606-P: Artificial Neuronal Network Model for Three-Month Prognosis in Diabetic Foot Syndrome

Abstract

Background: To make a reliable prognosis on wound healing in patients with diabetic foot syndrome (DFS) is extremely difficult. We used the artificial neuronal network (ANN) to identify the most significant variables which affect DFS healing process. We also aimed at providing data for designing a digital application which would help practitioners to predict the course of DFS. Methods: We enrolled 213 DFS patients and examined them using a variety of diagnostic tests. The patients were followed for three months. In the initial model, we assessed 35 clinical and biochemical variables. Subsequently, with the help of traditional statistics we reduced their number to twelve, and after conducting the sensitivity analysis, we found out that the final number of significant variables is as low as six. Results: The most significant variables in predicting the outcome of DFS treatment were: probe-to-bone test result, presence of blood flow in Doppler probe, prior amputation within the foot, erythrocyte sedimentation rate, and the area and duration of the ulceration. The area under the ROC curve was 0.87 (Figure). The total accuracy was 85%, sensitivity 94.6%, specificity 66% and F1 score 89%. Conclusions: ANN can be used in the prediction of the DFS course. The algorithm, which is the source of a digital application, is particularly useful in identifying individuals with diabetic foot ulcerations who fail to be healed in three months. Disclosure A.A. Poradzka: Other Relationship; Self; Boehringer Ingelheim (Canada) Ltd., Urgo Medical. L. Czupryniak: None.