Predictive Model for 1-Year Pain and Function Outcomes After Thumb Joint Surgery in Osteoarthritic Patients

December 18, 2024 updated by: Michael Oyewale

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.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

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

  1. Assess the extent and patterns of missingness for each variable using descriptive statistics and graphical methods.
  2. Investigate reasons for missing values and potential differences between individuals with and without incomplete data.
  3. 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 Type

Observational

Enrollment (Estimated)

330

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Switzerland
      • Zürich, Switzerland, 8008
        • Schulthess Klinik

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

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.

Description

Inclusion Criteria:

  • 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

Exclusion Criteria:

  • Revision surgery,
  • Not treated for primary osteoarthritis (i.e., secondary osteoarthritis or rheumatoid arthritis or other),
  • Did not sign the consent form

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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.
Procedure: Touch® Trapeziometacarpal Joint Arthroplasty
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.
Other Names:
  • Primary Trapeziometacarpal Joint Replacement
  • Thumb Joint Replacement with Touch® Implant

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
1 year after surgery
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.
1 year after surgery

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Michael Oyewale

Collaborators

Erasmus Medical Center
University of Rotterdam, The Netherlands

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 8, 2022

Primary Completion (Estimated)

February 28, 2025

Study Completion (Estimated)

February 28, 2025

Study Registration Dates

First Submitted

December 13, 2024

First Submitted That Met QC Criteria

December 13, 2024

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

December 18, 2024

Last Verified

December 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2019-02096

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

We do not plan on sharing the raw data. The full analysis code will be shared.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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