AMPREDICT PROsthetics-Predicting Prosthesis Mobility to Aid in Prosthetic Prescription and Rehabilitation Planning

Abstract

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.

Keywords: Amputation; Lower extremity; Peripheral artery disease; Prosthesis; Rehabilitation.

Figure 1:

Strobe diagram depicting total numbers identified in the VA Corporate Data Warehouse, total numbers and reasons for exclusion, and final number enrolled. *All transtibial and transfemoral amputations due to PAD or diabetes identified through the corporate Data Warehouse during March 2018-September 2020. **Exclusions on left side of the strobe are in the order they were applied horizontally from left to right. Among the 4,930 excluded, the final exclusion was for those that did not receive a prescription (n= 1,172). leaving 1,690 with a prescription. ^aPatients with spinal cord injury codes that did not have a hemi or quadriplegia code †Did not meet any other exclusion criteria; however, did not receive a prosthetic prescription. ††Prior to their 12-month mobility outcome follow-up window **Those otherwise eligible who were past their follow-up window due to timing of identification or Covid-19 study delays. ***28 recruits fell out of contact window before questionnaires could be mailed.

Figure 2:

Discrimination plot of predictions for the final model. Distributions of predicted probabilities for each AMPSIMM category are stratified by the actually observed category. The red lines show the prevalence of each AMPSIMM category.