CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach

• Published in: Diagnostics (Basel) Vol. 12; no. 5; p. 1076
• Main Authors: Zhang, Di, Dong, Wei, Guan, Haonan, Yakupu, Aobuliaximu, Wang, Hanqi, Chen, Liuping, Lu, Shuliang, Tang, Jiajun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25-04-2022; MDPI
• Subjects: Accuracy; Amputation; Ankle; artificial neural networks; Clinical medicine; Debridement; Diabetes; diabetic foot ulcer; Foot diseases; Generalized linear models; Hemoglobin; Hospitals; Leg ulcers; lower extremity CT angiography; Medical imaging; Neural networks; Neurons; Risk factors; Scanners; Skin; Statistical prediction; Wound healing; China; artificial neural networks; diabetic foot ulcer; lower extremity CT angiography
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12051076

The purpose of our study is to predict the occurrence and prognosis of diabetic foot ulcers (DFUs) by clinical and lower extremity computed tomography angiography (CTA) data of patients using the artificial neural networks (ANN) model. DFU is a common complication of diabetes that severely affects the quality of life of patients, leading to amputation and even death. There are a lack of valid predictive techniques for the prognosis of DFU. In clinical practice, the use of scales alone has a large subjective component, leading to significant bias and heterogeneity. Currently, there is a lack of evidence-based support for patients to develop clinical strategies before reaching end-stage outcomes. The present study provides a novel technical tool for predicting the prognosis of DFU. After screening the data, 203 patients with diabetic foot ulcers (DFUs) were analyzed and divided into two subgroups based on their Wagner Score (138 patients in the low Wagner Score group and 65 patients in the high Wagner Score group). Based on clinical and lower extremity CTA data, 10 predictive factors were selected for inclusion in the model. The total dataset was randomly divided into the training sample, testing sample and holdout sample in ratio of 3:1:1. After the training sample and testing sample developing the ANN model, the holdout sample was utilized to assess the accuracy of the model. ANN model analysis shows that the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of the overall ANN model were 92.3%, 93.5%, 87.0%, 94.2% and 0.955, respectively. We observed that the proposed model performed superbly on the prediction of DFU with a 91.6% accuracy. Evaluated with the holdout sample, the model accuracy, sensitivity, specificity, PPV and NPV were 88.9%, 90.0%, 88.5%, 75.0% and 95.8%, respectively. By contrast, the logistic regression model was inferior to the ANN model. The ANN model can accurately and reliably predict the occurrence and prognosis of a DFU according to clinical and lower extremity CTA data. We provided clinicians with a novel technical tool to develop clinical strategies before end-stage outcomes.