RESUMO
Objective: 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. Design: Classification development and validation study. Setting: 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. Participants: 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. Main Outcome Measures: 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. Results: 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. Conclusions: 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.
RESUMO
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.
