Related Experiment Video
Updated: Aug 16, 2025
A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
Published on: November 6, 2015
AMPREDICT PROsthetics-Predicting Prosthesis Mobility to Aid in Prosthetic Prescription and Rehabilitation Planning
Daniel C Norvell1, Mary Lou Thompson2, Aaron Baraff3
1VA Puget Sound Health Care System, Seattle, WA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA; VA Center for Limb Loss and Mobility (CLiMB), Seattle, WA.
Objective:
To develop and validate a patient-specific multivariable prediction model that uses variables readily available in the electronic medical record to predict 12-month mobility at the time of initial post-amputation prosthetic prescription. The prediction model is designed for patients who have undergone their initial transtibial (TT) or transfemoral (TF) amputation because of complications of diabetes and/or peripheral artery disease.
Design:
Multi-methodology cohort study that identified patients retrospectively through a large Veteran's Affairs (VA) dataset then prospectively collected their patient-reported mobility.
Setting:
The VA Corporate Data Warehouse, the National Prosthetics Patient Database, participant mailings, and phone calls.
Participants:
Three-hundred fifty-seven veterans who underwent an incident dysvascular TT or TF amputation and received a qualifying lower limb prosthesis between March 1, 2018, and November 30, 2020 (N=357).
Interventions:
Not applicable.
Main Outcome Measure:
The Amputee Single Item Mobility Measure (AMPSIMM) was divided into a 4-category outcome to predict wheelchair mobility (0-2), and household (3), basic community (4), or advanced community ambulation (5-6).
Results:
Multinomial logistic lasso regression, a machine learning methodology designed to select variables that most contribute to prediction while controlling for overfitting, led to a final model including 23 predictors of the 4-category AMPSIMM outcome that effectively discriminates household ambulation from basic community ambulation and from advanced community ambulation-levels of key clinical importance when estimating future prosthetic demands. The overall model performance was modest as it did not discriminate wheelchair from household mobility as effectively.
Conclusions:
The AMPREDICT PROsthetics model can assist providers in estimating individual patients' future mobility at the time of prosthetic prescription, thereby aiding in the formulation of appropriate mobility goals, as well as facilitating the prescription of a prosthetic device that is most appropriate for anticipated functional goals.
