Healing Profiles in Patients with a Chronic Diabetic Foot Ulcer: An Exploratory Study with Machine Learning

Abstract

Diabetic foot ulcers (DFU) are one of the most frequent and debilitating complications of diabetes. DFU wound healing is a highly complex process, resulting in significant medical, economic, and social challenges. Therefore, early identification of patients with a high-risk profile would be important to prompt adequate treatment and more successful health outcomes. This study explores risk assessment profiles for DFU healing and healing prognosis, using machine learning predictive approaches and decision trees algorithms. Patients were evaluated at baseline (T0; N = 158) and two months later (T1; N = 108) on sociodemographic, clinical, biochemical, and psychological variables. The performance evaluation of the models comprised F1-score, accuracy, precision, and recall. Only profiles with F1-score > 0.7 were selected for analysis. According to the two profiles generated for DFU healing, the most important predictive factors were illness representations on T1 IPQ-B (IPQ-B ≤ 9.5 and < 10.5) and the DFU duration (≤ 13 weeks). The two predictive models for DFU healing prognosis suggest that biochemical factors are the best predictors of a favorable healing prognosis, namely IL-6, microRNA-146a-5p, and PECAM-1 at T0, and angiopoietin-2 at T1. Illness representations at T0 (IPQ-B ≤ 39.5) also emerged as a relevant predictor for healing prognosis. Results emphasize the importance of DFU duration, illness perception, and biochemical markers to predict healing in chronic non-healing DFU. Future research is needed to confirm and test the obtained predictive models.