OsteoarthritisNCT06740851
Predictive Model for 1-Year Pain and Function Outcomes After Thumb Joint Surgery in Osteoarthritic Patients
An observational study of Osteoarthritis and Thumb Arthritis, run by Michael Oyewale at 1 site in Switzerland.
Trial summary
This study aims to develop a predictive model to help doctors better understand expected outcomes one year after thumb joint surgery for osteoarthritis. By analyzing clinical and patient-reported data from individuals who underwent surgery with the Touch® implant, the study seeks to predict pain levels and hand… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Osteoarthritis, Thumb Arthritis, Arthroplasty of the MCP Joint
- What is being tested?
- Touch® Trapeziometacarpal Joint Arthroplasty
- Type of treatment
- Injection or infusion, Surgery or procedure
- How many people
- 330 participants
- Where
- 1 siteSwitzerland
- Study dates
- 8 Feb 2022to 28 Feb 2025
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Study participants will be selected from a single-center prospective registry of patients treated at a specialized orthopedic hospital in Zurich, Switzerland. The population consists of individuals diagnosed with primary trapeziometacarpal joint (TMJ) osteoarthritis who underwent primary TMJ arthroplasty using the Touch® prosthesis. This population represents a mix of patients with varying severities of TMJ osteoarthritis who had failed conservative treatments such as splints, hand therapy, or corticosteroid injections before undergoing surgery.
Who may be able to join
- Patients who were treated with a primary TMJ arthroplasty using the Touch® prosthesis for TMJ OA in our single-center prospective registry.
- Patients who had completed their 1-year follow-up questionnaire
Who cannot join
- Revision surgery,
- Not treated for primary osteoarthritis (i.e., secondary osteoarthritis or rheumatoid arthritis or other),
- Did not sign the consent form
Trial locations and contacts
1 site in 1 country.
About this study
Official title: A Study Protocol for the Development of a Predictive Model for 1-Year Pain and Function Outcomes After Touch® Trapeziometacarpal Joint Arthroplasty
This study aims to develop a predictive model to help doctors better understand expected outcomes one year after thumb joint surgery for osteoarthritis. By analyzing clinical and patient-reported data from individuals who underwent surgery with the Touch® implant, the study seeks to predict pain levels and hand function 1-year after surgery. This information can support shared decision-making, set realistic expectations, and improve personalized treatment planning.
Read the detailed description
This study aims to develop and validate predictive models for assessing pain and hand function outcomes one year after trapeziometacarpal joint (TMJ) arthroplasty using the Touch® prosthesis. The study leverages a single-center prospective registry from a specialized orthopedic hospital in Zurich, Switzerland.
Analytical methods:
The investigators will use the following modeling approach to determine the best predictive value for the 1-year outcome of pain. The model chosen is:
- Extreme Gradient Boosting (XGBoost): A non-parametric, ensemble-based approach that combines decision trees through boosting to capture complex feature interactions and model non-linearity effectively. XGBoost also includes L1 and L2 regularization, allowing it to manage overfitting while providing strong predictive performance. XGBoost is known for its superior performance in capturing intricate relationships, providing robust predictions across various data contexts, as well as obtaining the first prize in many AI datathons.
Missing data:
The investigators anticipate missing data for patient-reported, clinical, radiological and supplementary variables. To address this, the investigators will
- Assess the extent and patterns of missingness for each variable using descriptive statistics and graphical methods.
- Investigate reasons for missing values and potential differences between individuals with and without incomplete data.
- Handle missing data using XGBoost's native capability to manage missing values directly by learning optimal splits for missing data, thereby eliminating the need for external imputation approaches for this model.
Model development:
Extreme Gradient Boosting (XGBoost):
- The data will be split into a training (70%) and testing (30%) set. The XGBoost model will be trained using hyperparameter tuning through grid search combined with repeated 5-fold cross-validation (repeated 5 times) on the training set. This repeated cross-validation serves as internal validation to ensure robust and unbiased estimation of model performance during training. The grid space search will explore a reasonable range of values for key hyperparameters, such as:
- Number of boosting rounds (nrounds): (e.g., 50, 100, 200)
- Learning rate (eta): (e.g., 0,05, 0.1, 0.3)
- Maximum depth of trees (max_depth): (e.g., 2, 4, 6)
- Minimum sum of instances in each node (min_child_weight): (e.g., 1, 2, 4)
- The grid space will be kept moderate in size to balance comprehensiveness with computational feasibility, ensuring a thorough exploration of important hyperparameters while avoiding an overly exhaustive search.
- In developing our model, the investigators will aim to enhance the performance and reduce overfitting by employing feature selection to address collinearity. The investigators will also investigate a minimal feature set using feature importance scores from an initial XGBoost model as well as expert knowledge to prioritize clinically relevant predictors. Lastly, the investigators will explore feature engineering (e.g., polynomial transformations) to enhance the predictive power of our model.
Model evaluation:
- Held-Out Test Set: After internal validation and hyperparameter tuning, the final model will be evaluated on the 30% held-out test set to assess its performance on unseen data, providing an unbiased estimate of generalizability.
- Performance Metrics:
- R² (Coefficient of Determination): To evaluate the proportion of variance explained by the model.
- Mean Absolute Error (MAE): To quantify the average magnitude of prediction errors and assess model accuracy for continuous outcomes, offering a clinician-friendly interpretation of error.
- Calibration and Validation Plots: To assess the agreement between observed and predicted outcomes, evaluating how well the predicted values align with the true values in the study population.
- Learning Curve: Learning curves will be employed to evaluate the model performance across different training set sizes. By plotting training and validation errors against the number of training sample sizes (subsets of whole dataset), the investigators can assess the model fit, observe the learning behavior, and determine whether our model performance would benefit from additional training data.
- Prediction Uncertainty: The investigators will use bootstrap aggregation (bagging), following methods from Hastie et al., to obtain prediction intervals, which quantify uncertainty in individual XGBoost predictions by analyzing the variance in prediction errors across bootstrap samples. Unlike confidence intervals, these intervals account for both model misspecification and outcome uncertainty.
Model output:
The investigators will use the final model to develop a web-based outcome calculator for pain and function 1-year after surgery. This tool is intended primarily for use by the clinicians, aiming to facilitate shared decision-making with the patients. By providing clear visualizations and easy-to-understand classifications, the calculator will help clinicians explain potential outcomes to patients and support patient engagement in their treatment planning. The model output will include either the predicted pain score on a 0-10 Numeric Rating Scale (NRS) or the bMHQ hand function score, which ranges from 0-100.
Based on the outcome, the exact predicted values will be visually highlighted followingly:
- Green: NRS 0-3, bMHQ 67-100 (indicative of positive outcomes)
- Orange: NRS 4-7, bMHQ 34-66 (indicative of moderate outcomes)
- Red: NRS 8-10, bMHQ 0-33 (indicative of poor outcomes) This classification aims to provide a quick, visual representation of the predicted outcomes, making it easier for clinicians and patients to interpret the results and understand the level of risk or expected recovery potential. The classification thresholds may be subject to change based on further validation to ensure accuracy and clinical relevance.
- Being tested
- Touch® Trapeziometacarpal Joint Arthroplasty
- Keywords
- prediction, xgboost, thumb arthroplasty, osteoarthritis, pain, function
- Medical subject headings
- Osteoarthritis, Pain
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Touch® Trapeziometacarpal Joint ArthroplastyProcedure or surgery
Also called Primary Trapeziometacarpal Joint Replacement, Thumb Joint Replacement with Touch® Implant
The intervention involves primary trapeziometacarpal joint (TMJ) arthroplasty using the Touch® prosthesis, a dual-mobility implant designed for the treatment of osteoarthritis in the thumb.
| Study group | Role | What they receive |
|---|---|---|
| Touch® patients | Patients who were treated with a primary trapeziometacarpal joint (TMJ) arthroplasty using the Touch® prosthesis for TMJ OA in our single-center prospective registry.
Patients had usually received treatments with splints, hand therapy and 1-3 steroid injections before undergoing surgery. Specific indications for primary TMJ implant arthroplasty included good trapezial bone stock without cysts, a trapezial height of at least 8 mm, no clinically relevant scaphotrapezoidal OA and no severe instability of the metacarpophalangeal joint. Receives: Procedure: Touch® Trapeziometacarpal Joint Arthroplasty |
What is being measured
The primary outcome is the main question the study is designed to answer.
Pain During Activities at 1-Year Post-Surgery
Pain during activities will be measured using a Numeric Rating Scale (NRS) ranging from 0 (no pain) to 10 (maximum pain). Participants rate the level of pain experienced during typical daily activities.
Measured: 1 year after surgery
Hand Function at 1-Year Post-Surgery
Hand function will be assessed using the brief Michigan Hand Outcomes Questionnaire (bMHQ), which evaluates hand performance in various activities. Scores range from 0 to 100, with higher scores indicating better function.
Measured: 1 year after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Dec 2024
- Study start8 Feb 2022
- Primary completion28 Feb 2025 (estimated)
- Study completion28 Feb 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for OsteoarthritisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 330 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- Michael Oyewale
- Sponsor type
- University, hospital or other
- Collaborators
- Erasmus Medical Center, University of Rotterdam, The Netherlands
- Responsible party
- Sponsor-investigator, Michael Oyewale, Schulthess Klinik
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06740851
- Sponsor's protocol ID
- 2019-02096
- Other IDs
- 2019
- Status last verified
- Dec 2024
- First submitted
- 13 Dec 2024
- Last updated
- 20 Dec 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: We do not plan on sharing the raw data. The full analysis code will be shared.
Publications
Hamasaki T, Harris PG, Bureau NJ, Gaudreault N, Ziegler D, Choiniere M. Efficacy of Surgical Interventions for Trapeziometacarpal (Thumb Base) Osteoarthritis: A Systematic Review. J Hand Surg Glob Online. 2021 Mar 23;3(3):139-148. doi: 10.1016/j.jhsg.2021.02.003. eCollection 2021 May.
PubMed 35415551Vermeulen GM, Slijper H, Feitz R, Hovius SE, Moojen TM, Selles RW. Surgical management of primary thumb carpometacarpal osteoarthritis: a systematic review. J Hand Surg Am. 2011 Jan;36(1):157-69. doi: 10.1016/j.jhsa.2010.10.028.
PubMed 21193136Falkner F, Tumkaya AM, Thomas B, Panzram B, Bickert B, Harhaus L. Dual mobility prosthesis for trapeziometacarpal osteoarthritis: results from a prospective study of 55 prostheses. J Hand Surg Eur Vol. 2023 Jun;48(6):566-574. doi: 10.1177/17531934231156280. Epub 2023 Feb 28.
PubMed 36855785Herren DB, Marks M, Neumeister S, Schindele S. Low complication rate and high implant survival at 2 years after Touch(R) trapeziometacarpal joint arthroplasty. J Hand Surg Eur Vol. 2023 Oct;48(9):877-883. doi: 10.1177/17531934231179581. Epub 2023 Jun 13.
PubMed 37310049Bertozzi L, Valdes K, Vanti C, Negrini S, Pillastrini P, Villafane JH. Investigation of the effect of conservative interventions in thumb carpometacarpal osteoarthritis: systematic review and meta-analysis. Disabil Rehabil. 2015;37(22):2025-43. doi: 10.3109/09638288.2014.996299. Epub 2015 Jan 5.
PubMed 25559974Esteban Lopez LMJ, Hoogendam L, Vermeulen GM, Tsehaie J, Slijper HP, Selles RW, Wouters RM; The Hand-Wrist Study Group. Long-Term Outcomes of Nonsurgical Treatment of Thumb Carpometacarpal Osteoarthritis: A Cohort Study. J Bone Joint Surg Am. 2023 Dec 6;105(23):1837-1845. doi: 10.2106/JBJS.22.01116. Epub 2023 Oct 30.
PubMed 37903291Bijsterbosch J, Visser W, Kroon HM, Stamm T, Meulenbelt I, Huizinga TW, Kloppenburg M. Thumb base involvement in symptomatic hand osteoarthritis is associated with more pain and functional disability. Ann Rheum Dis. 2010 Mar;69(3):585-7. doi: 10.1136/ard.2009.104562. Epub 2010 Feb 2.
PubMed 20124359Gehrmann SV, Tang J, Li ZM, Goitz RJ, Windolf J, Kaufmann RA. Motion deficit of the thumb in CMC joint arthritis. J Hand Surg Am. 2010 Sep;35(9):1449-53. doi: 10.1016/j.jhsa.2010.05.026.
PubMed 20807622Bakri K, Moran SL. Thumb carpometacarpal arthritis. Plast Reconstr Surg. 2015 Feb;135(2):508-520. doi: 10.1097/PRS.0000000000000916.
PubMed 25626796van der Oest MJW, Duraku LS, Andrinopoulou ER, Wouters RM, Bierma-Zeinstra SMA, Selles RW, Zuidam JM. The prevalence of radiographic thumb base osteoarthritis: a meta-analysis. Osteoarthritis Cartilage. 2021 Jun;29(6):785-792. doi: 10.1016/j.joca.2021.03.004. Epub 2021 Mar 17.
PubMed 33744429Haugen IK, Englund M, Aliabadi P, Niu J, Clancy M, Kvien TK, Felson DT. Prevalence, incidence and progression of hand osteoarthritis in the general population: the Framingham Osteoarthritis Study. Ann Rheum Dis. 2011 Sep;70(9):1581-6. doi: 10.1136/ard.2011.150078. Epub 2011 May 27.
PubMed 21622766Kloppenburg M, van Beest S, Kroon FPB. Thumb base osteoarthritis: A hand osteoarthritis subset requiring a distinct approach. Best Pract Res Clin Rheumatol. 2017 Oct;31(5):649-660. doi: 10.1016/j.berh.2018.08.007. Epub 2018 Sep 26.
PubMed 30509411
Study at a glance
From the registry record, last updated by the sponsor on 20 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study Protocol for the Development of a Predictive Model for 1-Year Pain and Function Outcomes After Touch® Trapeziometacarpal Joint Arthroplasty
- ClinicalTrials.gov ID
- NCT06740851
- Acronym / sponsor ID
- 2019-02096
- Keywords
- prediction, xgboost, thumb arthroplasty, osteoarthritis, pain, function
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 330 participants (estimated)
- Lead sponsor
- Michael OyewaleUniversity, hospital or other
- Collaborators
- Erasmus Medical Center, University of Rotterdam, The Netherlands
- Study start
- 8 Feb 2022 (actual)
- Primary completion
- 28 Feb 2025 (estimated)
- Study completion
- 28 Feb 2025 (estimated)
- First posted
- 18 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to develop a predictive model to help doctors better understand expected outcomes one year after thumb joint surgery for osteoarthritis. By analyzing clinical and patient-reported data from individuals who underwent surgery with the Touch® implant, the study seeks to predict pain levels and hand function 1-year after surgery. This information can support shared decision-making, set realistic expectations, and improve personalized treatment planning.
Detailed description
This study aims to develop and validate predictive models for assessing pain and hand function outcomes one year after trapeziometacarpal joint (TMJ) arthroplasty using the Touch® prosthesis. The study leverages a single-center prospective registry from a specialized orthopedic hospital in Zurich, Switzerland.
Analytical methods:
The investigators will use the following modeling approach to determine the best predictive value for the 1-year outcome of pain. The model chosen is:
- Extreme Gradient Boosting (XGBoost): A non-parametric, ensemble-based approach that combines decision trees through boosting to capture complex feature interactions and model non-linearity effectively. XGBoost also includes L1 and L2 regularization, allowing it to manage overfitting while providing strong predictive performance. XGBoost is known for its superior performance in capturing intricate relationships, providing robust predictions across various data contexts, as well as obtaining the first prize in many AI datathons.
Missing data:
The investigators anticipate missing data for patient-reported, clinical, radiological and supplementary variables. To address this, the investigators will
- Assess the extent and patterns of missingness for each variable using descriptive statistics and graphical methods.
- Investigate reasons for missing values and potential differences between individuals with and without incomplete data.
- Handle missing data using XGBoost's native capability to manage missing values directly by learning optimal splits for missing data, thereby eliminating the need for external imputation approaches for this model.
Model development:
Extreme Gradient Boosting (XGBoost):
- The data will be split into a training (70%) and testing (30%) set. The XGBoost model will be trained using hyperparameter tuning through grid search combined with repeated 5-fold cross-validation (repeated 5 times) on the training set. This repeated cross-validation serves as internal validation to ensure robust and unbiased estimation of model performance during training. The grid space search will explore a reasonable range of values for key hyperparameters, such as:
- Number of boosting rounds (nrounds): (e.g., 50, 100, 200)
- Learning rate (eta): (e.g., 0,05, 0.1, 0.3)
- Maximum depth of trees (max_depth): (e.g., 2, 4, 6)
- Minimum sum of instances in each node (min_child_weight): (e.g., 1, 2, 4)
- The grid space will be kept moderate in size to balance comprehensiveness with computational feasibility, ensuring a thorough exploration of important hyperparameters while avoiding an overly exhaustive search.
- In developing our model, the investigators will aim to enhance the performance and reduce overfitting by employing feature selection to address collinearity. The investigators will also investigate a minimal feature set using feature importance scores from an initial XGBoost model as well as expert knowledge to prioritize clinically relevant predictors. Lastly, the investigators will explore feature engineering (e.g., polynomial transformations) to enhance the predictive power of our model.
Model evaluation:
- Held-Out Test Set: After internal validation and hyperparameter tuning, the final model will be evaluated on the 30% held-out test set to assess its performance on unseen data, providing an unbiased estimate of generalizability.
- Performance Metrics:
- R² (Coefficient of Determination): To evaluate the proportion of variance explained by the model.
- Mean Absolute Error (MAE): To quantify the average magnitude of prediction errors and assess model accuracy for continuous outcomes, offering a clinician-friendly interpretation of error.
- Calibration and Validation Plots: To assess the agreement between observed and predicted outcomes, evaluating how well the predicted values align with the true values in the study population.
- Learning Curve: Learning curves will be employed to evaluate the model performance across different training set sizes. By plotting training and validation errors against the number of training sample sizes (subsets of whole dataset), the investigators can assess the model fit, observe the learning behavior, and determine whether our model performance would benefit from additional training data.
- Prediction Uncertainty: The investigators will use bootstrap aggregation (bagging), following methods from Hastie et al., to obtain prediction intervals, which quantify uncertainty in individual XGBoost predictions by analyzing the variance in prediction errors across bootstrap samples. Unlike confidence intervals, these intervals account for both model misspecification and outcome uncertainty.
Model output:
The investigators will use the final model to develop a web-based outcome calculator for pain and function 1-year after surgery. This tool is intended primarily for use by the clinicians, aiming to facilitate shared decision-making with the patients. By providing clear visualizations and easy-to-understand classifications, the calculator will help clinicians explain potential outcomes to patients and support patient engagement in their treatment planning. The model output will include either the predicted pain score on a 0-10 Numeric Rating Scale (NRS) or the bMHQ hand function score, which ranges from 0-100.
Based on the outcome, the exact predicted values will be visually highlighted followingly:
- Green: NRS 0-3, bMHQ 67-100 (indicative of positive outcomes)
- Orange: NRS 4-7, bMHQ 34-66 (indicative of moderate outcomes)
- Red: NRS 8-10, bMHQ 0-33 (indicative of poor outcomes) This classification aims to provide a quick, visual representation of the predicted outcomes, making it easier for clinicians and patients to interpret the results and understand the level of risk or expected recovery potential. The classification thresholds may be subject to change based on further validation to ensure accuracy and clinical relevance.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Year
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Touch® patients | – | Procedure: Touch® Trapeziometacarpal Joint ArthroplastyPatients who were treated with a primary trapeziometacarpal joint (TMJ) arthroplasty using the Touch® prosthesis for TMJ OA in our single-center prospective registry. Patients had usually received treatments with splints, hand therapy and 1-3 steroid injections before undergoing surgery. Specific indications for primary TMJ implant arthroplasty included good trapezial bone stock without cysts, a trapezial height of at least 8 mm, no clinically relevant scaphotrapezoidal OA and no severe instability of the metacarpophalangeal joint. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Study participants will be selected from a single-center prospective registry of patients treated at a specialized orthopedic hospital in Zurich, Switzerland. The population consists of individuals diagnosed with primary trapeziometacarpal joint (TMJ) osteoarthritis who underwent primary TMJ arthroplasty using the Touch® prosthesis. This population represents a mix of patients with varying severities of TMJ osteoarthritis who had failed conservative treatments such as splints, hand therapy, or corticosteroid injections before undergoing surgery.
Key inclusion criteria (2 listed)
- Patients who were treated with a primary TMJ arthroplasty using the Touch® prosthesis for TMJ OA in our single-center prospective registry.
- Patients who had completed their 1-year follow-up questionnaire
Key exclusion criteria (3 listed)
- Revision surgery,
- Not treated for primary osteoarthritis (i.e., secondary osteoarthritis or rheumatoid arthritis or other),
- Did not sign the consent form
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Touch® Trapeziometacarpal Joint ArthroplastyAlso: Primary Trapeziometacarpal Joint Replacement, Thumb Joint Replacement with Touch® ImplantThe intervention involves primary trapeziometacarpal joint (TMJ) arthroplasty using the Touch® prosthesis, a dual-mobility implant designed for the treatment of osteoarthritis in the thumb. | Procedure or surgery | Touch® patients |
Primary outcome
Pain During Activities at 1-Year Post-Surgery
Time frame: 1 year after surgery
Pain during activities will be measured using a Numeric Rating Scale (NRS) ranging from 0 (no pain) to 10 (maximum pain). Participants rate the level of pain experienced during typical daily activities.
Hand Function at 1-Year Post-Surgery
Time frame: 1 year after surgery
Hand function will be assessed using the brief Michigan Hand Outcomes Questionnaire (bMHQ), which evaluates hand performance in various activities. Scores range from 0 to 100, with higher scores indicating better function.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Dec 2024.
- Background: PubMed 35415551Hamasaki T, Harris PG, Bureau NJ, Gaudreault N, Ziegler D, Choiniere M. Efficacy of Surgical Interventions for Trapeziometacarpal (Thumb Base) Osteoarthritis: A Systematic Review. J Hand Surg Glob Online. 2021 Mar 23;3(3):139-148. doi: 10.1016/j.jhsg.2021.02.003. eCollection 2021 May.
- Background: PubMed 21193136Vermeulen GM, Slijper H, Feitz R, Hovius SE, Moojen TM, Selles RW. Surgical management of primary thumb carpometacarpal osteoarthritis: a systematic review. J Hand Surg Am. 2011 Jan;36(1):157-69. doi: 10.1016/j.jhsa.2010.10.028.
- Background: PubMed 36855785Falkner F, Tumkaya AM, Thomas B, Panzram B, Bickert B, Harhaus L. Dual mobility prosthesis for trapeziometacarpal osteoarthritis: results from a prospective study of 55 prostheses. J Hand Surg Eur Vol. 2023 Jun;48(6):566-574. doi: 10.1177/17531934231156280. Epub 2023 Feb 28.
- Background: PubMed 37310049Herren DB, Marks M, Neumeister S, Schindele S. Low complication rate and high implant survival at 2 years after Touch(R) trapeziometacarpal joint arthroplasty. J Hand Surg Eur Vol. 2023 Oct;48(9):877-883. doi: 10.1177/17531934231179581. Epub 2023 Jun 13.
- Background: PubMed 25559974Bertozzi L, Valdes K, Vanti C, Negrini S, Pillastrini P, Villafane JH. Investigation of the effect of conservative interventions in thumb carpometacarpal osteoarthritis: systematic review and meta-analysis. Disabil Rehabil. 2015;37(22):2025-43. doi: 10.3109/09638288.2014.996299. Epub 2015 Jan 5.
- Background: PubMed 37903291Esteban Lopez LMJ, Hoogendam L, Vermeulen GM, Tsehaie J, Slijper HP, Selles RW, Wouters RM; The Hand-Wrist Study Group. Long-Term Outcomes of Nonsurgical Treatment of Thumb Carpometacarpal Osteoarthritis: A Cohort Study. J Bone Joint Surg Am. 2023 Dec 6;105(23):1837-1845. doi: 10.2106/JBJS.22.01116. Epub 2023 Oct 30.
- Background: PubMed 20124359Bijsterbosch J, Visser W, Kroon HM, Stamm T, Meulenbelt I, Huizinga TW, Kloppenburg M. Thumb base involvement in symptomatic hand osteoarthritis is associated with more pain and functional disability. Ann Rheum Dis. 2010 Mar;69(3):585-7. doi: 10.1136/ard.2009.104562. Epub 2010 Feb 2.
- Background: PubMed 20807622Gehrmann SV, Tang J, Li ZM, Goitz RJ, Windolf J, Kaufmann RA. Motion deficit of the thumb in CMC joint arthritis. J Hand Surg Am. 2010 Sep;35(9):1449-53. doi: 10.1016/j.jhsa.2010.05.026.
- Background: PubMed 25626796Bakri K, Moran SL. Thumb carpometacarpal arthritis. Plast Reconstr Surg. 2015 Feb;135(2):508-520. doi: 10.1097/PRS.0000000000000916.
- Background: PubMed 33744429van der Oest MJW, Duraku LS, Andrinopoulou ER, Wouters RM, Bierma-Zeinstra SMA, Selles RW, Zuidam JM. The prevalence of radiographic thumb base osteoarthritis: a meta-analysis. Osteoarthritis Cartilage. 2021 Jun;29(6):785-792. doi: 10.1016/j.joca.2021.03.004. Epub 2021 Mar 17.
- Background: PubMed 21622766Haugen IK, Englund M, Aliabadi P, Niu J, Clancy M, Kvien TK, Felson DT. Prevalence, incidence and progression of hand osteoarthritis in the general population: the Framingham Osteoarthritis Study. Ann Rheum Dis. 2011 Sep;70(9):1581-6. doi: 10.1136/ard.2011.150078. Epub 2011 May 27.
- Background: PubMed 30509411Kloppenburg M, van Beest S, Kroon FPB. Thumb base osteoarthritis: A hand osteoarthritis subset requiring a distinct approach. Best Pract Res Clin Rheumatol. 2017 Oct;31(5):649-660. doi: 10.1016/j.berh.2018.08.007. Epub 2018 Sep 26.
- Individual participant dataSharing planned: No. We do not plan on sharing the raw data. The full analysis code will be shared.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Osteoarthritis and Thumb Arthritis, run by Michael Oyewale at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 20 Dec 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Pain During Activities at 1-Year Post-Surgery (time frame: 1 year after surgery). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06740851, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Dec 2024.