Observational Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Nov 15, 2022; 13(11): 986-1000
Published online Nov 15, 2022. doi: 10.4239/wjd.v13.i11.986
Risk factor analysis and clinical decision tree model construction for diabetic retinopathy in Western China
Yuan-Yuan Zhou, Tai-Cheng Zhou, Nan Chen, Guo-Zhong Zhou, Hong-Jian Zhou, Xing-Dong Li, Jin-Rui Wang, Chao-Fang Bai, Rong Long, Yu-Xin Xiong, Ying Yang
Yuan-Yuan Zhou, Hong-Jian Zhou, Xing-Dong Li, Department of Endocrinology and Metabolism, The Sixth Affiliated Hospital of Kunming Medical University, The People’s Hospital of Yuxi City, Yuxi 653100, Yunnan Province, China
Tai-Cheng Zhou, Jin-Rui Wang, Chao-Fang Bai, Rong Long, Yu-Xin Xiong, Ying Yang, Department of Endocrinology and Metabolism, Affiliated Hospital of Yunnan University, The Second People’s Hospital of Yunnan Province, Kunming 650021, Yunnan Province, China
Nan Chen, Guo-Zhong Zhou, Department of Endocrinology and Metabolism, The Frist People’s Hospital of Anning City, Anning City 650300, Yunnan Province, China
Author contributions: Zhou YY contributed to the conception and design, acquisition of data or analysis and interpretation of data, and drafting the article or revising it critically for important intellectual content; Yang Y and Zhou TC were responsible for supervision, project administration, and funding acquisition; Chen N and Zhou GZ were responsible for literature and formal analysis; Wang JR, Bai CF, Long R, Xiong YX, Zhou HJ, and Li XD were responsible for patient recruitment and clinical data curation; all authors gave final approval of the version to be published.
Supported by the Natural Science Foundation of China, No. 82160159; Natural Science Foundation of Yunnan Province, No. 202101AY070001-199; Scientific Research Fund of Yunnan Education Department, No. 2021J0303; and Postgraduate Innovation Fund of Kunming Medical University, No. 2020D009.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Affiliated Hospital of Yunnan University (Approval No. 2021062).
Informed consent statement: Written informed consent was obtained from all participants.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ying Yang, PhD, Chief Doctor, Professor, Department of Endocrinology and Metabolism, Affiliated Hospital of Yunnan University, The Second People’s Hospital of Yunnan Province, No. 176 Qingnian Road, Kunming 650021, Yunnan Province, China. yangying2072@126.com
Received: June 13, 2022
Peer-review started: June 13, 2022
First decision: August 1, 2022
Revised: August 20, 2022
Accepted: October 27, 2022
Article in press: October 27, 2022
Published online: November 15, 2022
Processing time: 151 Days and 1.9 Hours
Abstract
BACKGROUND

Diabetic retinopathy (DR) is the driving force of blindness in patients with type 2 diabetes mellitus (T2DM). DR has a high prevalence and lacks effective therapeutic strategies, underscoring the need for early prevention and treatment. Yunnan province, located in the southwest plateau of China, has a high pre-valence of DR and an underdeveloped economy.

AIM

To build a clinical prediction model that will enable early prevention and treatment of DR.

METHODS

In this cross-sectional study, 1654 Han population with T2DM were divided into groups without (n = 826) and with DR (n = 828) based on fundus photography. The DR group was further subdivided into non-proliferative DR (n = 403) and proliferative DR (n = 425) groups. A univariate analysis and logistic regression analysis were conducted and a clinical decision tree model was constructed.

RESULTS

Diabetes duration ≥ 10 years, female sex, standing- or supine systolic blood pressure (SBP) ≥ 140 mmHg, and cholesterol ≥ 6.22 mmol/L were risk factors for DR in logistic regression analysis (odds ratio = 2.118, 1.520, 1.417, 1.881, and 1.591, respectively). A greater severity of chronic kidney disease (CKD) or hemoglobin A 1c increased the risk of DR in patients with T2DM. In the decision tree model, diabetes duration was the primary risk factor affecting the occurrence of DR in patients with T2DM, followed by CKD stage, supine SBP, standing SBP, and body mass index (BMI). DR classification outcomes were obtained by evaluating standing SBP or BMI according to the CKD stage for diabetes duration < 10 years and by evaluating CKD stage according to the supine SBP for diabetes duration ≥ 10 years.

CONCLUSION

Based on the simple and intuitive decision tree model constructed in this study, DR classification outcomes were easily obtained by evaluating diabetes duration, CKD stage, supine or standing SBP, and BMI.

Keywords: Diabetic retinopathy; Type 2 diabetes; Western China; Decision tree

Core Tip: Due to the underdeveloped economy and higher prevalence of diabetic retinopathy (DR), Yunnan province is facing a serious task of prevention. Based on a large sample of the Han population with type 2 diabetes mellitus in Yunnan province, this study constructed a cost-effective predictive model that may facilitate the timely and individualized estimation of DR risk.