OBJECTIVE: 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). METHODS: 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/no re-amputation (ND/NR)
(ii) no death/re-amputation (ND/R)
(iii) death/no re-amputation (D/NR)
and (iv) death/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. RESULTS: 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). CONCLUSION: 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.