Predicting diabetic foot ulceration using routinely collected data in a foot clinic. What level of prognostic accuracy can be achieved?

ABSTRACT

This study aimed to investigate the efficacy of using routinely collected clinical data in predicting the risk of diabetic foot ulcer (DFU). The first objective was to develop a prognostic model based on the most important risk factors objectively selected from a set of 39 clinical measures. The second objective was to compare the prediction accuracy of the developed model against that of a model based on only the 3 risk factors that were suggested in the systematic review and meta-analyses study (PODUS). In a cohort study, a set of 12 continuous and 27 categorical data from patients (n = 203 M/F99/104) who attended a specialised diabetic foot clinic were collected at baseline. These patients were then followed-up for 24 months during which 24 (M/F17/7) patients had DFU. Multivariate logistic regression was used to develop a prognostic model using the identified risk factors that achieved p < 0.2 based on univariate logistic regression. The final prognostic model included 4 risk factors (Adjusted-OR [95% CI]; p) in total. Impaired sensation (116.082 [12.06-1117.287]; p = 0.000) and presence of callus (6.257 [1.312-29.836]; p = 0.021) were significant (p < 0.05), while having dry skin (5.497 [0.866-34.89]; p = 0.071) and Onychomycosis (6.386 [0.856-47.670]; p = 0.071) that stayed in the model were not significant. The accuracy of the model with these 4 risk factors was 92.3%, where sensitivity and specificity were 78.9%, and 94.0% respectively. The 78.9% sensitivity of our prognostic 4-risk factor model was superior to the 50% sensitivity that was achieved when the three risk factors proposed by PODUS were used. Also our proposed model based on the above 4 risk factors showed to predict the DFU with higher overall prognostic accuracy. These findings have implications for developing prognostic models and clinical prediction rules in specific patient populations to more accurately predict DFU.