Mortality prediction following non-traumatic amputation of the lower extremity

Background: Patients who undergo lower extremity amputation secondary to the complications of diabetes or peripheral artery disease have poor long-term survival. Providing patients and surgeons with individual-patient, rather than population, survival estimates provides them with important information to make individualized treatment decisions. Methods: Patients with peripheral artery disease and/or diabetes undergoing their first unilateral transmetatarsal, transtibial or transfemoral amputation were identified in the Veterans Affairs Surgical Quality Improvement Program (VASQIP) database. Stepdown logistic regression was used to develop a 1-year mortality risk prediction model from a list of 33 candidate predictors using data from three of five Department of Veterans Affairs national geographical regions. External geographical validation was performed using data from the remaining two regions. Calibration and discrimination were assessed in the development and validation samples. Results: The development sample included 5028 patients and the validation sample 2140. The final mortality prediction model (AMPREDICT-Mortality) included amputation level, age, BMI, race, functional status, congestive heart failure, dialysis, blood urea nitrogen level, and white blood cell and platelet counts. The model fit in the validation sample was good. The area under the receiver operating characteristic (ROC) curve for the validation sample was 0·76 and Cox calibration regression indicated excellent calibration (slope 0·96, 95 per cent c.i. 0·85 to 1·06; intercept 0·02, 95 per cent c.i. –0·12 to 0·17). Given the external validation characteristics, the development and validation samples were combined, giving a total sample of 7168. Conclusion: The AMPREDICT-Mortality prediction model is a validated parsimonious model that can be used to inform the 1-year mortality risk following non-traumatic lower extremity amputation of patients with peripheral artery disease or diabetes.