A clinical decision-making algorithm for the personalized prescription of microprocessor-controlled prosthetic knees: An evidence-based approach based on a randomized trial.

Abstract

Background: Current processes for identifying the best microprocessor-controlled prosthetic knee (MPK) for individuals with transfemoral amputations are subjective, nonscientific, and sometimes fail to consider unique patient needs. Inaccurate prescriptions may hinder a patient's ability to make a speedy rehab.

Objectives: We developed a clinical decision equation that outputs MPK recommendation scores for 3 commercially available MPKs (Power Knee, C-Leg 4.0, Rheo Knee) based on easily acquirable user evaluation data.

Study design: Participants wore each of the study MPKs at home for a 1-week acclimation period. On the experiment day, participants completed a set of functional tasks including a 10-m walk test, stair and ramp ambulation tasks, a 2-minute walk test, and a narrow beam walking test. Performance outcome measures were collected.

Methods: Microprocessor-controlled prosthetic knees were scored relatively to the best performing knee based on their performance in 5 areas of interest: agility, community ambulation, energy, stability, and gait quality. The relative importance of each of these areas was computed based on a quantitative prediction of a user's functional needs from features including age, body mass index (BMI), AMPnoPRO score, and likelihood of stairs/ramps. We describe the algorithm-suggested optimal patient profiles for each device.

Results: We developed an application that allows clinicians to obtain instant recommendations. Clinicians can further adjust the relative importance of each area of interest based on patient needs.

Conclusions: This algorithm represents a transparent, experimentally backed clinical decision-making aid with the potential to streamline the prosthesis fitting process. Future studies are required to evaluate the effectiveness of the algorithm.