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
Abstract
BACKGROUND

Among older adults, type 2 diabetes mellitus (T2DM) is widely recognized as one of the most prevalent diseases. Diabetic nephropathy (DN) is a frequent complication of DM, mainly characterized by renal microvascular damage. Early detection, aggressive prevention, and cure of DN are key to improving prognosis. Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis.

AIM

To investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model.

METHODS

The clinical data of 210 patients diagnosed with T2DM and admitted to the First People’s Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed. According to whether the patients had DN, they were divided into the DN group (complicated with DN) and the non-DN group (without DN). Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM. The data were randomly split into a training set (n = 147) and a test set (n = 63) in a 7:3 ratio using a random function. The training set was used to construct the nomogram, decision tree, and random forest models, and the test set was used to evaluate the prediction performance of the model by comparing the sensitivity, specificity, accuracy, recall, precision, and area under the receiver operating characteristic curve.

RESULTS

Among the 210 patients with T2DM, 74 (35.34%) had DN. The validation dataset showed that the accuracies of the nomogram, decision tree, and random forest models in predicting DN in patients with T2DM were 0.746, 0.714, and 0.730, respectively. The sensitivities were 0.710, 0.710, and 0.806, respectively; the specificities were 0.844, 0.875, and 0.844, respectively; the area under the receiver operating characteristic curve (AUC) of the patients were 0.811, 0.735, and 0.850, respectively. The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models (P < 0.05), whereas the difference in AUC values of the random forest and column-line graph models was not statistically significant (P > 0.05).

CONCLUSION

Among the three prediction models, random forest performs best and can help identify patients with T2DM at high risk of DN.

Keywords: Type 2 diabetes mellitus, Diabetic nephropathy, Random forest, Decision-making tree, Nomogram, Forecast

Core Tip: Machine learning is widely used in medical prediction models. Logistic regression (nomogram), decision tree, and random forest models are three important machine learning techniques. However, few studies have compared the predictive efficacies of these three models in patients with type 2 diabetes mellitus and diabetic nephropathy. Here, we established three risk prediction models-nomogram, decision tree, and random forest-for comparison and found that random forest has the strongest combined predictive power.