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

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

Identyfikatory

DOI

10.1016/j.bbe.2021.08.001

• Języki publikacji: EN

Abstrakty

EN

This study was designed to evaluate the clinical applications of body mass index (BMI) and a percussion-entropy-based index (PEI_NEW) 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 (MEI_SS), pulse wave velocity with electrocardiogram located (PWV_mean), and PEI_original, were computed and compared. A logistic regression model with PEI_NEW 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 PEI_NEW 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 PEI_NEW 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 PEI_NEW as being at increased risk of future DPN.

Słowa kluczowe

EN

percussion entropy index; PEI; body mass index; BMI; diabetic peripheral neuropathy; DPN; type 2 diabetes; logistic regression; odds ratio

PL

PEI; wskaźnik masy ciała; wskaźnik BMI; neuropatia cukrzycowa; DPN; cukrzyca typu 2; regresja logistyczna; iloraz szans

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 3
• Strony: 1140--1149
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Xiao Ming-Xia

• School of Computer Science and Information Technology, Hefei University of Technology, Hefei, Anhui, China; School of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, China

autor

Lu Chang-Hua

• School of Computer Science and Information Technology, Hefei University of Technology, Hefei, Anhui, China

autor

Ta Na

• School of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, China

autor

Wei Hai-Cheng

• Basic Experimental Teaching & Engineering Training Center, North Minzu University, Yinchuan, Ningxia, China

autor

Haryadi Bagus

• Department of Physics, Universitas Ahmad Dahlan, Jendral A. Yani Street, Kragilan, Tamanan, Kec, Banguntapan, Bantul, Daerah Istimewa, Yogyakarta, Indonesia

autor

Wu Hsien-Tsai

• Department of Electrical Engineering, Dong Hwa University, Shoufeng, Hualien, Taiwan

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).