Using Early Gait Data From a Smart-Enabled Total Knee Arthroplasty to Identify Patient Function and Activity at 90 Days Postoperative
DOKPE
- Schwab, Joseph M ORCID University of Fribourg
- Raynor, Michael E Canary Medical, Carlsbad, California
- 2026
Published in:
- The Journal of Arthroplasty. - Elsevier BV. - 2026
Gait analysis
Kinematics
Postoperative recovery
Predictive modeling
Smart implant
Total knee arthroplasty
English
Background
A subset of total knee arthroplasty (TKA) patients experiences suboptimal recovery manifested by a decline in function, activity, or both. Smart implantable devices capable of continuous, adherence-independent kinematic monitoring offer an opportunity for early risk identification. This study aimed to determine whether early gait data from a smart implant could predict 90-day recovery outcomes reported as a three-class measure (green, yellow, and red).
Methods
Kinematic data were obtained from 4,281 devices implanted in TKA patients. Data from 2,809 smart implantable devices formed the training dataset, and 1,472 formed the validation dataset. There were seven daily gait parameters from postoperative days eight to 90 that were reduced to two composite scores, function (tibial range of motion, knee range of motion, and stride length) and activity (step count, walk speed, distance, and cadence), based on principal components analyses. “Starting” (days, 8 to 21) and “outcome” (days, 77 to 90) ellipses were calculated for each patient, and a three-class outcome measure was assigned based on the proportion of the 95% confidence ellipse area within predefined function/activity quadrants. Multivariate logistic regression models using starting ellipse parameters were tested to predict the three-class outcomes at 90 days.
Results
The distribution of outcomes was consistent across datasets (training: 42 green, 42 yellow, and 15% red; validation: 45 green, 40 yellow, and 16% red; P = 0.2). Using early gait data, the model achieved a sensitivity of 0.780, specificity of 0.612, positive predictive value of 0.712, negative predictive value of 0.692, and overall accuracy of 0.704 in the validation cohort. Sensitivity was highest in women under the age of 65 years (0.801).
Conclusions
Passively collected implant-based gait data within the first three postoperative weeks can reasonably predict 90-day recovery class after TKA. Early kinematic modeling may enable timely, targeted interventions to improve functional recovery and satisfaction.
A subset of total knee arthroplasty (TKA) patients experiences suboptimal recovery manifested by a decline in function, activity, or both. Smart implantable devices capable of continuous, adherence-independent kinematic monitoring offer an opportunity for early risk identification. This study aimed to determine whether early gait data from a smart implant could predict 90-day recovery outcomes reported as a three-class measure (green, yellow, and red).
Methods
Kinematic data were obtained from 4,281 devices implanted in TKA patients. Data from 2,809 smart implantable devices formed the training dataset, and 1,472 formed the validation dataset. There were seven daily gait parameters from postoperative days eight to 90 that were reduced to two composite scores, function (tibial range of motion, knee range of motion, and stride length) and activity (step count, walk speed, distance, and cadence), based on principal components analyses. “Starting” (days, 8 to 21) and “outcome” (days, 77 to 90) ellipses were calculated for each patient, and a three-class outcome measure was assigned based on the proportion of the 95% confidence ellipse area within predefined function/activity quadrants. Multivariate logistic regression models using starting ellipse parameters were tested to predict the three-class outcomes at 90 days.
Results
The distribution of outcomes was consistent across datasets (training: 42 green, 42 yellow, and 15% red; validation: 45 green, 40 yellow, and 16% red; P = 0.2). Using early gait data, the model achieved a sensitivity of 0.780, specificity of 0.612, positive predictive value of 0.712, negative predictive value of 0.692, and overall accuracy of 0.704 in the validation cohort. Sensitivity was highest in women under the age of 65 years (0.801).
Conclusions
Passively collected implant-based gait data within the first three postoperative weeks can reasonably predict 90-day recovery class after TKA. Early kinematic modeling may enable timely, targeted interventions to improve functional recovery and satisfaction.
- Faculty
- Faculté des sciences et de médecine
- Department
- Master en médecine
- Language
-
- English
- Classification
- Pathology, clinical medicine
- Other electronic version
- License
-
CC BY
- Open access status
- hybrid
- Identifiers
-
- DOI 10.1016/j.arth.2026.01.063
- ISSN 0883-5403
- Persistent URL
- https://folia.unifr.ch/unifr/documents/334903
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