Machine learning prediction of future peripheral neuropathy in type 2 diabetics with percussion entropy and body mass indices

This study was designed to evaluate the clinical applications of body mass index (BMI) and a percussion-entropy-based index (PEINEW) for predicting the development of diabetic peripheral neuropathy (DPN) in a group of type 2 diabetes mellitus (DM) patients. The study population comprised a sample of 90 subjects with diabetics (aged 37-86 years), who went through a blood test and photoplethysmography (PPG) measurement and were then followed for 5.5 years. Conventional parameters, including the small-scale multiscale entropy index (MEISS), pulse wave velocity with electrocardiogram located (PWVmean), and PEIoriginal, were computed and compared. A logistic regression model with PEINEW and a single categorical variable (BMI) showed a graded association between the diabetics, with a high BMI (i.e., "high" category) associated with a 12.53-fold greater risk of developing DPN relative to the diabetics with a low BMI (i.e., "low" category) (p = 0.001). The odds ratio for PEINEW was 0.893. The Kaplan-Meier survival analysis showed that the diabetic patients with BMI > 30 had a significantly higher cumulative incidence of PN on follow-up than those with BMI <= 30 (log-rank test, p < 0.001). These findings suggest that BMI and PEINEW are both important risk and protective factors for new-onset DPN from diabetes mellitus and, thus, BMI and percussion entropy calculation can provide valid information that may help to identify diabetics with a high BMI and a low PEINEW as being at increased risk of future DPN. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences.