Personalised Modeling and Simulations for the Differential Diagnosis of Dynapenia: Study on Patients With Osteoarthritis

NCT05795348 | Completed

• 1 other identifier: ForceLoss II
• Study Type: interventional
• Target: 20
• Locations: 1 country
• Sites: 1

Brief Summary

The ForceLoss study aims to develop personalised modeling and simulation procedures to enable the differential diagnosis for the loss of muscle force, namely dynapenia. The primary causes of dynapenia can be identified in a diffuse or selective sarcopenia, a lack of activation (inhibition), or suboptimal motor control. Each of these causes requires different interventions, but a reliable differential diagnosis is currently impossible. While biomedical instruments and tools can provide valuable information, it is often left to the experience of the single clinican to integrate such information into a complete diagnostic picture. An accurate diagnosis for dynapenia is important for a number of pathologies, including neurological diseases, age-related frailty, diabetes, and orthopaedic conditions. The hypothesis is that the use of mechanistic, subject-specific models (digital twins) to simulate a maximal isometric knee extension task, informed by experimental measures may be employed to conduct a robust differential diagnosis for dynapenia. In this study, on patients candidate for knee arthroplasty, the investigators will expand (i) the experimental protocol previously developed and tested on healthy volunteers with a measure of involuntary muscle contraction (superimposed neuromuscular electrical stimulation, SNMES), a hand-grip test, measures of bio-impedance and clinical questionnaires, and (ii) the modeling and simulation framework to include one additional step (to check for muscle inhibition). Medical imaging, electromyography (EMG) and dynamometry data will be collected and combined to inform a digital twin of each participant. Biomechanical computer simulations of a Maximal Voluntary Isometric Contraction (MVIC) task will then be performed. Comparing the models' estimates to in vivo dynamometry measurements and EMG data, the investigators will test one by one the three possible causes of dynapenia, and, through a process of hypothesis falsification will exclude those that do not explain the observed loss of muscle force.

Conditions

Osteoarthritis, Knee

Keywords

Dynapenia; Osteoarthritic patients; In Silico Medicine; Digital Twins; Maximal Voluntary Isometric Contraction; Musculoskeletal Modeling; Dynamometry; Electromyography; Superimposed Neuromuscular Electrical Stimulation

Outcome Measures

Primary Outcomes (4)

• Muscle volume

  Full lower limb MRI data will be acquired with subjects in supine position. Individual muscle volumes (in cm3) will be segmented using commercial software and stored in anonymized form.

  at baseline (Day 0)

• MVIC Torque

  Dynamometry data will be acquired while participants perform a MVIC leg extension test. The maximum torque values (Nm) measured over three repetitions will be recorded. These correspond to the values observed in correspondence of the plateaux of force, developed over a sustained contraction.

  at baseline (Day 0)

• Muscle Inhibition level

  The difference between the maximal force exerted during the MVIC test (voluntary contraction) and that achieved when the muscles are electrically stimulated (involuntary contraction) will be computed.

  at baseline (Day 0)

• Co-contraction index (CCI)

  Experimental EMG data will be recorded from the major lower limb muscles involved in the knee extension, while participants perform a maximal voluntary isometric contraction on a dynamometer (i.e., MVIC test to quantify muscle strength). The co-contraction index, defined as the relative activation of agonist and antagonist muscles (for this task: quadriceps and hamstrings) in the act of kicking (MVIC test), will be computed according to Li et al (2020).

  at baseline (Day 0)

Study Arms (1)

Knee Osteoarthritic Patients

OTHER

Patients candidate for knee arthroplasty; Age: 65-80 years; Body Mass Index: 18.5-30 kg/m²; ASA Classification: 1 or 2; Diagnosis of primary osteoarthritis at the knee; Suspect sarcopenia.

Diagnostic Test: Personalised Musculoskeletal Modeling

Interventions

Personalised Musculoskeletal Modeling DIAGNOSTIC_TEST

Magnetic resonance images, electromyography and dynamometry data will be used to develop and inform personalised musculoskeletal models

Also known as: Clinical measures (BIA, Hand-grip test), Clinical questionnaires (WOMAC, KSS)

Knee Osteoarthritic Patients

Eligibility Criteria

• Age: 65 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)

You may qualify if:

• Diagnosis of Primary Arthrosis at the knee (according to the American College of Rheumatology criteria), subjects elected for tota knee arthroplasty
• Body Mass Index between 18.5 and 30 kg/m2
• Health status (according to the American Society of Anesthesiology classification) equal to 1 or 2
• Suspected systemic sarcopenia due to aging or localized sarcopenia due to disuse

You may not qualify if:

• Neurological, rheumatic or tumoral diseases
• Inguinal or abdominal hernia
• Diabetes
• Severe Hypertension (Level 3)
• Severe Cardio-pulmonary insufficiency
• Diagnosis of Osteonecrosis in the lower limb joints
• Pathologies or physical conditions incompatible with the use of magnetic resonance imaging and electrostimulation (i.e., active and passive implanted biomedical devices, epilepsy, severe venous insufficiency in the lower limbs)
• Previous interventions or traumas to the joints of the lower limb

Sponsors & Collaborators

• Istituto Ortopedico Rizzoli: lead

Study Sites (1)

IRCCS Istituto Ortopedico Rizzoli

Bologna, 40136, Italy

Location

Related Publications (7)

• Fernandez J, Zhang J, Heidlauf T, Sartori M, Besier T, Rohrle O, Lloyd D. Multiscale musculoskeletal modelling, data-model fusion and electromyography-informed modelling. Interface Focus. 2016 Apr 6;6(2):20150084. doi: 10.1098/rsfs.2015.0084.

  PMID: 27051510 BACKGROUND

• Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol. 2000 Oct;10(5):361-74. doi: 10.1016/s1050-6411(00)00027-4.

  PMID: 11018445 BACKGROUND

• Petterson SC, Barrance P, Buchanan T, Binder-Macleod S, Snyder-Mackler L. Mechanisms underlying quadriceps weakness in knee osteoarthritis. Med Sci Sports Exerc. 2008 Mar;40(3):422-7. doi: 10.1249/MSS.0b013e31815ef285.

  PMID: 18379202 BACKGROUND

• Rice DA, McNair PJ. Quadriceps arthrogenic muscle inhibition: neural mechanisms and treatment perspectives. Semin Arthritis Rheum. 2010 Dec;40(3):250-66. doi: 10.1016/j.semarthrit.2009.10.001. Epub 2009 Dec 2.

  PMID: 19954822 BACKGROUND

• Pons C, Borotikar B, Garetier M, Burdin V, Ben Salem D, Lempereur M, Brochard S. Quantifying skeletal muscle volume and shape in humans using MRI: A systematic review of validity and reliability. PLoS One. 2018 Nov 29;13(11):e0207847. doi: 10.1371/journal.pone.0207847. eCollection 2018.

  PMID: 30496308 BACKGROUND

• Manini TM, Clark BC. Dynapenia and aging: an update. J Gerontol A Biol Sci Med Sci. 2012 Jan;67(1):28-40. doi: 10.1093/gerona/glr010. Epub 2011 Mar 28.

  PMID: 21444359 BACKGROUND

• O'Brien TD, Reeves ND, Baltzopoulos V, Jones DA, Maganaris CN. The effects of agonist and antagonist muscle activation on the knee extension moment-angle relationship in adults and children. Eur J Appl Physiol. 2009 Aug;106(6):849-56. doi: 10.1007/s00421-009-1088-4. Epub 2009 May 27.

  PMID: 19471955 BACKGROUND

MeSH Terms

Conditions

Osteoarthritis, Knee

Condition Hierarchy (Ancestors)

Osteoarthritis; Arthritis; Joint Diseases; Musculoskeletal Diseases; Rheumatic Diseases

Study Officials

• Marco Viceconti, Professor

  IRCCS Istituto Ortopedico Rizzoli

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 21, 2023
• First Posted: April 3, 2023
• Study Start: March 28, 2023
• Primary Completion: May 27, 2024
• Study Completion: May 27, 2024
• Last Updated: May 29, 2024

Record last verified: 2024-05

Data Sharing

• IPD Sharing: Will share
• Time Frame: The (anonymized) dataset will be made available to the wider biomechanical community upon study completion
• Access Criteria: Not yet defined

Locations

IT (1)