A Longitudinal Cohort Study with Integrated Technologies to Inform Personalized Robotic Knee Arthroplasty

4 pages•Published: December 17, 2024

Janie Wilson, Annemarie Laudanski, Stephanie Civiero, Adam Dorrance, Nadim Ammoury, Glen Richardson, David Wilson, Jennifer Leighton and Michael Dunbar

Abstract

Many patients exhibit joint level biomechanics and mobility deficits after knee arthroplasty, which have been linked to patient dissatisfaction. Advancements in robotic-assisted surgery offer the potential for surgery personalization to address these deficits, yet the link between surgical planning and joint mechanics remains unclear. This research aims to comprehensively model the relationships among patient variability in joint mechanics, anatomy/morphology, physical activity, implant characteristics and post-operative outcomes to inform personalization strategies to address these deficits in joint mechanics. This is a five-year longitudinal patient cohort study with integrated data at several time points perioperatively including longitudinally during the pre-operative wait period, and post-operatively to one year. We combine patient-specific information from multiple domains including demographics and anthropometrics; patient-reported outcomes; three-dimensional gait kinematics through AI-driven markerless motion capture integrated into the clinic hallway; free-living physical activity (PA) and gait outcomes with inertial sensors; joint anatomy, morphology and OA feature modeling through custom CT image processing; and intraoperative robotic data. Longitudinal pre-operative outcomes have been collected for a subset (n=57) to date. There were no significant longitudinal changes in gait kinematics or objective PA outcomes on a population level pre-operatively. However, a subset of patients exhibited significant gait worsening, and worsening was significantly correlated with more advanced OA-related gait deficits at baseline. Unique free-living gait metrics were identified that moderately correlated with in-clinic gait kinematics.
PA outcomes were not significantly correlated to gait outcomes, and significantly worse PA outcomes (step count, %sedentary, %light and moderate to vigorous PA) were identified for female patients.

Keyphrases: computational modeling, gait kinematics, knee arthroplasty, morphology, robotics, wearable sensors

In: Joshua W Giles and Aziliz Guezou-Philippe (editors). Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 7, pages 232-235.

Links:	https://easychair.org/publications/paper/4bQg
	https://doi.org/10.29007/dmq1

BibTeX entry

@inproceedings{CAOS2024:Longitudinal_Cohort_Study_with,
  author    = {Janie Wilson and Annemarie Laudanski and Stephanie Civiero and Adam Dorrance and Nadim Ammoury and Glen Richardson and David Wilson and Jennifer Leighton and Michael Dunbar},
  title     = {A Longitudinal Cohort Study with Integrated Technologies to Inform Personalized Robotic Knee Arthroplasty},
  booktitle = {Proceedings of The 24th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Joshua W Giles and Aziliz Guezou-Philippe},
  series    = {EPiC Series in Health Sciences},
  volume    = {7},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/4bQg},
  doi       = {10.29007/dmq1},
  pages     = {232-235},
  year      = {2024}}

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