# AMPREDICT PRO- Predicting Prosthetic Mobility and Matching Prosthetic Characteristics to Patient Functional Goals

> **NIH VA I01** · VA PUGET SOUND HEALTHCARE SYSTEM · 2020 · —

## Abstract

Recent research has highlighted the challenges faced by the clinical rehabilitation team in determining if a
dysvascular amputee patient is a prosthetic candidate, formulating the most appropriate rehabilitation plan
based upon their functional goals, and developing a prosthetic prescription that best matches their probable
mobility outcome. The current prescription paradigm involves the estimation of the patient's future mobility
outcomes using K-levels. The appropriate prosthetic componentry is then matched to the predicted K-level.
This approach is wrought with challenges and clinicians do not have a lot of confidence in the system. There is
no evidence to back the clinician's ability to use K-levels to predict future function. Current clinical practice
guidelines also do not offer adequate evidence or guidance to shape these decisions. The time of initial
prosthetic evaluation is a key time point in the amputee care continuum that can profoundly influence later
outcome. Lower extremity amputations are a major health care concern within the VA Health Care System.
The VA Amputee System of Care (ASoC), which is a specialized clinical program within Rehabilitation Care
Services, has been implemented with the primary goal of enhancing amputee care and improving patient
outcomes. This program is concerned with the most appropriate management of amputees to maximizing
function and quality of life, as well as reducing health care disparities between regions and improving
geographical access to health care resources. To improve the establishment of rehabilitation mobility goals,
the provision of the optimum prosthetic device to meet those goals, we must address one of the key perceived
needs of amputee rehabilitation providers. We propose to develop a prediction model (AMPREDICT-
PROsthetics) that will predict mobility outcome based upon key predictors including demographic, comorbidity,
health behavior, cognitive, mental health, access to care, reamputation, stump factors, rehab environment, and
prosthetic component sophistication available at the time of prosthetic prescription. It will enhance prosthetic
prescription and assist the rehabilitation team in setting realistic goals, as well as manage patient expectations.
Ultimately, it should reduce regional variation in care and enhance decision making to all VHA facilities that
perform this procedure. Although not a primary aim of the proposed research, the successful development of
the proposed prediction model may have additional downstream benefits for this population, reducing trial and
error, prosthetic component fitting, with its incumbent dollar costs and patient costs in terms of travel and
repeated health care visits. If successful, it could be implemented into VA/DoD Clinical Practice Guidelines and
implemented throughout the VA Amputation System of Care.

## Key facts

- **NIH application ID:** 10014657
- **Project number:** 5I01RX002919-03
- **Recipient organization:** VA PUGET SOUND HEALTHCARE SYSTEM
- **Principal Investigator:** Daniel C Norvell
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2018-10-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10014657

## Citation

> US National Institutes of Health, RePORTER application 10014657, AMPREDICT PRO- Predicting Prosthetic Mobility and Matching Prosthetic Characteristics to Patient Functional Goals (5I01RX002919-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10014657. Licensed CC0.

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