritis Rheumatol 2022; 74:1488-1496. [PMID: 35644035 PMCID: PMC9541521 DOI: 10.1002/art.42246] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 04/24/2022] [Accepted: 05/24/2022] [Indexed: 11/25/2022]
Abstract
Objective Polygenic risk scores (PRS) allow risk stratification using common single‐nucleotide polymorphisms (SNPs), and clinical applications are currently explored for several diseases. This study was undertaken to assess the risk of hip and knee osteoarthritis (OA) using PRS. Methods We analyzed 12,732 individuals from a population‐based cohort from the Rotterdam Study (n = 11,496), a clinical cohort (Cohort Hip and Cohort Knee [CHECK] study; n = 908), and a high‐risk cohort of overweight women (Prevention of Knee OA in Overweight Females [PROOF] study; n = 328), for the association of the PRS with prevalence/incidence of radiographic OA, of clinical OA, and of total hip replacement (THR) or total knee replacement (TKR). The hip PRS and knee PRS contained 44 and 24 independent SNPs, respectively, and were derived from a recent genome‐wide association study meta‐analysis. Standardized PRS (with Z transformation) were used in all analyses. Results We found a stronger association of the PRS for clinically defined OA compared to radiographic OA phenotypes, and we observed the highest PRS risk stratification for TKR/THR. The odds ratio (OR) per SD was 1.3 for incident THR (95% confidence interval [95% CI] 1.1–1.5) and 1.6 (95% CI 1.3–1.9) for incident TKR in the Rotterdam Study. The knee PRS was associated with incident clinical knee OA in the CHECK study (OR 1.3 [95% CI 1.1–1.5]), but not for the PROOF study (OR 1.2 [95% CI 0.8–1.7]). The OR for OA increased gradually across the PRS distribution, up to 2.1 (95% CI 1.4–3.2) for individuals with the 10% highest PRS compared to the middle 50% of the PRS distribution. Conclusion Our findings validated the association of PRS across OA definitions. Since OA is becoming frequent and primary prevention is not commonly applicable, PRS‐based risk assessment could play a role in OA prevention. However, the utility of PRS is dependent on the setting. Further studies are needed to test the integration of genetic risk assessment in diverse health care settings.
Collapse
Affiliation(s)
- Bahar Sedaghati-Khayat
- Department of Internal medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Cindy G Boer
- Department of Internal medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jos Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sita M A Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Orthopaedics & Sports Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Linda Broer
- Department of Internal medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| | - André G Uitterlinden
- Department of Internal medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jeroen G J van Rooij
- Department of Internal medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Joyce B J van Meurs
- Department of Internal medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| |
Collapse
|
27
|
Wang Q, Runhaar J, Kloppenburg M, Boers M, Bijlsma J, Bierma-Zeinstra S. Diagnosis for early stage knee osteoarthritis: probability stratification, internal and external validation; data from the CHECK and OAI cohorts. Semin Arthritis Rheum 2022; 55:152007. [DOI: 10.1016/j.semarthrit.2022.152007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/22/2022] [Accepted: 04/11/2022] [Indexed: 10/18/2022]
|
28
|
Binvignat M, Pedoia V, Butte AJ, Louati K, Klatzmann D, Berenbaum F, Mariotti-Ferrandiz E, Sellam J. Use of machine learning in osteoarthritis research: a systematic literature review. RMD Open 2022; 8:rmdopen-2021-001998. [PMID: 35296530 PMCID: PMC8928401 DOI: 10.1136/rmdopen-2021-001998] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 02/16/2022] [Indexed: 11/21/2022] Open
Abstract
Objective The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA). Methods A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected. Results From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles. Conclusion This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.
Collapse
Affiliation(s)
- Marie Binvignat
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France.,Bakar Computational Health Science Institute, University of California, San Francisco, California, USA.,Immunology Immunopathology Immunotherapy UMRS_959, Sorbonne Universite, Paris, France
| | - Valentina Pedoia
- Center for Intelligent Imaging (CI2), Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Atul J Butte
- Bakar Computational Health Science Institute, University of California, San Francisco, California, USA
| | - Karine Louati
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
| | - David Klatzmann
- Immunology Immunopathology Immunotherapy UMRS_959, Sorbonne Universite, Paris, France.,Biotherapy (CIC-BTi) and Inflammation Immunopathology-Biotherapy Department (i2B), Hôpital Pitié-Salpêtrière, AP-HP, Paris, France
| | - Francis Berenbaum
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
| | | | - Jérémie Sellam
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
| |
Collapse
|
29
|
Gebre RK, Hirvasniemi J, van der Heijden RA, Lantto I, Saarakkala S, Leppilahti J, Jämsä T. Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT. Osteoporos Int 2022; 33:355-365. [PMID: 34476540 PMCID: PMC8813821 DOI: 10.1007/s00198-021-06130-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 08/20/2021] [Indexed: 10/27/2022]
Abstract
UNLABELLED We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images. INTRODUCTION In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT). METHODS The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher. The study data was split into 55% training, 30% validation, and 15% test sets. A pretrained ResNet18 was optimized for a classification task of rHOA vs. no-rHOA. Five models were trained using (1) X-rays, (2) downsampled X-rays, (3) combination of CT-AP and X-ray images, (4) combination of CT-AP and downsampled X-ray images, and (5) CT-AP images. RESULTS Amongst the five models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images. Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve (ROC AUC) of 0.93 [0.75-0.99]. Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89 [0.67-0.97]. CONCLUSION CT-based summation images that resemble radiographs can be used to detect rHOA. In addition, in the absence of large training data, a reliable DL model can be optimized by combining CT-AP and X-ray images.
Collapse
Affiliation(s)
- R K Gebre
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
| | - J Hirvasniemi
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - R A van der Heijden
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - I Lantto
- Division of Orthopaedic and Trauma Surgery, Oulu University Hospital, Oulu, Finland
- Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - S Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland
- Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - J Leppilahti
- Division of Orthopaedic and Trauma Surgery, Oulu University Hospital, Oulu, Finland
- Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - T Jämsä
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland
- Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| |
Collapse
|
30
|
Runhaar J, Özbulut Ö, Kloppenburg M, Boers M, Bijlsma JWJ, Bierma-Zeinstra SMA. Diagnostic criteria for early hip osteoarthritis: first steps, based on the CHECK study. Rheumatology (Oxford) 2021; 60:5158-5164. [PMID: 33576791 PMCID: PMC8566292 DOI: 10.1093/rheumatology/keab111] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 12/21/2020] [Accepted: 01/18/2021] [Indexed: 11/14/2022] Open
Abstract
Objectives Although there is a general focus on early diagnosis and treatment of hip OA, there are no validated diagnostic criteria for early-stage hip OA. The current study aimed to take the first steps in developing diagnostic criteria for early-stage hip OA, using factors obtained through history taking, physical examination, radiography and blood testing at the first consultation in individuals presenting with hip pain, suspicious for hip OA, in primary care. Methods Data of the 543 individuals with 735 symptomatic hips at baseline who had any follow-up data available from the prospective CHECK cohort study were used. A group of 26 clinical experts [general practitioners (GPs), rheumatologists and orthopaedic surgeons] evaluated standardized clinical assessment forms of all subjects on the presence of clinically relevant hip OA 5–10 years after baseline. Using the expert-based diagnoses as reference standard, a backward selection method was used to create predictive models based on pre-defined baseline factors from history taking, physical examination, radiography and blood testing. Results Prevalence of clinically relevant hip OA during follow-up was 22%. Created models contained four to eight baseline factors (mainly WOMAC pain items, painful/restricted movements and radiographic features) and obtained area under the curve between 0.62 (0.002) and 0.71 (0.002). Conclusion Based on clinical and radiographic features of hip OA obtained at first consultation at a GP for pain/stiffness of the hip, the prediction of clinically relevant hip OA within 5–10 years was ‘poor’ to ‘fair’.
Collapse
Affiliation(s)
- Jos Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam
| | - Ömer Özbulut
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam
| | | | - Maarten Boers
- Department of Epidemiology & Biostatistics, Amsterdam UMC, Amsterdam
| | - Johannes W J Bijlsma
- Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht
| | - Sita M A Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam.,Department of Orthopedics, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | |
Collapse
|
31
|
van Berkel AC, Schiphof D, Waarsing JH, Runhaar J, van Ochten JM, Bindels PJE, Bierma-Zeinstra SMA. Characteristics associated between the incidence of hip osteoarthritis and early hip complaints (CHECK study) within 10 years. Rheumatology (Oxford) 2021; 60:5012-5019. [PMID: 33576373 PMCID: PMC8566295 DOI: 10.1093/rheumatology/keab137] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/03/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE To determine which baseline characteristics, especially clinically variables like pain, stiffness, physical functioning and disease variables, are associated with incident hip OA within 10 years in first presenters with hip complaints. Rheumatology key messages History taking and not physical exam variables are associated with incident hip osteoarthritis. Specific questions about daily life activities are associated with incident hip OA. These questions are about pain while walking/shopping, difficulties putting socks on/off and rising from bed. METHODS Data were obtained from the nationwide prospective Cohort Hip and Cohort Knee (CHECK) study (n = 1002). Incident hip OA was defined as fulfilling the clinical ACR criteria for hip OA, a Kellgren and Lawrence score ≥2 with hip pain, or received a hip replacement during follow-up. Baseline measurements were used of participants with hip complaints and without hip OA. Principal component analysis (PCA) was used to reduce the number of correlated variables. Associations between baseline characteristics (including PCA components) and incident hip OA were investigated using logistic regression analysis, adjusted for age, sex and BMI. RESULTS In total, 312 participants (85% female and 98% Caucasian) were included, 181 developed hip OA. PCA resulted in four components. Incident hip OA was associated with (i) component 1 (general presence of pain and symptoms) [odds ratio (OR) = 1.46 (95%CI: 1.08, 1.98)], (ii) component 3 (relatively high levels of pain during shopping/walking combined with less difficulty with putting socks on/off and rising from bed) [OR = 1.58 (95%CI: 1.18, 2.12)] and (iii) knee pain [OR = 0.34 (95% CI: 0.17, 0.66)]. CONCLUSION In first presenters with hip complaints, use of a few history-taking variables might allow better recognition of those at higher odds for incident hip OA within 10 years.
Collapse
Affiliation(s)
| | | | - Jan H Waarsing
- Department of Orthopaedics, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | | | | | - Sita M A Bierma-Zeinstra
- Department of General Practice.,Department of Orthopaedics, Erasmus Medical Center, Rotterdam, The Netherlands
| |
Collapse
|
32
|
Jansen MP, Welsing PMJ, Vincken KL, Mastbergen SC. Performance of knee image digital analysis of radiographs of patients with end-stage knee osteoarthritis. Osteoarthritis Cartilage 2021; 29:1530-1539. [PMID: 34343678 DOI: 10.1016/j.joca.2021.07.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 06/10/2021] [Accepted: 07/24/2021] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Knee Image Digital Analysis (KIDA) is standardized radiographic analysis software for measuring osteoarthritis (OA) characteristics. It was validated in mild OA, but used for severe OA as well. The current goal was to evaluate the performance of KIDA in severe OA. DESIGN Of 103 patients, standardized radiographs were performed before and one and 2 years after treatment for severe OA. All radiographs were evaluated on subchondral bone density, joint space width (JSW), osteophytes, eminence height, and joint angle, twice within years by the same observer. Part of the radiographs were randomly selected for reevaluation twice within 1 month and evaluation by another observer. The intraclass correlation coefficient (ICC), smallest detectable difference (SDD) and coefficient of variation (CV) were calculated; the SDD and CV were compared to those in mild OA. The relation of severity with KIDA parameters and with observer differences was calculated with linear regression. RESULTS Intra-observer ICCs were higher in the 98 severe radiographs reanalyzed within 1 month (all >0.8) than the 293 reanalyzed within years (all >0.5; most >0.8) and than inter-observer ICCs (all >0.7). SDDs and CVs were smaller when reanalyzed within a month and comparable to those in mild OA. Some parameters showed bias between readings. Severity showed significant relation with osteophytes and JSW parameters, and with the observer variation in these parameters (all P < 0.04). CONCLUSIONS KIDA is a well-performing tool also for severe OA. In order to decrease variability and SDDs, images should be analyzed in a limited time frame and randomized order.
Collapse
Affiliation(s)
- M P Jansen
- Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - P M J Welsing
- Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - K L Vincken
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - S C Mastbergen
- Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, the Netherlands.
| |
Collapse
|
33
|
van Helvoort EM, Ladel C, Mastbergen S, Kloppenburg M, Blanco FJ, Haugen IK, Berenbaum F, Bacardit J, Widera P, Welsing PMJ, Lafeber F. Baseline clinical characteristics of predicted structural and pain progressors in the IMI-APPROACH knee OA cohort. RMD Open 2021; 7:rmdopen-2021-001759. [PMID: 34426541 PMCID: PMC8383877 DOI: 10.1136/rmdopen-2021-001759] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 08/14/2021] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVES To describe the relations between baseline clinical characteristics of the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) participants and their predicted probabilities for knee osteoarthritis (OA) structural (S) progression and/or pain (P) progression. METHODS Baseline clinical characteristics of the IMI-APPROACH participants were used for this study. Radiographs were evaluated according to Kellgren and Lawrence (K&L grade) and Knee Image Digital Analysis. Knee Injury and Osteoarthritis Outcome Score (KOOS) and Numeric Rating Scale (NRS) were used to evaluate pain. Predicted progression scores for each individual were determined using machine learning models. Pearson correlation coefficients were used to evaluate correlations between scores for predicted progression and baseline characteristics. T-tests and χ2 tests were used to evaluate differences between participants with high versus low progression scores. RESULTS Participants with high S progressions score were found to have statistically significantly less structural damage compared with participants with low S progression scores (minimum Joint Space Width, minJSW 3.56 mm vs 1.63 mm; p<0.001, K&L grade; p=0.028). Participants with high P progression scores had statistically significantly more pain compared with participants with low P progression scores (KOOS pain 51.71 vs 82.11; p<0.001, NRS pain 6.7 vs 2.4; p<0.001). CONCLUSIONS The baseline minJSW of the IMI-APPROACH participants contradicts the idea that the (predicted) course of knee OA follows a pattern of inertia, where patients who have progressed previously are more likely to display further progression. In contrast, for pain progressors the pattern of inertia seems valid, since participants with high P score already have more pain at baseline compared with participants with a low P score.
Collapse
Affiliation(s)
| | | | - Simon Mastbergen
- Rheumatology & Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands
| | - Margreet Kloppenburg
- Rheumatology, Leids Universitair Medisch Centrum, Leiden, Zuid-Holland, The Netherlands.,Epidemiology, Leids Universitair Medisch Centrum, Leiden, Zuid-Holland, The Netherlands
| | - Francisco J Blanco
- Servicio de Reumatologia, Complexo Hospitalario Universitario A Coruña, A Coruna, Galicia, Spain
| | - Ida K Haugen
- Rheumatology, Diakonhjemmet Hospital, Oslo, Norway
| | - Francis Berenbaum
- Rheumatology, Assistance Publique Hopitaux de Paris, Paris, Île-de-France, France
| | - Jaume Bacardit
- School of Computing Science, Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
| | - Pawel Widera
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Paco M J Welsing
- Rheumatology & Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands
| | - Floris Lafeber
- Rheumatology & Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands
| |
Collapse
|
34
|
Wang Q, Runhaar J, Kloppenburg M, Boers M, Bijlsma JWJ, Bierma-Zeinstra SMA. Diagnosis of early stage knee osteoarthritis based on early clinical course: data from the CHECK cohort. Arthritis Res Ther 2021; 23:217. [PMID: 34412670 PMCID: PMC8375192 DOI: 10.1186/s13075-021-02598-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022] Open
Abstract
Background Early diagnosis of knee osteoarthritis (OA) is important in managing this disease, but such an early diagnostic tool is still lacking in clinical practice. The purpose of this study was to develop diagnostic models for early stage knee OA based on the first 2-year clinical course after the patient’s initial presentation in primary care and to identify whether these course factors had additive discriminative value over baseline factors. Methods We extracted eligible patients’ clinical and radiographic data from the CHECK cohort and formed the first 2-year course factors according to the factors’ changes over the 2 years. Clinical expert consensus-based diagnosis, which was made via evaluating patients’ 5- to 10-year follow-up data, was used as the outcome factor. Four models were developed: model 1, included clinical course factors only; model 2, included clinical and radiographic course factors; model 3, clinical baseline factors + clinical course factors; and model 4, clinical and radiographic baseline factors + clinical and radiographic course factors. All the models were built by a generalized estimating equation with a backward selection method. Area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (CI) were calculated for assessing model discrimination. Delong’s method compared AUCs. Results Seven hundred sixty-one patients with 1185 symptomatic knees were included in this study. Thirty-seven percent knees were diagnosed as OA at follow-up. Model 1 contained 6 clinical course factors; model 2: 6 clinical and 3 radiographic course factors; model 3: 6 baseline clinical factors combined with 5 clinical course factors; and model 4: 4 clinical and 1 radiographic baseline factors combined with 5 clinical and 3 radiographic course factors. Model discriminations are as follows: model 1, AUC 0.70 (95% CI 0.67–0.74); model 2, 0.74 (95% CI 0.71–0.77); model 3, 0.77 (95% CI 0.74–0.80); and model 4, 0.80 (95% CI 0.77–0.82). AUCs of model 3 and model 4 were slightly but significantly higher than corresponding baseline-factor models (model 3 0.77 vs 0.75, p = 0.031; model 4 0.80 vs 0.76, p = 0.003). Conclusions Four diagnostic models were developed with “fair” to “good” discriminations. First 2-year course factors had additive discriminative value over baseline factors. Supplementary Information The online version contains supplementary material available at 10.1186/s13075-021-02598-5.
Collapse
Affiliation(s)
- Qiuke Wang
- Department of General Practice, Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands.
| | - Jos Runhaar
- Department of General Practice, Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands
| | - Margreet Kloppenburg
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Maarten Boers
- Department of Epidemiology & Biostatistics, Amsterdam UMC, Amsterdam, The Netherlands
| | - Johannes W J Bijlsma
- Department of Rheumatology and Clinical Immunology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Sita M A Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands.,Department of Orthopaedics, Erasmus MC University Center Rotterdam, Rotterdam, The Netherlands
| | | |
Collapse
|
35
|
Runhaar J, Kloppenburg M, Boers M, Bijlsma JWJ, Bierma-Zeinstra SMA. Towards developing diagnostic criteria for early knee osteoarthritis: data from the CHECK study. Rheumatology (Oxford) 2021; 60:2448-2455. [PMID: 33246329 PMCID: PMC8121451 DOI: 10.1093/rheumatology/keaa643] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 08/23/2020] [Indexed: 11/12/2022] Open
Abstract
Objectives There is a general consensus that a shift in focus towards early diagnosis and treatment of knee OA is warranted. However, there are no validated and widely accepted diagnostic criteria for early knee OA available. The current study aimed to take the first steps towards developing diagnostic criteria for early knee OA. Methods Data of 761 individuals with 1185 symptomatic knees at baseline were selected from the CHECK study. For CHECK, individuals with pain/stiffness of the knee, aged 45–65 years, who had no prior consultation or a first consultation with the general practitioner for these symptoms in the past 6 months were recruited and followed for 10 years. A group of 36 experts (17 general practitioners and 19 secondary care physicians) evaluated the medical records in pairs to diagnose the presence of clinically relevant knee OA 5–10 years after enrolment. A backward selection methods was used to create predictive models based on pre-defined baseline factors from history taking, physical examination, radiography and blood testing, using the experts’ diagnoses as gold standard outcome. Results Prevalence of clinically relevant knee OA during follow-up was 37%. Created models contained 7–11 baseline factors and obtained an area under the curve between 0.746 (0.002) and 0.764 (0.002). Conclusion The obtained diagnostic models for early knee OA had ‘fair’ predictive ability in individuals presenting with knee pain in primary care. Further modelling and validation of the identified predictive factors is required to obtain clinically feasible and relevant diagnostic criteria for early knee OA.
Collapse
Affiliation(s)
- J Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam
| | - M Kloppenburg
- Department of Rheumatology, Leiden University Medical Center, Leiden
| | - M Boers
- Department of Epidemiology & Biostatistics, Amsterdam UMC, Amsterdam
| | - J W J Bijlsma
- Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht
| | - S M A Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam.,Department of Orthopaedics, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | |
Collapse
|
36
|
van Berkel AC, Schiphof D, Waarsing JH, Runhaar J, van Ochten JM, Bindels PJE, Bierma-Zeinstra SMA. 10-Year natural course of early hip osteoarthritis in middle-aged persons with hip pain: a CHECK study. Ann Rheum Dis 2021; 80:487-493. [PMID: 33451999 PMCID: PMC7958083 DOI: 10.1136/annrheumdis-2020-218625] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 10/14/2020] [Accepted: 11/06/2020] [Indexed: 12/21/2022]
Abstract
Objective To explore the natural course of hip osteoarthritis (OA) in a population of first-time presenters with hip complaints. Methods Data were collected at baseline and after 2, 5, 8 and 10 years on participants from the Cohort Hip and Cohort Knee study with early symptomatic hip OA. Descriptive statistics were used to analyse the natural course of the hip complaints with respect to clinical signs and symptoms, physical functioning and radiographic osteoarthritis (ROA) features. Results In total, 588 participants were included with hip complaints and 86% completed the 10-year follow-up. The 10-year follow-up showed that 12% (69 participants) underwent hip replacement (HR), an increase of ROA of the hip (Kellgren and Lawrence score≥2) from 19% to 49%, and an increase in clinical hip OA according to the American College of Rheumatology criteria from 27% to 43%. All Western Ontario and McMaster Osteoarthritis Index subscales and physical activity remained on average constant during the 10-year follow-up for those who did not undergo an HR. The use of pain medication increased from 43% at baseline to 50% after 10 years. Conclusion One out of nine participants with early hip problems received an HR during the 10-year follow-up. Prevalence of clinical hip OA and hip ROA increased steadily during the 10-year follow-up. Overall, we observed more hip OA, but fewer or stable complaints with respect to clinical signs and symptoms, and physical functioning. So it could be cautiously concluded that after 10 years, first-time presenters with hip complaints either received an HR or their symptoms remained stable.
Collapse
Affiliation(s)
- Annemaria C van Berkel
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Dieuwke Schiphof
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Jan H Waarsing
- Department of Orthopaedics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Jos Runhaar
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - John M van Ochten
- Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Patrick J E Bindels
- Department of General Practice, 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 Orthopaedics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| |
Collapse
|
37
|
Macri EM, Runhaar J, Damen J, Oei EH, Bierma-Zeinstra SM. Kellgren & Lawrence grading in cohort studies: methodological update and implications illustrated using data from the CHECK cohort. Arthritis Care Res (Hoboken) 2021; 74:1179-1187. [PMID: 33450140 PMCID: PMC9541941 DOI: 10.1002/acr.24563] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 11/26/2020] [Accepted: 01/12/2021] [Indexed: 11/13/2022]
Abstract
Objective The Cohort Hip and Cohort Knee (CHECK) is a cohort of middle‐aged individuals with hip or knee pain. Radiographs were assigned Kellgren/Lawrence (K/L) scores under different conditions at each follow‐up visit for 10 years. We aimed to describe and consolidate each scoring approach, then illustrate implications of their use by comparing baseline K/L scores assigned using 2 of these approaches, and evaluating their respective associations with joint replacement and incident radiographic osteoarthritis (ROA). Methods We compared baseline K/L scores assigned to hips and knees using 2 scoring approaches: 1) assigned by senior researchers to baseline images alone and 2) assigned by trained readers, with images read paired and in known sequence with up to 10 years of follow‐up radiographs (Poisson regression). We evaluated the associations of baseline ROA (any: K/L grade ≥1; established: K/L ≥2) with joint replacement, and of K/L 1 joints with incident established ROA (survival analysis). Results Of 1,002 participants (79% women, mean ± SD age 55.9 ± 5.2 years, body mass index 26.2 ± 4.0 kg/m2), the second scoring approach had 2.4 times (95% confidence interval [95% CI] 1.8–3.1 for knees) and 2.9 times (95% CI 2.3–3.7 for hips) higher prevalence of established ROA than the first approach. Established hip ROA had a higher risk of joint replacement using the first approach (hazard ratio [HR] 24.2 [95% CI 15.0–39.8] versus second approach HR 7.7 [95% CI 4.9–12.1]), as did knees (HR 19.3 [95% CI 10.3–36.1] versus second approach HR 4.8 [95% CI 2.4–9.6]). The risk of incident ROA did not differ by approach. Conclusion This study demonstrates that evaluating ROA prevalence and predicting outcomes depends on the scoring approach.
Collapse
Affiliation(s)
- Erin M Macri
- Department of General Practice, Erasmus University Medical Center Rotterdam, Netherlands.,Department of Orthopaedics and Sports Medicine, Erasmus University Medical Center Rotterdam, Netherlands
| | - Jos Runhaar
- Department of General Practice, Erasmus University Medical Center Rotterdam, Netherlands
| | - Jurgen Damen
- Department of General Practice, Erasmus University Medical Center Rotterdam, Netherlands
| | - Edwin Hg Oei
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, Netherlands
| | - Sita Ma Bierma-Zeinstra
- Department of General Practice, Erasmus University Medical Center Rotterdam, Netherlands.,Department of Orthopaedics and Sports Medicine, Erasmus University Medical Center Rotterdam, Netherlands
| |
Collapse
|
38
|
Gielis WP, Rayegan H, Arbabi V, Ahmadi Brooghani SY, Lindner C, Cootes TF, de Jong PA, Weinans H, Custers RJH. Predicting the mechanical hip-knee-ankle angle accurately from standard knee radiographs: a cross-validation experiment in 100 patients. Acta Orthop 2020; 91:732-737. [PMID: 32567436 PMCID: PMC8023880 DOI: 10.1080/17453674.2020.1779516] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background and purpose - Being able to predict the hip-knee-ankle angle (HKAA) from standard knee radiographs allows studies on malalignment in cohorts lacking full-limb radiography. We aimed to develop an automated image analysis pipeline to measure the femoro-tibial angle (FTA) from standard knee radiographs and test various FTA definitions to predict the HKAA. Patients and methods - We included 110 pairs of standard knee and full-limb radiographs. Automatic search algorithms found anatomic landmarks on standard knee radiographs. Based on these landmarks, the FTA was automatically calculated according to 9 different definitions (6 described in the literature and 3 newly developed). Pearson and intra-class correlation coefficient [ICC]) were determined between the FTA and HKAA as measured on full-limb radiographs. Subsequently, the top 4 FTA definitions were used to predict the HKAA in a 5-fold cross-validation setting. Results - Across all pairs of images, the Pearson correlations between FTA and HKAA ranged between 0.83 and 0.90. The ICC values from 0.83 to 0.90. In the cross-validation experiments to predict the HKAA, these values decreased only minimally. The mean absolute error for the best method to predict the HKAA from standard knee radiographs was 1.8° (SD 1.3). Interpretation - We showed that the HKAA can be automatically predicted from standard knee radiographs with fair accuracy and high correlation compared with the true HKAA. Therefore, this method enables research of the relationship between malalignment and knee pathology in large (epidemiological) studies lacking full-limb radiography.
Collapse
Affiliation(s)
- Willem Paul Gielis
- Department of Orthopedic Surgery, UMC Utrecht, Utrecht, The Netherlands,Correspondence:
| | - Hassan Rayegan
- Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Vahid Arbabi
- Department of Orthopedic Surgery, UMC Utrecht, Utrecht, The Netherlands,Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran,Department of Biomechanical Engineering, Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology (TU Delft), Delft, The Netherlands
| | - Seyed Y Ahmadi Brooghani
- Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Claudia Lindner
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK
| | - Tim F Cootes
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK
| | - Pim A de Jong
- Department of Radiology, UMC Utrecht and Utrecht University, Utrecht, The Netherlands
| | - H Weinans
- Department of Orthopedic Surgery, UMC Utrecht, Utrecht, The Netherlands
| | - Roel J H Custers
- Department of Orthopedic Surgery, UMC Utrecht, Utrecht, The Netherlands
| |
Collapse
|
39
|
Wang Q, Runhaar J, Kloppenburg M, Boers M, Bijlsma JWJ, Bierma-Zeinstra SMA. The Added Value of Radiographs in Diagnosing Knee Osteoarthritis Is Similar for General Practitioners and Secondary Care Physicians; Data from the CHECK Early Osteoarthritis Cohort. J Clin Med 2020; 9:jcm9103374. [PMID: 33096821 PMCID: PMC7594082 DOI: 10.3390/jcm9103374] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/08/2020] [Accepted: 10/19/2020] [Indexed: 01/18/2023] Open
Abstract
Objective: The purpose of this study was to evaluate the added value of radiographs for diagnosing knee osteoarthritis (KOA) by general practitioners (GPs) and secondary care physicians (SPs). Methods: Seventeen GPs and nineteen SPs were recruited to evaluate 1185 knees from the CHECK cohort (presenters with knee pain in primary care) for the presence of clinically relevant osteoarthritis (OA) during follow-up. Experts were required to make diagnoses independently, first based on clinical data only and then on clinical plus radiographic data, and to provide certainty scores (ranging from 1 to 100, where 1 was “certainly no OA” and 100 was “certainly OA”). Next, experts held consensus meetings to agree on the final diagnosis. With the final diagnosis as gold standard, diagnostic indicators were calculated (sensitivity, specificity, positive/negative predictive value, accuracy and positive/negative likelihood ratio) for all knees, as well as for clinically “certain” and “uncertain” knees, respectively. Student paired t-tests compared certainty scores. Results: Most diagnoses of GPs (86%) and SPs (82%) were “consistent” after assessment of radiographic data. Diagnostic indicators improved similarly for GPs and SPs after evaluating the radiographic data, but only improved relevantly in clinically “uncertain” knees. Radiographs added some certainty to “consistent” OA knees (GP 69 vs. 72, p < 0.001; SP 70 vs. 77, p < 0.001), but not to the consistent no OA knees (GP 21 vs. 22, p = 0.16; SP 20 vs. 21, p = 0.04). Conclusions: The added value of radiographs is similar for GP and SP, in terms of diagnostic accuracy and certainty. Radiographs appear to be redundant when clinicians are certain of their clinical diagnosis.
Collapse
Affiliation(s)
- Qiuke Wang
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (Q.W.); (S.M.A.B.-Z.)
| | - Jos Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (Q.W.); (S.M.A.B.-Z.)
- Correspondence: ; Tel.: +31(0)-10-7044192; Fax: +31(0)-10-7044766
| | - Margreet Kloppenburg
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands;
| | - Maarten Boers
- Department of Epidemiology & Data Science, Amsterdam Rheumatology & Immunology Center, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands;
| | - Johannes W. J. Bijlsma
- Department of Rheumatology and Clinical Immunology, University Medical Centre Utrecht, 3584 CX Utrecht, The Netherlands;
| | - Sita M. A. Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (Q.W.); (S.M.A.B.-Z.)
- Department of Orthopaedics, Erasmus MC University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
| | | |
Collapse
|
40
|
van Erp JHJ, Gielis WP, Arbabi V, de Gast A, Weinans H, Arbabi S, Öner FC, Castelein RM, Schlösser TPC. Unravelling the knee-hip-spine trilemma from the CHECK study. Bone Joint J 2020; 102-B:1261-1267. [PMID: 32862680 DOI: 10.1302/0301-620x.102b9.bjj-2019-1315.r2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AIMS The aetiologies of common degenerative spine, hip, and knee pathologies are still not completely understood. Mechanical theories have suggested that those diseases are related to sagittal pelvic morphology and spinopelvic-femoral dynamics. The link between the most widely used parameter for sagittal pelvic morphology, pelvic incidence (PI), and the onset of degenerative lumbar, hip, and knee pathologies has not been studied in a large-scale setting. METHODS A total of 421 patients from the Cohort Hip and Cohort Knee (CHECK) database, a population-based observational cohort, with hip and knee complaints < 6 months, aged between 45 and 65 years old, and with lateral lumbar, hip, and knee radiographs available, were included. Sagittal spinopelvic parameters and pathologies (spondylolisthesis and degenerative disc disease (DDD)) were measured at eight-year follow-up and characteristics of hip and knee osteoarthritis (OA) at baseline and eight-year follow-up. Epidemiology of the degenerative disorders and clinical outcome scores (hip and knee pain and Western Ontario and McMaster Universities Osteoarthritis Index) were compared between low PI (< 50°), normal PI (50° to 60°), and high PI (> 60°) using generalized estimating equations. RESULTS Demographic details were not different between the different PI groups. L4 to L5 and L5 to S1 spondylolisthesis were more frequently present in subjects with high PI compared to low PI (L4 to L5, OR 3.717; p = 0.024 vs L5 to S1 OR 7.751; p = 0.001). L5 to S1 DDD occurred more in patients with low PI compared to high PI (OR 1.889; p = 0.010), whereas there were no differences in L4 to L5 DDD among individuals with a different PI. The incidence of hip OA was higher in participants with low PI compared to normal (OR 1.262; p = 0.414) or high PI (OR 1.337; p = 0.274), but not statistically different. The incidence of knee OA was higher in individuals with a high PI compared to low PI (OR 1.620; p = 0.034). CONCLUSION High PI is a risk factor for development of spondylolisthesis and knee OA. Low pelvic incidence is related to DDD, and may be linked to OA of the hip. Level of Evidence: 1b Cite this article: Bone Joint J 2020;102-B(9):1261-1267.
Collapse
Affiliation(s)
- Joost H J van Erp
- Clinical Orthopedic Research Center Midden-Nederland, Zeist, Netherlands.,Department of Orthopedics, Diakonessenhuis Utrecht, Netherlands.,Department of Orthopedics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Willem P Gielis
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Vahid Arbabi
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, Netherlands.,Orthopaedic-BiMechanics Research Group, Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Arthur de Gast
- Clinical Orthopedic Research Center Midden-Nederland, Zeist, Netherlands.,Department of Orthopedics, Diakonessenhuis Utrecht, Netherlands
| | - Harrie Weinans
- Clinical Orthopedic Research Center Midden-Nederland, Zeist, Netherlands.,Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
| | - Saeed Arbabi
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - F Cumhur Öner
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, Netherlands
| | - René M Castelein
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tom P C Schlösser
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, Netherlands
| |
Collapse
|
41
|
van Helvoort EM, van Spil WE, Jansen MP, Welsing PMJ, Kloppenburg M, Loef M, Blanco FJ, Haugen IK, Berenbaum F, Bacardit J, Ladel CH, Loughlin J, Bay-Jensen AC, Mobasheri A, Larkin J, Boere J, Weinans HH, Lalande A, Marijnissen ACA, Lafeber FPJG. Cohort profile: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) study: a 2-year, European, cohort study to describe, validate and predict phenotypes of osteoarthritis using clinical, imaging and biochemical markers. BMJ Open 2020; 10:e035101. [PMID: 32723735 PMCID: PMC7389775 DOI: 10.1136/bmjopen-2019-035101] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
PURPOSE The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) consortium intends to prospectively describe in detail, preselected patients with knee osteoarthritis (OA), using conventional and novel clinical, imaging, and biochemical markers, to support OA drug development. PARTICIPANTS APPROACH is a prospective cohort study including 297 patients with tibiofemoral OA, according to the American College of Rheumatology classification criteria. Patients were (pre)selected from existing cohorts using machine learning models, developed on data from the CHECK cohort, to display a high likelihood of radiographic joint space width (JSW) loss and/or knee pain progression. FINDINGS TO DATE Selection appeared logistically feasible and baseline characteristics of the cohort demonstrated an OA population with more severe disease: age 66.5 (SD 7.1) vs 68.1 (7.7) years, min-JSW 2.5 (1.3) vs 2.1 (1.0) mm and Knee injury and Osteoarthritis Outcome Score pain 31.3 (19.7) vs 17.7 (14.6), except for age, all: p<0.001, for selected versus excluded patients, respectively. Based on the selection model, this cohort has a predicted higher chance of progression. FUTURE PLANS Patients will visit the hospital again at 6, 12 and 24 months for physical examination, pain and general health questionnaires, collection of blood and urine, MRI scans, radiographs of knees and hands, CT scan of the knee, low radiation whole-body CT, HandScan, motion analysis and performance-based tests.After two years, data will show whether those patients with the highest probabilities for progression experienced disease progression as compared to those wit lower probabilities (model validation) and whether phenotypes/endotypes can be identified and predicted to facilitate targeted drug therapy. TRIAL REGISTRATION NUMBER NCT03883568.
Collapse
Affiliation(s)
| | - Willem E van Spil
- Rheumatology and Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands
| | - Mylène P Jansen
- Rheumatology and Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands
| | - Paco M J Welsing
- Rheumatology and Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands
| | - Margreet Kloppenburg
- Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Rheumatology, Leiden Universitair Medisch Centrum, Leiden, The Netherlands
| | - Marieke Loef
- Rheumatology, Leiden Universitair Medisch Centrum, Leiden, The Netherlands
| | - Francisco J Blanco
- Servicio de Reumatologia, INIBIC-Hospital Universitario A Coruña, A Coruña, Spain
| | - Ida K Haugen
- Rheumatology, Diakonhjemmet Hospital, Oslo, Norway
| | | | - Jaume Bacardit
- School of Computing Science, Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
| | | | - John Loughlin
- Musculoskeletal Research Group, Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
| | | | - Ali Mobasheri
- Regenarative Medicine, State Research Institute Center of Innovative Medicine, Vilnius, Lithuania
| | | | | | - Harrie H Weinans
- Rheumatology and Clinical Immunology, UMC Utrecht, Utrecht, The Netherlands
- Orthopaedics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Agnes Lalande
- Institut de Recherches Internationales Servier, Suresnes, France
| | | | | |
Collapse
|
42
|
Johnsen MB, Magnusson K, Børte S, Gabrielsen ME, Winsvold BS, Skogholt AH, Thomas L, Storheim K, Hveem K, Zwart JA. Development and validation of a prediction model for incident hand osteoarthritis in the HUNT study. Osteoarthritis Cartilage 2020; 28:932-940. [PMID: 32360252 DOI: 10.1016/j.joca.2020.04.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 04/01/2020] [Accepted: 04/21/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To develop and externally validate prediction models for incident hand osteoarthritis (OA) in a large population-based cohort of middle aged and older men and women. DESIGN We included 17,153 men and 18,682 women from a population-based cohort, aged 35-70 years at baseline (1995-1997). Incident hand OA were obtained from diagnostic codes in the Norwegian National Patient Register (1995-2018). We studied whether a range of self-reported and clinically measured predictors could predict hand OA, using the Area Under the receiver-operating Curve (AUC) from logistic regression. External validation of an existing prediction model for male hand OA was tested on discrimination in a sample of men. Bootstrapping was used to avoid overfitting. RESULTS The model for men showed modest discriminatory ability (AUC = 0.67, 95% CI 0.62-0.71). Adding a genetic risk score did not improve prediction. Similar discrimination was observed in the model for women (AUC = 0.62, 95% CI 0.59-0.64). Prediction was not improved by adding a genetic risk score or hormonal and reproductive factors. Applying external validation, similar results were observed among men in HUNT (The Nord-Trøndelag Health Study) as in the developmental sample (AUC = 0.62, 95% CI 0.57-0.65). CONCLUSION We developed prediction models for incident hand OA in men and women. For women, the model included body mass index (BMI), heavy physical work, high physical activity and perceived poor health. The model showed moderate discrimination. For men, we have shown that a prediction model including BMI, education and information on sleep can predict incident hand OA in several populations with moderate discriminative ability.
Collapse
Affiliation(s)
- M B Johnsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway; K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - K Magnusson
- Lund University, Faculty of Medicine, Department of Clinical Sciences, Clinical Epidemiology Unit, Lund, Orthopaedics, Lund, Sweden; National Advisory Unit on Rehabilitation in Rheumatology, Department of Rheumatology, Diakonhjemmet Hospital, Oslo, Norway.
| | - S Børte
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway; K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - M E Gabrielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - B S Winsvold
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway; K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - A H Skogholt
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - L Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
| | - K Storheim
- Research and Communication Unit for Musculoskeletal Health, Oslo University Hospital, Oslo, Norway.
| | - K Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - J-A Zwart
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway; K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| |
Collapse
|
43
|
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data. Sci Rep 2020; 10:8427. [PMID: 32439879 PMCID: PMC7242357 DOI: 10.1038/s41598-020-64643-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 04/20/2020] [Indexed: 12/22/2022] Open
Abstract
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.
Collapse
|
44
|
Zarringam D, Saris DB, Bekkers JE. Identification of early prognostic factors for knee and hip arthroplasty; a long-term follow-up of the CHECK cohort. J Orthop 2020; 19:41-45. [PMID: 32021034 DOI: 10.1016/j.jor.2019.10.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Accepted: 10/30/2019] [Indexed: 01/14/2023] Open
|
45
|
van den Berg R, Jongbloed EM, Kuchuk NO, Koes BW, Oei EHG, Bierma-Zeinstra SMA, Luijsterburg PAJ. Association Between Self-Reported Spinal Morning Stiffness and Radiographic Evidence of Lumbar Disk Degeneration in Participants of the Cohort Hip and Cohort Knee (CHECK) Study. Phys Ther 2020; 100:255-267. [PMID: 31742363 DOI: 10.1093/ptj/pzz170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 04/24/2019] [Accepted: 06/28/2019] [Indexed: 02/09/2023]
Abstract
BACKGROUND Low back pain (LBP) is very common and is a main cause of limited activity and work absence. Patients with LBP may also report spinal morning stiffness; this symptom could be useful for identifying subgroups with signs and symptoms related to spinal osteoarthritis. OBJECTIVE This study investigated whether an association exists between reported spinal morning stiffness and radiographic evidence of lumbar disk degeneration (LDD) in people with LBP and a history of pain of the hip and/or knee. DESIGN This cross-sectional study used 8-year follow-up data from the Cohort Hip and Cohort Knee study. METHODS The association between spinal morning stiffness and radiographic LDD features was assessed with multivariable logistic regression models. RESULTS The presence of osteophytes was significantly associated with spinal morning stiffness (odds ratio [OR] = 2.1 [95% confidence interval [CI] = 1.3-3.2]) as was the presence of grade 2 or 3 disk space narrowing (OR = 2.0 [95% CI = 1.1-3.5]). There was also a significant association between morning stiffness persisting for > 30 minutes and grade 2 osteophytes (OR = 2.6 [95% CI = 1.1-6.2]) and grade 1 disk space narrowing (OR = 2.0 [95% CI = 1.1-3.6]). Furthermore, there was a significant association between moderate spinal morning stiffness and the presence of osteophytes (OR = 2.0 [95% CI = 1.2-3.2]). Both the presence of osteophytes and disk space narrowing were significantly associated with severe spinal morning stiffness (for osteophytes: OR = 2.0 [95% CI = 1.2-3.7]; for narrowing at L1-S1: OR = 1.8 [95% CI = 1.1-3.1]). LIMITATIONS Only lumbar lateral radiographs were available for each participant, implying that the LDD features could have been underestimated. The quality of the radiographs was not consistent. CONCLUSIONS This study showed an association between self-reported spinal morning stiffness and symptomatic LDD. When morning stiffness lasted > 30 minutes, there was a significant association with the features of LDD. The association was stronger when the severity of spinal morning stiffness increased.
Collapse
Affiliation(s)
- Roxanne van den Berg
- Department of General Practice, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
| | | | - Natalia O Kuchuk
- Department of Rheumatology and Clinical Immunology, University Medical Center, Utrecht, the Netherlands; and Department of Rheumatology, Tergooi Hospital, Hilversum, the Netherlands
| | - Bart W Koes
- Department of General Practice, Erasmus University Medical Center
| | - Edwin H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Center
| | - Sita M A Bierma-Zeinstra
- Department of General Practice, and Department of Orthopedics, Erasmus University Medical Center
| | - Pim A J Luijsterburg
- Department of General Practice, Erasmus University Medical Center.*S.M.A. Bierma-Zeinstra and P.A.J. Luijsterburg contributed equally to the work
| |
Collapse
|
46
|
Gielis WP, Weinans H, Welsing PMJ, van Spil WE, Agricola R, Cootes TF, de Jong PA, Lindner C. An automated workflow based on hip shape improves personalized risk prediction for hip osteoarthritis in the CHECK study. Osteoarthritis Cartilage 2020; 28:62-70. [PMID: 31604136 DOI: 10.1016/j.joca.2019.09.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 09/07/2019] [Accepted: 09/22/2019] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To design an automated workflow for hip radiographs focused on joint shape and tests its prognostic value for future hip osteoarthritis. DESIGN We used baseline and 8-year follow-up data from 1,002 participants of the CHECK-study. The primary outcome was definite radiographic hip osteoarthritis (rHOA) (Kellgren-Lawrence grade ≥2 or joint replacement) at 8-year follow-up. We designed a method to automatically segment the hip joint from radiographs. Subsequently, we applied machine learning algorithms (elastic net with automated parameter optimization) to provide the Shape-Score, a single value describing the risk for future rHOA based solely on joint shape. We built and internally validated prediction models using baseline demographics, physical examination, and radiologists scores and tested the added prognostic value of the Shape-Score using Area-Under-the-Curve (AUC). Missing data was imputed by multiple imputation by chained equations. Only hips with pain in the corresponding leg were included. RESULTS 84% were female, mean age was 56 (±5.1) years, mean BMI 26.3 (±4.2). Of 1,044 hips with pain at baseline and complete follow-up, 143 showed radiographic osteoarthritis and 42 were replaced. 91.5% of the hips had follow-up data available. The Shape-Score was a significant predictor of rHOA (odds ratio per decimal increase 5.21, 95%-CI (3.74-7.24)). The prediction model using demographics, physical examination, and radiologists scores demonstrated an AUC of 0.795, 95%-CI (0.757-0.834). After addition of the Shape-Score the AUC rose to 0.864, 95%-CI (0.833-0.895). CONCLUSIONS Our Shape-Score, automatically derived from radiographs using a novel machine learning workflow, may strongly improve risk prediction in hip osteoarthritis.
Collapse
Affiliation(s)
- W P Gielis
- UMC Utrecht, Department of Orthopedics and Department of Radiology, Utrecht, the Netherlands.
| | - H Weinans
- UMC Utrecht, Department of Orthopedics and Department of Rheumatology & Clinical Immunology, Utrecht, the Netherlands; TU Delft, Department of Biomechanical Engineering, Delft, the Netherlands.
| | - P M J Welsing
- UMC Utrecht, Department of Rheumatology & Clinical Immunology, Utrecht, the Netherlands.
| | - W E van Spil
- UMC Utrecht, Department of Rheumatology & Clinical Immunology, Utrecht, the Netherlands.
| | - R Agricola
- Erasmus MC, Department of Orthopedics, Rotterdam, the Netherlands.
| | - T F Cootes
- University of Manchester, Division of Informatics, Imaging & Data Sciences, Manchester, United Kingdom.
| | - P A de Jong
- UMC Utrecht, Department of Radiology, Utrecht, the Netherlands.
| | - C Lindner
- University of Manchester, Division of Informatics, Imaging & Data Sciences, Manchester, United Kingdom.
| |
Collapse
|
47
|
Schiphof D, Runhaar J, Waarsing JH, van Spil WE, van Middelkoop M, Bierma-Zeinstra SMA. The clinical and radiographic course of early knee and hip osteoarthritis over 10 years in CHECK (Cohort Hip and Cohort Knee). Osteoarthritis Cartilage 2019; 27:1491-1500. [PMID: 31202721 DOI: 10.1016/j.joca.2019.06.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 05/29/2019] [Accepted: 06/03/2019] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To describe the radiographic and symptomatic course in subjects with hip or knee complaints suspected of early osteoarthritis (OA). DESIGN CHECK (Cohort Hip and Cohort Knee) is a multicenter, prospective observational cohort study of 1,002 subjects with first complaints in knee(s) and/or hip(s) (age 56 ± 5 years; 79% female; body mass index (BMI) 26 ± 4 kg/m2). Visits took place at baseline and at 2, 5, 8, and 10 year follow-up. At each visit, questionnaires were administered, physical examination performed, and X-ray images obtained. Clinical OA was defined according to the clinical American College of Rheumatism (ACR) criteria. Radiographic OA (ROA) was defined as Kellgren and Lawrence score (K&L) ≥2. RESULTS 83% of the subjects reported knee pain, 59% hip pain, and 42% reported both hip and knee pain at baseline. 85% of the subjects completed 10-year follow-up. Pain scores remained rather stable over time, although individual scores fluctuated. A total of 138 subjects never fulfilled the clinical American College of Rheumatology (ACR) criteria. 60% (n = 601) had ROA in one or both knees, and 51% (n = 513) had ROA in one or both hips at 10 years. Only 13.5% of the subjects did not develop ROA after 10 years. Most joint replacements (n = 52 (57%)) took place in subjects with multiple affected joints. CONCLUSIONS The symptomatic course in subjects with hip or knee complaints suspected of OA remained fairly stable on population level, though individual scores fluctuated. The radiological course was progressive, with joint replacements particularly in subjects with both hip and knee OA.
Collapse
Affiliation(s)
- D Schiphof
- Department of General Practice, Erasmus University Medical Center, Rotterdam, the Netherlands.
| | - J Runhaar
- Department of General Practice, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - J H Waarsing
- Department of Orthopedics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - W E van Spil
- Department of Rheumatology & Clinical Immunology, UMC Utrecht, Utrecht, the Netherlands
| | - M van Middelkoop
- Department of General Practice, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - S M A Bierma-Zeinstra
- Department of General Practice, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Orthopedics, Erasmus University Medical Center, Rotterdam, the Netherlands.
| |
Collapse
|
48
|
Ho CM, Thorstensson CA, Nordeman L. Physiotherapist as primary assessor for patients with suspected knee osteoarthritis in primary care-a randomised controlled pragmatic study. BMC Musculoskelet Disord 2019; 20:329. [PMID: 31301739 PMCID: PMC6626628 DOI: 10.1186/s12891-019-2690-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 06/24/2019] [Indexed: 12/18/2022] Open
Abstract
Background In Swedish primary care, the healthcare process for patients with knee osteoarthritis (KOA) can be initiated by a physician or physiotherapist assessment. However, it is unclear how the different assessments affect the healthcare processes and patient reported outcomes over time. The purpose of this study was to examine the differences in health-related quality of life (HrQoL), adjusted for pain and physical function, for patients with KOA when the healthcare process is initiated by a physiotherapist assessment compared to a physician assessment in primary care. Methods An assessor-blinded randomised controlled pragmatic trial. Using a computer-generated list of random numbers, patients seeking primary care during 2013–2017 with suspected KOA were randomised to either a physiotherapist or physician for primary assessment and treatment. Data was collected before randomisation and at 3, 6, and 12-month follow-ups. Primary outcome was HrQoL using EuroQol 5 dimensions 3 levels questionnaire, index (EQ-5D-3L index) and a visual analogue scale (VAS) (EQ-5D-3L VAS); pain intensity was measured with VAS (0–100) and physical function measured with the 30-s chair stand test. Mixed effect model analyses compared repeated measures of HrQoL between groups. The significance level was p < 0.05 and data was applied with intention-to-treat. Results Patients were randomised to either a physiotherapist (n = 35) or physician (n = 34) for primary assessment. All 69 patients were included in the analyses. There were no significant differences in HrQoL for patients assessed by a physiotherapist or a physician as primary assessor (EQ-5D-3L index, p = 0.18; EQ-5D-3L VAS, p = 0.49). We found that HrQoL changed significantly 12 months after baseline assessment for all patients regardless of assessor (EQ-5D-3L index, p < 0.001; EQ-5D-3 L VAS, p = 0.0049). No adverse events or side effects were reported. Conclusions There were no differences in HrQoL, when adjusted for pain and physical function, for patients with KOA when the healthcare process was initiated with physiotherapist assessment compared to physician assessment in primary care. Both assessments resulted in significantly higher HrQoL at the 12-month follow-up. The results imply that physiotherapists and physicians in primary care are equally qualified as primary assessors. Trial registration Retrospectively registered at http://clinicaltrial.gov, ID: NCT03715764. Electronic supplementary material The online version of this article (10.1186/s12891-019-2690-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Chan-Mei Ho
- Region Västra Götaland, Närhälsan Health Unit, Primary Health Care, Lidköping, Sweden. .,Department of Health and Rehabilitation, Unit of Physiotherapy, University of Gothenburg, Sahlgrenska Academy, Institute of Neuroscience and Physiology, Gothenburg, Sweden.
| | - Carina A Thorstensson
- Department of Health and Rehabilitation, Unit of Physiotherapy, University of Gothenburg, Sahlgrenska Academy, Institute of Neuroscience and Physiology, Gothenburg, Sweden.,Department of Neurobiology, Care sciences and Society, Unit of Physiotherapy, Karolinska Institutet, Stockholm, Sweden
| | - Lena Nordeman
- Department of Health and Rehabilitation, Unit of Physiotherapy, University of Gothenburg, Sahlgrenska Academy, Institute of Neuroscience and Physiology, Gothenburg, Sweden.,Region Västra Götaland, Närhälsan, Research and Development Primary Health Care, Research and Development Center Södra Älvsborg, Borås, Sweden
| |
Collapse
|
49
|
Hirvasniemi J, Gielis WP, Arbabi S, Agricola R, van Spil WE, Arbabi V, Weinans H. Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study. Osteoarthritis Cartilage 2019; 27:906-914. [PMID: 30825609 DOI: 10.1016/j.joca.2019.02.796] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 01/27/2019] [Accepted: 02/10/2019] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To assess the ability of radiography-based bone texture variables in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. DESIGN Pelvic radiographs from CHECK at baseline (987 hips) were analyzed for bone texture using fractal signature analysis (FSA) in proximal femur and acetabulum. Elastic net (machine learning) was used to predict the incidence of rHOA (including Kellgren-Lawrence grade (KL) ≥ 2 or total hip replacement (THR)), joint space narrowing score (JSN, range 0-3), and osteophyte score (OST, range 0-3) after 10 years. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC). RESULTS Of the 987 hips without rHOA at baseline, 435 (44%) had rHOA at 10-year follow-up. Of the 667 hips with JSN grade 0 at baseline, 471 (71%) had JSN grade ≥ 1 at 10-year follow-up. Of the 613 hips with OST grade 0 at baseline, 526 (86%) had OST grade ≥ 1 at 10-year follow-up. AUCs for the models including age, gender, and body mass index (BMI) to predict incident rHOA, JSN, and OST were 0.59, 0.54, and 0.51, respectively. The inclusion of bone texture variables in the models improved the prediction of incident rHOA (ROC AUC 0.68 and 0.71 when baseline KL was also included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROC AUC 0.52). CONCLUSION Bone texture analysis provides additional information for predicting incident rHOA or THR over 10 years.
Collapse
Affiliation(s)
- J Hirvasniemi
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland; Department of Orthopedics, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - W P Gielis
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - S Arbabi
- Department of Computer Engineering, Faculty of Engineering, University of Zabol, Zabol, Iran.
| | - R Agricola
- Department of Orthopaedics, Erasmus University Medical Center, Rotterdam, the Netherlands.
| | - W E van Spil
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - V Arbabi
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Biomechanical Engineering, Delft University of Technology, Delft, the Netherlands; Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
| | - H Weinans
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Biomechanical Engineering, Delft University of Technology, Delft, the Netherlands.
| |
Collapse
|
50
|
Differential item functioning of the PROMIS physical function, pain interference, and pain behavior item banks across patients with different musculoskeletal disorders and persons from the general population. Qual Life Res 2019; 28:1231-1243. [PMID: 30600494 DOI: 10.1007/s11136-018-2087-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/10/2018] [Indexed: 01/09/2023]
Abstract
PURPOSE To investigate the validity of comparisons across patients with different musculoskeletal disorders and persons from the general population by evaluating differential item functioning (DIF) for the PROMIS physical function (PROMIS-PF), pain interference (PROMIS-PI), and pain behavior (PROMIS-PB) item banks. METHODS Patients with chronic pain, rheumatoid arthritis (RA), or osteoarthritis (OA); patients receiving physiotherapy (PT); and persons from the Dutch general population completed the full Dutch-Flemish PROMIS-PF (121-items), PROMIS-PI (40-items), or PROMIS-PB (39-items) banks. DIF was assessed with ordinal logistic regression models and McFadden's pseudo R2-change of ≥ 2% as critical value. The impact of DIF on item scores and the T-scores per bank was examined by inspecting item characteristic curves (ICCs) and test characteristic curves (TCCs). RESULTS 2762 patients with chronic pain, 2029 with RA, 1247 with OA, 805 receiving PT, and 1310 healthy persons participated. For the PROMIS-PF, 25 out of 121 items were flagged for DIF, of which 10 items were flagged in multiple comparisons. For the PROMIS-PI, only 2 out of 40 items were flagged for DIF and for the PROMIS-PB, only 3 out of 39 items. Most DIF items had R2 values just above the critical value of 2% and all showed uniform DIF. The ICCs and TCCs showed that the magnitude and impact of DIF on the item and T-scores were negligible. CONCLUSIONS This study supports the universal applicability of PROMIS across (patient) populations. Comparisons across patients with different musculoskeletal disorders and persons from the general population are valid, when applying the PROMIS-PF, PROMIS-PI, and PROMIS-PB banks.
Collapse
|