Background Patients undergoing amputation of the lower extremity for the complications of peripheral artery disease and/or diabetes are at risk of treatment failure and the need for reamputation at a higher level. The aim of this study was to develop a patient‐specific reamputation risk prediction model. Methods Patients with incident unilateral transmetatarsal, transtibial or transfemoral amputation between 2004 and 2014 secondary to diabetes and/or peripheral artery disease, and who survived 12 months after amputation, were identified using Veterans Health Administration databases. Procedure codes and natural language processing were used to define subsequent ipsilateral reamputation at the same or higher level. Stepdown logistic regression was used to develop the prediction model. It was then evaluated for calibration and discrimination by evaluating the goodness of fit, area under the receiver operating characteristic curve (AUC) and discrimination slope. Results Some 5260 patients were identified, of whom 1283 (24·4 per cent) underwent ipsilateral reamputation in the 12 months after initial amputation. Crude reamputation risks were 40·3, 25·9 and 9·7 per cent in the transmetatarsal, transtibial and transfemoral groups respectively. The final prediction model included 11 predictors (amputation level, sex, smoking, alcohol, rest pain, use of outpatient anticoagulants, diabetes, chronic obstructive pulmonary disease, white blood cell count, kidney failure and previous revascularization), along with four interaction terms. Evaluation of the prediction characteristics indicated good model calibration with goodness‐of‐fit testing, good discrimination (AUC 0·72) and a discrimination slope of 11·2 per cent. Conclusion A prediction model was developed to calculate individual risk of primary healing failure and the need for reamputation surgery at each amputation level. This model may assist clinical decision‐making regarding amputation‐level selection.

Significantly higher mortality was associated with glibenclamide, glipizide and rosiglitazone use compared with metformin, and with glipizide use compared with rosiglitazone or glibenclamide. The potential for residual confounding by indication should be considered in interpreting these results.

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.

Background Electronic health data are routinely used to conduct studies of cardiovascular disease in the setting of the Veterans Health Administration (VA). Previous studies have estimated the positive predictive value (PPV) of International Classification of Disease, Ninth Revision (ICD-9) codes for acute myocardial infarction (MI), but the sensitivity of these codes for all true events and the accuracy of coding algorithms for prevalent disease status at baseline are largely unknown. Methods We randomly sampled 180 Veterans from the VA Puget Sound Health Care System who initiated diabetes treatment. The full electronic medical record was reviewed to identify prevalent conditions at baseline and acute MI events during follow up. The accuracy of various coding algorithms was assessed. Results Algorithms for previous acute events at baseline had high PPV (previous MI: 97%; previous stroke: 81%) but low sensitivity (previous MI: 38%; previous stroke: 52%). Algorithms for chronic conditions at baseline had high PPV (heart failure: 72%; coronary heart disease [CHD]: 85%) and high sensitivity (heart failure: 90%, CHD: 84%). For current smoking status at baseline, ICD-9 codes with pharmacy data had a PPV of 77% and sensitivity of 73%. The coding algorithm for acute MI events during follow up had high PPV (80%) and sensitivity (89%) Conclusions ICD-9 codes for acute MI events during follow up had high PPV and sensitivity. The sensitivity of ICD-9 codes for previous acute events at baseline was low, but a composite variable for baseline CHD had good accuracy.

Initiation of metformin versus a sulfonylurea among individuals with type 2 diabetes and CKD was associated with a substantial reduction in mortality, in terms of both relative and absolute risk reduction. The largest absolute risk reduction was observed among individuals with moderately-severely reduced eGFR (30-44 mL/min/1.73m).