Tibial Plateau FractureNCT04983316
TPF Machine Learning Algorithms
An observational study of Tibial Plateau Fracture, run by Universitaire Ziekenhuizen KU Leuven at 1 site in Belgium.
Trial summary
To adopt a machine learning technique to decide whether operative or non-operative treatment will result in the best patient-outcome. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Tibial Plateau Fracture
- How many people
- 70 participants
- Where
- 1 siteBelgium
- Study dates
- 5 Oct 2020to 6 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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 18 years and older
- All sexes
- Adult, Older adult
Study population: Patients of UZ Leuven
Who may be able to join
- Age > 18 years
- Proximal tibia plateau fracture
- Patient is able to attend follow-up visits
Who cannot join
- Age < 18 years
- Bilateral fractures
- Neurologic disorders (ie paraplegia, CVA, dementia etc.)
- Not understanding Dutch or English
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Operative or Nonoperative Management of Tibial Plateau Fractures? Application of Machine Learning Algorithms to Assist in Treatment Decision
To adopt a machine learning technique to decide whether operative or non-operative treatment will result in the best patient-outcome.
Read the detailed description
The overall goal is to adopt a machine learning technique to decide whether operative or non-operative treatment will result in the best patient-outcome.
The primary objectives are to identify the most suitable machine learning algorithm to predict the best treatment for future patients. Whether conservative or operative treatment will lead to the best patient outcome, will be decided on the predicted KOOS score. Several input factors, such as treatment (conservative or operative), number of fracture fragments, location of the fracture, soft tissue involvement,…for each patient will be used as training data for the algorithm. Some of these input data will be derived from CT-scans. Therefore, the CT scans will be segmented in Mimics, for which UZ Leuven recently purchased licenses. The output variable of the training data will be the KOOS score of each patient. Based on the input and output variable, the algorithm will determine a relation between these. For future patients of which the input variable are known, the output variable (=KOOS score) will be predicted both in case of operative and conservative treatment. We hypothesize that the prediction will be improved by adding more input data over time.
To secondary objective is to identify clinical and radiological factors that help predicting the best treatment for future patients.
As an outlook, the machine learning technique could be implemented in the future in clinical practice and utilized as a patient-specific planning tool for tibial plateau fracture management by aiding the surgeon to select the best treatment for a given case. The collected data in this registry will be used to validate the machine learning model. Patients will not yet be treated based on the results of the developed model, the trauma surgeon is responsible to decide which treatment option is best for the patient.
- Conditions
- Tibial Plateau Fracture
- Medical subject headings
- Tibial Plateau Fractures
What is being measured
The primary outcome is the main question the study is designed to answer.
Machine learning algorithm
To identify the most suitable machine learning algorithm that predicts the best treatment for future patients. The prediction will be improved over time by additional input.
Measured: 1 year
Secondary outcomes (2)
Clinical factors
To identify clinical factors that help predicting the best treatment for future patients
Measured: 1 year
Radiological factors
To identify radiological factors that help predicting the best treatment for future patients
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jul 2021
- Study start5 Oct 2020
- Primary completion27 Jun 2023
- Study completion6 Dec 2024
Why it stopped: Not enough capacity for recruitment
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Tibial Plateau FractureStudies of the same condition, open and under way.
- More trials run by Universitaire Ziekenhuizen KU LeuvenThe sponsor's other registered studies.
- 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
- 70 participants (actual)
Sponsor and investigators
- Lead sponsor
- Universitaire Ziekenhuizen KU Leuven
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Harm Hoekstra, prof. dr., Universitaire Ziekenhuizen KU Leuven
- Investigators
- Harm Hoekstra, Prof. MD, Principal investigator, UZ Leuven
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04983316
- Sponsor's protocol ID
- S64352
- Status last verified
- Jul 2025
- First submitted
- 16 Jul 2021
- Last updated
- 29 Jul 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Operative or Nonoperative Management of Tibial Plateau Fractures? Application of Machine Learning Algorithms to Assist in Treatment Decision
- ClinicalTrials.gov ID
- NCT04983316
- Acronym / sponsor ID
- S64352
- Conditions
- Tibial Plateau Fracture
- Study type
- Observational
- Recruitment status
- TerminatedReason stopped: Not enough capacity for recruitment
- Enrolment
- 70 participants (actual)
- Lead sponsor
- Universitaire Ziekenhuizen KU LeuvenUniversity, hospital or other
- Study start
- 5 Oct 2020 (actual)
- Primary completion
- 27 Jun 2023 (actual)
- Study completion
- 6 Dec 2024 (actual)
- First posted
- 30 Jul 2021 (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.
To adopt a machine learning technique to decide whether operative or non-operative treatment will result in the best patient-outcome.
Detailed description
The overall goal is to adopt a machine learning technique to decide whether operative or non-operative treatment will result in the best patient-outcome.
The primary objectives are to identify the most suitable machine learning algorithm to predict the best treatment for future patients. Whether conservative or operative treatment will lead to the best patient outcome, will be decided on the predicted KOOS score. Several input factors, such as treatment (conservative or operative), number of fracture fragments, location of the fracture, soft tissue involvement,…for each patient will be used as training data for the algorithm. Some of these input data will be derived from CT-scans. Therefore, the CT scans will be segmented in Mimics, for which UZ Leuven recently purchased licenses. The output variable of the training data will be the KOOS score of each patient. Based on the input and output variable, the algorithm will determine a relation between these. For future patients of which the input variable are known, the output variable (=KOOS score) will be predicted both in case of operative and conservative treatment. We hypothesize that the prediction will be improved by adding more input data over time.
To secondary objective is to identify clinical and radiological factors that help predicting the best treatment for future patients.
As an outlook, the machine learning technique could be implemented in the future in clinical practice and utilized as a patient-specific planning tool for tibial plateau fracture management by aiding the surgeon to select the best treatment for a given case. The collected data in this registry will be used to validate the machine learning model. Patients will not yet be treated based on the results of the developed model, the trauma surgeon is responsible to decide which treatment option is best for the patient.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients of UZ Leuven
Key inclusion criteria (3 listed)
- Age > 18 years
- Proximal tibia plateau fracture
- Patient is able to attend follow-up visits
Key exclusion criteria (4 listed)
- Age < 18 years
- Bilateral fractures
- Neurologic disorders (ie paraplegia, CVA, dementia etc.)
- Not understanding Dutch or English
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Machine learning algorithm
Time frame: 1 year
To identify the most suitable machine learning algorithm that predicts the best treatment for future patients. The prediction will be improved over time by additional input.
2 secondary outcomes
Clinical factors
Time frame: 1 year
Radiological factors
Time frame: 1 year
Results and safety
The study was stopped early (reason given: Not enough capacity for recruitment). Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jul 2025.
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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.
Harm Hoekstra, Prof. MD principal investigator
UZ Leuven
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 Tibial Plateau Fracture, run by Universitaire Ziekenhuizen KU Leuven at 1 site in Belgium.
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 3 inclusion and 4 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: stopped early, as of the sponsor's last update on 29 Jul 2025. 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 Belgium. 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?
The study was stopped early (reason given: Not enough capacity for recruitment). Results are not available in the source record.
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?
Machine learning algorithm (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 29 Jul 2025.