Prediction of Postoperative Outcomes After TKA Using Instrumented Insoles and DNN

NCT07367789 | Recruiting

• 1 other identifier: GBIRB2025-324
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

This multicenter prospective observational study aims to evaluate whether preoperative clinical variables and wearable sensor-derived gait features can predict postoperative improvement after total knee arthroplasty (TKA). Participants will undergo standardized gait assessments using instrumented insoles and complete validated patient-reported outcome measures (PROMs). Prediction models including linear regression, random forest, and deep neural networks will be applied and their performance compared.

Conditions

Total Knee Arthroplasty; Knee Osteoarthritis

Keywords

Total knee arthroplasty; Prediction model; Deep neural network; Instrumented insole

Outcome Measures

Primary Outcomes (1)

• Western Ontario and McMaster Universities Arthritis Index (WOMAC) Improvement

  Improvement in patient-reported symptoms as measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). The WOMAC is a validated patient-reported outcome measure consisting of 24 items assessing pain (5 items), stiffness (2 items), and physical function (17 items). Each item is scored on a 5-point Likert scale (0-4), resulting in a total score ranging from 0 to 96, where higher scores indicate worse pain, stiffness, and functional limitation, and lower scores indicate better clinical status. Postoperative improvement is defined as a decrease of ≥15 points in the WOMAC total score compared with the preoperative baseline, corresponding to the minimal clinically important difference (MCID). Participants will be classified as "improved" or "not improved" based on this criterion (binary outcome).

  Preoperative baseline; postoperative 6 weeks, 3 months, 6 months, and 12 months

Secondary Outcomes (1)

• Timed Up and Go Test (TUGT) Improvement

  Preoperative baseline; postoperative 6 weeks, 3 months, 6 months, and 12 months

Study Arms (1)

TKA Patients

Participants scheduled for primary unilateral total knee arthroplasty at either Yongin Severance Hospital or Gachon University Gil Medical Center. No intervention is assigned; all data are collected as part of observational evaluation.

Other: Instrumented Insole Gait Assessment

Interventions

Instrumented Insole Gait Assessment OTHER

Participants undergo gait assessment using an instrumented insole system during the Timed Up and Go Test. The device is used solely for data collection and does not provide therapeutic intervention.

TKA Patients

Eligibility Criteria

• Age: 19 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adults with knee osteoarthritis undergoing primary total knee arthroplasty at two hospitals in South Korea.

You may qualify if:

• Age ≥ 19 years
• Diagnosed with knee osteoarthritis (Kellgren-Lawrence grade 1-4)
• Scheduled for primary Total Knee Arthroplasty (TKA) at either study site
• Able to walk independently on level ground (Functional Ambulation Categories, FAC ≥ 3)

You may not qualify if:

• Cancellation of scheduled TKA
• History of TKA on the same knee
• Neurological or musculoskeletal disorders affecting gait
• Pregnancy or possibility of pregnancy
• Any condition deemed inappropriate for study participation by the investigator

Sponsors & Collaborators

• Yonsei University: lead

Study Sites (1)

Yongin Severance Hospital

Yongin-si, Gyeonggi-do, 16995, South Korea

RECRUITING

Related Publications (21)

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  PMID: 33167444 BACKGROUND

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  PMID: 28471509 BACKGROUND

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  PMID: 33123659 BACKGROUND

• Batailler C, Lording T, De Massari D, Witvoet-Braam S, Bini S, Lustig S. Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis. Arthroplast Today. 2021 Apr 24;9:1-15. doi: 10.1016/j.artd.2021.03.013. eCollection 2021 Jun.

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• Lee DW, Han HS, Lee MC, Ro DH. Prediction of postoperative gait speed change after bilateral primary total knee arthroplasty in female patients using a machine learning algorithm. Orthop Traumatol Surg Res. 2024 Nov;110(7):103842. doi: 10.1016/j.otsr.2024.103842. Epub 2024 Feb 19.

  PMID: 38382881 BACKGROUND

MeSH Terms

Conditions

Osteoarthritis, Knee

Condition Hierarchy (Ancestors)

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

Study Officials

• Na Young Kim, MD, PhD

  Severance Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Na Young Kim, MD, PhD

CONTACT

+82 010 9127 4482; kny8452@yuhs.ac

Seung Ick Choi

CONTACT

+82 010 8821 5297; rehab1@yuhs.ac

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 9, 2025
• First Posted: January 26, 2026
• Study Start: June 1, 2021
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: January 26, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)