Machine learning prediction of diabetic foot ulcers in the inpatient population

Abstract

Background The objective of this study was to create an algorithm that could predict diabetic foot ulcer (DFU) incidence in the in-patient population. Materials and Methods The Nationwide Inpatient Sample datasets were examined from 2008 to 2014. The International Classification of Diseases 9th Edition Clinical Modification (ICD-9-CM) and the Agency for Healthcare Research and Quality comorbidity codes were used to assist in the data collection. Chi-square testing was conducted, using variables that positively correlated with DFUs. For descriptive statistics, the Student T-test, Wilcoxon rank sum test, and chi-square test were used. There were six predictive variables that were identified. A decision tree model CTREE was utilized to help develop an algorithm. Results 326,853 patients were noted to have DFU. The major variables that contributed to this diagnosis (both with p < 0.001) were cellulitis (OR 63.87, 95% CI [63.87–64.49]) and Charcot joint (OR 25.64, 95% CI [25.09–26.20]). The model performance of the six-variable testing data was 79.5% (80.6% sensitivity and 78.3% specificity). The area under the curve (AUC) for the 6-variable model was 0.88. Conclusion We developed an algorithm with a 79.8% accuracy that could predict the likelihood of developing a DFU.

Machine learning for the prediction of minor amputation in University of Texas grade 3 diabetic foot ulcers

“…With the continuous development of artificial intelligence, machine learning has been applied in the field of medical diagnosis and demonstrated better performance in the prediction of clinical disease prognosis [41]. In recent studies, many studies have shown the superior predictive power of machine learning algorithms for the diagnosis and prognosis of DFU [14][15][16][17][18][19]. However, there is a lack of studies with sufficient case data to determine comparatively which machine learning algorithm is the most suitable for use, especially in patients with UT3 DFU, which is most commonly seen in tertiary care hospitals.…”

“…Machine learning plays an important role in the prediction of many common diseases, such as the diagnostic prediction of patients with type 2 diabetes [12] and the classification of cardiovascular diseases in patients with diabetes [13]. Several studies on DFU have shown the superior predictive performance of machine learning algorithms [14][15][16][17][18][19]. For the DFU diagnosis, Amith Khandakar et al used thermogram images to establish a machine learning model based on CNN for early detection of DFU [15].…”

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

“…In 2021, Stefanopoulos et al published a study based on a conditional inference tree (CTREE) algorithm for the prediction of DFU risk in an inpatient population [ 24 ]. Nationwide Inpatient Sample datasets (USA) from 2008 to 2014, including over 10 million diabetic patients (of which 326,853 had DFU), were used for model generation and testing.…”

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

“…41 Yap et al 42 created an app that allows users to determine whether an image contains a DFU, which is an especially useful tool for clinicians in regions where little training for DFU management exists or for use by vision-impaired patients. Stefanopoulos et al 62 used ML to retrospectively determine which patients in the Nationwide Inpatient Sample (a database which contains approximately 20% of all US hospital admissions) with active DFUs were noted to have a variety of risk factors. Using six risk factors incorporating both physical parameters and demographic information (cellulitis, Charcot joint, peripheral arterial disease, uncontrolled diabetes mellitus, peripheral vascular disease, and male gender), they developed an algorithm that can predict the likelihood of developing a DFU with 79.8% accuracy.…”