AMPREDICT MoRe: Predicting Mortality and Re-amputation Risk after Dysvascular Amputation.

Abstract

This study aimed to create a novel prediction model (AMPREDICT MoRe) that predicts death and re-amputation after dysvascular amputation, which overcomes prior implementation barriers by using only predictors that are readily available in the electronic health record (EHR). This was a retrospective cohort study of 9 221 patients with incident unilateral transmetatarsal, transtibial, or transfemoral amputation secondary to diabetes and or peripheral arterial disease identified in the Veterans Affairs Corporate Data Warehouse between 1 October 2015 and 30 September 2021. The prediction model evaluated factors falling into several key domains: prior revascularisation; amputation level; demographics; comorbidities; mental health; health behaviours; laboratory values; and medications. The primary outcome included four categories: (i) no death and no re-amputation (ND/NR); (ii) no death and re-amputation (ND/R); (iii) death and no re-amputation (D/NR); and (iv) death and re-amputation (D/R). Multinomial logistic regression was used to fit one year post-incident amputation risk prediction models. Variable selection was performed using LASSO (least absolute shrinkage and selection operator), a machine learning methodology. Model development was performed using a randomly selected 80% of the data, and the final model was externally validated using the remaining 20% of subjects. The final prediction model included 23 predictors. The following outcome distribution was observed in the development sample: ND/NR, n = 4 254 (57.7%); ND/R, n = 1 690 (22.9%); D/NR, n = 1 056 (14.3%); and D/R, n = 376 (5.1%). The overall discrimination of the model was moderately strong (M index 0.70), but a deeper look at the c indices indicated that the model had better ability to predict death than re-amputation (ND/NR vs. ND/R, 0.64; ND/NR vs. D/NR, 0.78; grouped ND vs. D, 0.79 and NR vs. R, 0.67). The model was best at distinguishing individuals with no negative outcomes vs. both negative outcomes (ND/NR vs. D/R, 0.82). The AMPREDICT MoRe model has been successfully developed and validated, and can be applied at the time of amputation level decision making. Since all predictors are available in the EHR, a future decision support tool will not require patient interview.