PROClass: The Development and Validation of a Novel Prosthetic Component Sophistication Classification System

• Published in: Archives of rehabilitation research and clinical translation 2023-09, Vol.5 (3), p.100273-100273, Article 100273
• Main Authors: Norvell, Daniel C., Biggs, Wayne T., Bott, Jeffrey, Henderson, Alison W., Moore, Kathryn P., Czerniecki, Joseph M.
• Format: Article
• Language: English
• Subjects: Accuracy; Classification system; Lower limb amputation; Lower limb prosthesis; Original Research; Rehabilitation; Reliability
• ISSN: 2590-1095; 2590-1095
• DOI: 10.1016/j.arrct.2023.100273

To develop a lower limb prosthesis (LLP) sophistication classification system that categorizes prosthetic component prescriptions into “basic,” “intermediate,” and “advanced” and assess its content validity, reliability, and accuracy. Classification development and validation study. The Veterans Affairs (VA) Corporate Data Warehouse database and National Prosthetics Patient Database were used to identify patients undergoing their first amputation at the transtibial or transfemoral level due to diabetes or peripheral artery disease and to identify the associated codes for each LLP. An expert panel of 6 nationally recognized certified prosthetists, a national expert in VA prosthetics data and coding, a physical medicine and rehabilitation physician, and an epidemiologist developed an LLP classification system (PROClass) using 30 transfemoral and transtibial lower limb amputees. The expert panel reviewed 20 consecutive participants meeting study criteria for the development of the PROClass system and a subsequent 30 consecutive cases for assessing the inter- and intra-rater reliability and accuracy. The interrater and intrarater reliability was almost perfect with Gwet's AC1 values ranging from .82 to .96 for both expert panel members and research assistants. The accuracy of the research assistant's classifications to the “criterion standard” was excellent with Gwet's AC1 values ranging between .75 and .92. PROClass is a pragmatic, reliable, and accurate prosthetic classification system with strong face validity that will enable the classification of prosthetic components used for large data set research aimed at evaluating important clinical questions such as the effects of sophistication on patient outcomes.