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

Xiao Ming-Xia, Lu Chang-Hua, Ta Na, Wei Hai-Cheng, Haryadi Bagus, Wu Hsien-Tsai

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 3

1140--1149

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.