Comparison of five systems of classification of diabetic foot ulcers and predictive factors for amputation. Issue 3 (10th October 2016) - Jeon, Byung‐Joon; Choi, Hwan Jun; Kang, Jin Seok; Tak, Min Sung; Park, Eun Soo
Abstract: Diabetes mellitus is a common metabolic disorder. Among various complications, diabetic neuropathy and peripheral vascular disorders are closely associated with diabetic foot ulcers (DFUs). Lower extremity ulcers and amputations are ongoing problems among individuals with diabetes. There are several classification systems for DFUs; however, no prognostic system has to date been accepted as the gold standard or the optimum prediction tool for amputations. A retrospective study was designed. Demographic data and baseline laboratory data were gathered and scored or evaluated using five representative DFU classification systems. These included (i) the diabetic ulcer severity score (DUSS); (ii) University of Texas (UT) diabetic wound classification; (iii) Meggitt–Wagner classification; (iv) depth of the ulcer, extent of bacterial colonisation, phase of ulcer and association aetiology (DEPA) scoring system; and (v) site, ischaemia, neuropathy, bacterial infection and depth (SINBAD) score. Finally, a statistical analysis was performed. A total of 137 patients were included in this study. During the follow‐up, DFU had healed in 51·1% of subjects and 48·9% of the individuals underwent lower extremity amputations (LEAs). In a univariable logistic regression analysis, history of previous DFU, hypertension, neuropathy, haemoglobin, C‐reactive protein (CRP) and ankle‐brachial index (ABI) showed a statistically significant difference between the healed group and the LEA group. Moreover, the stages, grades or overall prognostic ability of all five classifications were highly associated with the overall occurrence of LEA. On multivariable logistic regression analysis of the risk of LEA, all classifications showed a significant positive trend with an increased number of amputations. All the five classification systems exhibited high sensitivity, specificity, classification accuracy, positive predictive, negative predictive and area under the curve (AUC) values. They showed substantial accuracy and their main variables were associated with LEA occurrence. The Wagner and UT systems, although they are relatively simple to assess, were better predictors of LEA.