Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Jan 15, 2024; 15(1): 43-52
Published online Jan 15, 2024. doi: 10.4239/wjd.v15.i1.43
Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus
Sha-Sha Cai, Teng-Ye Zheng, Kang-Yao Wang, Hui-Ping Zhu
Sha-Sha Cai, Teng-Ye Zheng, Kang-Yao Wang, Hui-Ping Zhu, Department of Nephrology, The First People’s Hospital of Wenling, Wenling 317500, Zhejiang Province, China
Author contributions: Cai SS contributed to the conception and design of this study; Zheng TY and Wang KY participated in the administrative support; Zhu HP took part in the provision of study materials or patients; and all authors approved the final manuscript.
Institutional review board statement: The study was reviewed and approved by the First People’s Hospital of Wenling (Approval No. KY-2023-2034-01).
Informed consent statement: Approved exemption for informed consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The clinical data for research can be obtained from the corresponding author.
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: Hui-Ping Zhu, MM, Associate Chief Physician, Reader in Health Technology Assessment, Department of Nephrology, The First People’s Hospital of Wenling, No. 333 Chuan’an South Road, Chengxi Street, Wenling 317500, Zhejiang Province, China. zhuhuiping2261@163.com
Received: August 24, 2023
Peer-review started: August 24, 2023
First decision: November 9, 2023
Revised: November 25, 2023
Accepted: December 25, 2023
Article in press: December 25, 2023
Published online: January 15, 2024
ARTICLE HIGHLIGHTS
Research background

Hyperglycemia is the main pathophysiological feature of diabetes, and its complications are the key factors of death and disability in patients with diabetes. Diabetic nephropathy (DN) is a microvascular complication and is one of the main complications of diabetes. The initial prediction of DN is beneficial for taking measures to prevent and delay the occurrence and progression of corresponding complications. Machine learning has been widely used to construct predictive models for diabetic complications.

Research motivation

Patients with type 2 diabetes mellitus (T2DM) complicated by DN are at high risk of mortality. We explored the factors affecting the complications of DN to establish three prediction models commonly used in medicine, compared the prediction effects, and selected the optimal model to provide a basis for clinical identification of patients with T2DM complicated with DN.

Research objectives

This study aimed to explore the factors influencing T2DM complicated with DN and use these factors to construct a prediction model for DN. The prediction effect of random forest is the best among the three models of nomogram, decision tree, and random forest and may become a useful tool for the early recognition of the risk of DN.

Research methods

We retrospectively analyzed the clinical data of 210 patients with T2DM treated at our hospital between August 2019 and August 2022. Factors influencing DN were analyzed, and nomograms, decision trees, and random forest prediction models were established to compare their prediction efficiency. These three prediction methods are widely used in the medical field and have advantages and limitations. At the same time, through research, we can select a more suitable model to predict the complication risk of DN.

Research results

Fasting blood glucose, serum creatinine, glycosylated hemoglobin, diabetic retinopathy, and the duration of diabetes were independent factors influencing DN. Among the established nomograms, decision trees, and random forest prediction models, random forest has the best predictive ability and can be applied to the prevention and early screening of DN. Future studies should validate the model using prospective and multi-center data and include more samples and variables to further improve the prediction ability of the model. In addition, existing algorithms should be further improved, and a combination of multiple algorithms should be considered to improve the prediction accuracy.

Research conclusions

In this study, the predictive performances of three models were compared. The random forest model performed best in predicting the risk of DN in patients with T2DM and may be a useful alternative tool for diagnosing T2DM.

Research perspectives

Future studies should include larger and more comprehensive samples, conduct multi-center studies, further improve existing algorithms, and consider the combination of multiple algorithms to construct a more complete and accurate prediction model.