Predicting reamputation risk in patients undergoing lower extremity amputation due to the complications of peripheral artery disease and/or diabetes

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 m...

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Published in:British journal of surgery 2019-07, Vol.106 (8), p.1026-1034
Main Authors: Czerniecki, J. M., Thompson, M. L., Littman, A. J., Boyko, E. J., Landry, G. J., Henderson, W. G., Turner, A. P., Maynard, C., Moore, K. P., Norvell, D. C.
Format: Article
Language:English
Subjects:
Aged
Amputation
Amputation - statistics & numerical data
Clinical Decision-Making
Diabetes
Diabetic Angiopathies - epidemiology
Diabetic Angiopathies - surgery
Female
Humans
Leg - surgery
Male
Middle Aged
Models, Statistical
Peripheral Arterial Disease - complications
Peripheral Arterial Disease - epidemiology
Reoperation - statistics & numerical data
Risk Assessment
Risk Factors
Vein & artery diseases
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Summary: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. This study presents the development and validation of a multivariable prediction model (AMPREDICT Reamputation) that can be used to predict risk of reamputation within the first year after incident dysvascular amputation. This model is intended to assist in preoperative surgical treatment planning, postoperative rehabilitation planning and patient education. AMPREDICT quantifies reamputation risk
ISSN:0007-1323
1365-2168
DOI:10.1002/bjs.11160