Abstract
OBJECTIVE: The aim of this study was to create a new mobility outcome prediction model (AMPREDICT Mobility-4) that can be applied at the time a decision is made that amputation is necessary due to an underlying vascular aetiology. It can be used to predict mobility outcome in patients undergoing transmetatarsal (TM), transtibial (TT), or transfemoral (TF) amputation. METHODS: A cohort study retrospectively identified persons with lower limb amputation (LLA) through a large Veterans Affairs dataset, then prospectively enrolled participants to obtain their 12 month post-amputation self reported mobility. Seven hundred and four patients with first unilateral TM, TT, or TF amputation secondary to diabetes and or peripheral arterial disease were identified between February 2021 and September 2022. Potential predictors incorporated factors from the following domains: prior revascularisation; amputation level; demographics; comorbidities; mental health; and health behaviour factors. The predicted mobility outcome included four functional levels: wheelchair mobility; household ambulatory mobility; basic community ambulation; and advanced community ambulation. Multinomial logistic regression was used to fit 1 year post-incident amputation risk prediction models. Variable selection was performed using lasso, a machine learning methodology. RESULTS: Variable selection led to a final model of 15 predictors that successfully discriminated between all categories of mobility, except household vs. basic community mobility. The model discriminated best when comparing wheelchair mobility with at least some community ambulatory mobility (optimism adjusted c index = 0.71), a basic level of community mobility vs. less than a basic level of community mobility (optimism adjusted c index = 0.70), and advanced community mobility vs. less than advanced community mobility (optimism adjusted c index = 0.73). CONCLUSION: The AMPREDICT Mobility-4 model predicted four categories of functional mobility at each of three LLA levels using predictors available in the electronic health record. The model can augment clinical personalised mobility outcome prediction, enhancing both surgeon and patient awareness of the consequences of amputation level on mobility, to facilitate patient surgeon amputation level shared decision-making and assisting in setting appropriate patient outcome expectations.