In Archives of physical medicine and rehabilitation ; h5-index 61.0
UNLABELLED : AMPREDICT PROsthetics- Predicting Prosthesis Mobility to aid in prosthetic prescription and rehabilitation planning.
OBJECTIVE : To develop and validate a patient-specific multivariable prediction model that utilizes 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 due to 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 (CDW), the National Prosthetics Patient Database, participant mailings and phone calls.
PARTICIPANTS : 357 Veterans who underwent an incident dysvascular TT or TF amputation and received a qualifying lower limb prosthesis (LLP) between March 1, 2018, and November 30, 2020.
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.
Norvell Daniel C, Thompson Mary Lou, Baraff Aaron, Biggs Wayne T, Henderson Alison, Moore Kathryn P, Turner Aaron P, Williams Rhonda, Maynard Charles C, Czerniecki Joseph M
2022-Dec-17
Dysvascular, diabetes, lower extremity amputation, lower limb prosthesis, mobility prediction model, peripheral artery disease
