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

Abstract

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. 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. 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. 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. 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. 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.