AMPREDICT PROsthetics- Predicting Prosthetic Mobility to Aid in Prosthetic Prescription and Rehabilitation Planning

Abstract

Research Objectives To develop and validate a patient-specific multivariable prediction model that utilizes readily available predictors to predict 12-month mobility at the time of initial post amputation prosthetic prescription. The prediction model is designed to be used on patients who have undergone their initial transtibial (TT) or transfemoral (TF) amputation for chronic limb threatening ischemia (CLTI). The overall goal is to enhance prediction of future mobility to assist the rehabilitation team in setting appropriate, evidence-based goals, to better inform patient expectations, and to facilitate appropriate prosthetic prescription. Design Mixed-methods study that identified patients retrospectively through a large Veteran's Affairs (VA) dataset then prospectively collected patient reported mobility. Setting The Veterans Administration Corporate Data Warehouse (CDW), the National Prosthetics Patient Database (NPPD), participant mailings and phone calls. Participants 357 Veterans who underwent an incident TT or TF amputation for CLTI and received a qualifying lower limb prosthesis (LLP) between March 1, 2018, and November 30, 2020. Interventions Not applicable. Main Outcome Measures 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 Variable selection, using a machine learning methodology, led to a final model of 23 predictors 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 patient's 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 their anticipated functional goals. Author(s) Disclosures No conflicts of interest. This project was completed from funding provided by a Veterans Administration Research Rehabilitation and Development Grant. To develop and validate a patient-specific multivariable prediction model that utilizes readily available predictors to predict 12-month mobility at the time of initial post amputation prosthetic prescription. The prediction model is designed to be used on patients who have undergone their initial transtibial (TT) or transfemoral (TF) amputation for chronic limb threatening ischemia (CLTI). The overall goal is to enhance prediction of future mobility to assist the rehabilitation team in setting appropriate, evidence-based goals, to better inform patient expectations, and to facilitate appropriate prosthetic prescription. Mixed-methods study that identified patients retrospectively through a large Veteran's Affairs (VA) dataset then prospectively collected patient reported mobility. The Veterans Administration Corporate Data Warehouse (CDW), the National Prosthetics Patient Database (NPPD), participant mailings and phone calls. 357 Veterans who underwent an incident TT or TF amputation for CLTI and received a qualifying lower limb prosthesis (LLP) between March 1, 2018, and November 30, 2020. Not applicable. 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). Variable selection, using a machine learning methodology, led to a final model of 23 predictors 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. The AMPREDICT PROsthetics model can assist providers in estimating individual patient's 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 their anticipated functional goals.