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©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
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, 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
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
Core Tip
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.