Ignite Creation Date:
2025-12-24 @ 2:28 PM
Ignite Modification Date:
2026-01-11 @ 3:01 AM
Study NCT ID:
NCT06351059
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-04-19
First Post:
2023-10-25
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Acoustic Emission Biomarkers for the Detection and Monitoring of Early Knee Osteoarthritis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D020370', 'term': 'Osteoarthritis, Knee'}, {'id': 'D004194', 'term': 'Disease'}], 'ancestors': [{'id': 'D010003', 'term': 'Osteoarthritis'}, {'id': 'D001168', 'term': 'Arthritis'}, {'id': 'D007592', 'term': 'Joint Diseases'}, {'id': 'D009140', 'term': 'Musculoskeletal Diseases'}, {'id': 'D012216', 'term': 'Rheumatic Diseases'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D008279', 'term': 'Magnetic Resonance Imaging'}], 'ancestors': [{'id': 'D014054', 'term': 'Tomography'}, {'id': 'D003952', 'term': 'Diagnostic Imaging'}, {'id': 'D019937', 'term': 'Diagnostic Techniques and Procedures'}, {'id': 'D003933', 'term': 'Diagnosis'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 120}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2023-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-04', 'completionDateStruct': {'date': '2024-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-04-17', 'studyFirstSubmitDate': '2023-10-25', 'studyFirstSubmitQcDate': '2024-04-03', 'lastUpdatePostDateStruct': {'date': '2024-04-19', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-04-08', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-07-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'acoustic emissions', 'timeFrame': '1 year and 2 years', 'description': 'Acoustic sensors are embedded in the hardware of the inmodi knee brace, allowing the non-invasively collection of acoustic data at frequencies between 100Hz and 20 kHz. During data analysis, signals will be segmented and frequency components of low interest will be removed. Specific sound features, such as spectral, fractality, peak amplitude or click patters will be extracted.\n\nData of both knees will be assessed at the same time with the inmodi knee brace (v2) during four different tests, based on recommendations by the Osteoarthritis Research Society International (OARSI) for people with knee OA: - unloaded flexion-extension (F/E) of the knee in a seated position, - Sit-to-Stand test (STS), - One-step test (OST), - Walk test.\n\nAssociations between the extracted sound parameters and kinematic data, MOAKS grading, KL grading, leg alignment angles and PROMS will be studied to identify biomarkers with a prognosis value in the prediction of OA progress.'}, {'measure': 'kinematic data', 'timeFrame': '1 year and 2 years', 'description': 'Inertial sensors, also embedded in the hardware, do collect movement trajectories at the same time during the four different tests. This kinematic data will be combined with the acoustic emissions to analyze which sound features appear at specific ranges of motion (knee flexion/extension).'}, {'measure': 'MRI / MOAKS', 'timeFrame': '1 year and 2 years', 'description': 'Patients will be scanned using a 3T MRI scanner (Magnetom Prisma, Siemens). MRI allow to visualize and assess structural changes in joint tissues, such as cartilage, meniscus and subchondral bone. Semi-quantitative scoring of these degenerative structures in OA will be done using the MRI Osteoarthritis Knee Score (MOAKS) system by a radiologist.'}, {'measure': 'EOS / leg alignment', 'timeFrame': '1 year and 2 years', 'description': 'Bilateral long-leg radiographic images will be acquired in a standing position as stereoradiography using the EOS Edge (EOS X-Ray Imaging Acquisition System, Paris, France). Images will be analyzed for OA grading (Kellgren-Lawrence grade 0 - 4) and leg alignment parameters (hip-knee-ankle angle, mechanical axis length and deviation, proximal tibia width and compartmental joint space width).'}, {'measure': 'PROMS', 'timeFrame': '1 year and 2 years', 'description': "Five different patient reported outcome measures are used:\n\n* Oxford Knee Score: contains 12 questions about individual's activities of daily living and how they have been affected by pain over the last 4 weeks.\n* Core Outcome Measures Index Knee: assesses the main outcomes of importance to patients with knee problems (pain, function, symptom-specific well-being, quality of life, disability).\n* University of California at Los Angeles: an activity rating scale, which rates the activity level of joint replacement patients globally in one out of 10.\n* Knee injury and osteoarthritis outcome score: an instrument to assess the patient's opinion about their knee and associated problems.\n* Forgotten Joint Score 12 is a concise 12-item PROM that evaluates patients' ability to forget their joint in daily life."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Acoustic emission', 'Knee Osteoarthritis'], 'conditions': ['Osteoarthritis, Knee', 'Diagnosis']}, 'referencesModule': {'references': [{'pmid': '34023527', 'type': 'BACKGROUND', 'citation': 'Leifer VP, Katz JN, Losina E. The burden of OA-health services and economics. Osteoarthritis Cartilage. 2022 Jan;30(1):10-16. doi: 10.1016/j.joca.2021.05.007. Epub 2021 May 20.'}, {'pmid': '13498604', 'type': 'BACKGROUND', 'citation': 'KELLGREN JH, LAWRENCE JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957 Dec;16(4):494-502. doi: 10.1136/ard.16.4.494. No abstract available.'}, {'pmid': '22046518', 'type': 'BACKGROUND', 'citation': 'Eckstein F, Wirth W. Quantitative cartilage imaging in knee osteoarthritis. Arthritis. 2011;2011:475684. doi: 10.1155/2011/475684. Epub 2010 Dec 8.'}, {'pmid': '31887390', 'type': 'BACKGROUND', 'citation': 'van Spil WE, Szilagyi IA. Osteoarthritis year in review 2019: biomarkers (biochemical markers). Osteoarthritis Cartilage. 2020 Mar;28(3):296-315. doi: 10.1016/j.joca.2019.11.007. Epub 2019 Dec 27.'}, {'pmid': '32807320', 'type': 'BACKGROUND', 'citation': 'Kalo K, Niederer D, Stief F, Wurzberger L, van Drongelen S, Meurer A, Vogt L. Validity of and recommendations for knee joint acoustic assessments during different movement conditions. J Biomech. 2020 Aug 26;109:109939. doi: 10.1016/j.jbiomech.2020.109939. Epub 2020 Jul 8.'}, {'pmid': '20413570', 'type': 'BACKGROUND', 'citation': 'Prior J, Mascaro B, Shark LK, Stockdale J, Selfe J, Bury R, Cole P, Goodacre JA. Analysis of high frequency acoustic emission signals as a new approach for assessing knee osteoarthritis. Ann Rheum Dis. 2010 May;69(5):929-30. doi: 10.1136/ard.2009.112599. No abstract available.'}, {'pmid': '31032989', 'type': 'BACKGROUND', 'citation': 'Shakya BR, Tiulpin A, Saarakkala S, Turunen S, Thevenot J. Detection of experimental cartilage damage with acoustic emissions technique: An in vitro equine study. Equine Vet J. 2020 Jan;52(1):152-157. doi: 10.1111/evj.13132. Epub 2019 Jun 6.'}, {'type': 'BACKGROUND', 'citation': 'Bahador N, Pfeifle J, Thevenot J, et al. Evaluating the Potential of Novel Biomarkers for Characterizing Deviation of Acoustic Dynamics from Self-similarity in Osteoarthritic Knees (Manuscript in preparation). Published online 2021.'}]}, 'descriptionModule': {'briefSummary': "The aim of this exploratory study is to further investigate the potential of acoustic emission biomarkers, assessed by the inmodi knee brace, to diagnose osteoarthritis (OA) at earlier stages. Therefore, 20 healthy participants and 100 patients with increased risk of knee OA will be recruited from the Schulthess Klinik in Zurich and examined twice with 9 ± 3 months' time interval. Anthropometric data, EOS radiographs and MR images of both knees, PROMs and acoustic emission data will be collected and evaluated. Artificial Intelligence algorithm will then be used to identify and validate the most promising acoustic emission biomarkers with a prognosis value in the prediction of knee osteoarthritis progress.", 'detailedDescription': 'Disease background Osteoarthritis (OA) is a highly prevalent and disabling condition that affects over 7% of people globally (528 million people). It is significantly limiting their mobility and independent lifestyle. OA is mainly described by loss of cartilage, structural changes in bone, and inflammation of the synovium and joint capsule. Common risk factors include aging, obesity, prior joint injury and overuse. OA takes several years to develop, before the patient sees the doctor when pain intensifies. In early stages patients are asymptomatic or only experience activity related pain. The pain becomes constant over time with intermittent intense pain episodes. In regions around the world, the average annual cost of OA for an individual is estimated between USD $700-$15,600 (2019). Consequently, OA has a large socioeconomic impact due to its high medical costs, early retirements, and high absenteeism from work.\n\nOA typically affects the hips, knees, hands, feet, and spine, with a high prevalence of polyarticular involvement. This study focuses on OA of the knee.\n\nCurrent standard of assessment The current gold-standard assessment of the severity of knee OA is based on plain radiography, using the Kellgren-Lawrence (KL) grading system, which was accepted as a standard by the World Health Organization in 1961. This system does not evaluate the primary affected tissue - cartilage - but only the overall aspect of the joint. It is therefore unable to detect early stages of OA and the diagnosis is often established at a late stage, with few treatment options besides knee arthroplasty.\n\nAlternative assessment methods Earlier diagnostic and improved patient stratification is required to deploy preventive OA treatments. Possible early diagnostic options include Magnetic Resonance Imaging (MRI) and biochemical markers. MRI can be used to assess changes in cartilage volume, thickness or even quality, however, widespread use of MRI is limited by its high cost, availability, and the absence of a validated international score. Many biochemical markers are currently under investigation, but so far they lack specificity to joint tissues and to particular joints, and sensitivity and specificity are still not high enough for widespread clinical use.\n\nRecently, there has been a growing interest in acoustic analysis as an alternative to conventional biomarkers, particularly for the knee joint where poorly lubricated moving joint surfaces generate abnormal sounds that acoustic analysis could reveal.\n\nRelevance of the project The inmodi knee brace, developed at the ETH Lausanne is a device designed for an in-motion knee health assessment. Its core technology combines acoustic, thermal, and kinematic sensors and an artificial intelligence (AI)-based data analysis to extract relevant biomarkers of joint function.\n\nPreliminary data suggests that this combined approach may enable a non-invasive early diagnosis of OA. In horses, a strong association was found between the joint condition and the power of acoustic emission (AE) signals analysed. Combination of data (acoustic data/motion data) contains likely more information about the knee functional health, but such data is very heavy (computationally) and an algorithmic approach like AI seems the best option to extract meaningful parameters and combine them into clinically useful biomarkers. Recently, the investigators tested a prototype of inmodi knee brace in seventeen patients from the clinic shortly before total knee arthroplasty (TKA). These patients suffered from severe OA on the leg undergoing surgery and mild to moderate on the contralateral one. A machine learning algorithm was able to discriminate the leg states with a specificity of 0.96 (perfect = 1).\n\nThe aim of this new project is to further explore the potential of the inmodi system to diagnose OA at earlier stages. For this the investigators will develop a preliminary reference database in a subpopulation at risk of developing OA. The identification and validation of novel biomarkers with an AI algorithm requires a training dataset and a test dataset, both with potential biomarkers and gold standard clinical outcomes for validation. This database will require a sample size large enough to be statistically sound to prevent statistical overfitting.\n\nNon-invasive knee health diagnostics should help identifying patients at risk at a relatively low cost and without radiation exposure and enable preventive measures to be implemented earlier than with standard radiographic measures.\n\nRisk category A (minimal): Imaging procedures contain (1) MRI of both knees, which is non-ionizing and (2) EOS full leg scan with much lower radiation dose of conventional radiography. Another procedure is the functional test with the inmodi knee brace of both legs. The probability that a biological effect on cells will occur from x-ray radiation is very low. Other risks are psychological, related to "screening overdiagnosis", or claustrophobic stress and noise discomfort in the MRI scanner. Further there is a risk on data privacy, which the investigators try to minimize by following the legal and internal data protection rules.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '35 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'This study targets a population at risk of OA. The risk of knee OA after knee joint injury is up to five-fold that of non-injured patients. As much as 50% of individuals with an anterior cruciate ligament (ACL) or meniscus tear develop knee OA. Therefore, to represent the whole distribution of OA stages in the study sample, participants will be recruited from the Meniscus/ACL-surgery (MAC) registry of the Schulthess Klinik at 2 years and 5 years post-operative times. Additionally, a healthy control group and a late-stage OA group will be included.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Group A: persons judging themselves to be of good subjective health without any knee problems, aged 35-65 years\n* Groups B\\&C: participant in MAC registry, completed patient reported-outcome measurements (PROM) questionnaires at 2y or 5y within the last 6 months, aged 35-65 years, unilateral knee surgery (another side as internal control), B1/C1: the normal group is defined as patients closely around the median COMI scores of their respective group (determined medians are: 2yr females: 0.9 / 2yr males: 0.7 / 5yr females: 0.5 / 5yr males: 0.5), B2/C2: the poor outcomes group consists of patients with scores ≥ 2.\n* Group D: patient booked for TKA due to severe OA, aged 35-75 years\n\nExclusion Criteria:\n\n* inability to give consent or follow procedures\n* no understanding of German language\n* open wounds or tissue injuries\n* irritated or infected sections on the limbs\n* Class II obesity defined by Body Mass Index (BMI) ≥ 35 kg/m2 (comorbidities associated with obesity should be investigated in future studies)\n* uncooperative patients who disregard or cannot follow instructions, including those who abuse drugs and/or alcohol\n* Pregnant or with intention to get pregnant (x-rays)\n* Current address outside of Switzerland\n* Groups B\\&C: revision surgeries at the operated knee, death, known pathologies or former injuries of the comparator knee'}, 'identificationModule': {'nctId': 'NCT06351059', 'briefTitle': 'Acoustic Emission Biomarkers for the Detection and Monitoring of Early Knee Osteoarthritis', 'organization': {'class': 'OTHER', 'fullName': 'Schulthess Klinik'}, 'officialTitle': 'Acoustic Emission Biomarkers for the Detection and Monitoring of Early Knee Osteoarthritis', 'orgStudyIdInfo': {'id': 'UE-0083'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'A: healthy participants', 'description': '20 healthy participants judging themselves to be of good subjective health without any knee problems', 'interventionNames': ['Diagnostic Test: InModi acoustic emission analysis']}, {'label': 'B1: 2 years normal', 'description': '20 patients from the knee registry at 2 years post-op with normal outcomes', 'interventionNames': ['Diagnostic Test: InModi acoustic emission analysis']}, {'label': 'B2: 2 years poor', 'description': '20 patients from the knee registry at 2 years post-op with poor outcomes', 'interventionNames': ['Diagnostic Test: InModi acoustic emission analysis']}, {'label': 'C1: 5 years normal', 'description': '20 patients from the knee registry at 5 years post-op with normal outcomes', 'interventionNames': ['Diagnostic Test: InModi acoustic emission analysis']}, {'label': 'C2: 5 years poor', 'description': '20 patients from the knee registry at 5 years post-op with poor outcomes', 'interventionNames': ['Diagnostic Test: InModi acoustic emission analysis']}, {'label': 'D: TKA patients', 'description': '20 patients booked for unilateral TKA surgery for severe OA', 'interventionNames': ['Diagnostic Test: InModi acoustic emission analysis']}], 'interventions': [{'name': 'InModi acoustic emission analysis', 'type': 'DIAGNOSTIC_TEST', 'otherNames': ['MRI / MOAKS', 'EOS / leg alignment', 'Functional tests', 'PROMs'], 'description': 'All diagnosis tests', 'armGroupLabels': ['A: healthy participants', 'B1: 2 years normal', 'B2: 2 years poor', 'C1: 5 years normal', 'C2: 5 years poor', 'D: TKA patients']}]}, 'contactsLocationsModule': {'locations': [{'zip': '8008', 'city': 'Zurich', 'state': 'Canton of Zurich', 'country': 'Switzerland', 'facility': 'Schulthess Klinik', 'geoPoint': {'lat': 47.36667, 'lon': 8.55}}], 'overallOfficials': [{'name': 'Vincent A Stadelmann, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Schulthess Klinik'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Schulthess Klinik', 'class': 'OTHER'}, 'collaborators': [{'name': 'Ecole Polytechnique Fédérale de Lausanne', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}