Sun CF, Lin YH, Ling GX, Gao HJ, Feng XZ, Sun CQ. Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy: Existing challenges and proposed enhancements. World J Diabetes 2025; 16(4): 101310 [PMID: 40236862 DOI: 10.4239/wjd.v16.i4.101310]
Corresponding Author of This Article
Chun-Quan Sun, Office of Drug Clinical Trial Institution, Health Management Center (Preventive Treatment of Disease) Tsinghua University Yuquan Hospital, No. 5 Shijingshan Road, Shijingshan District, Beijing 100040, China. scq1218@163.com
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Systematic Reviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Chao-Fan Sun, Hui-Juan Gao, Xing-Zhong Feng, Department of Endocrinology, Tsinghua University Yuquan Hospital, Beijing 100040, China
Chao-Fan Sun, Yu-Han Lin, Guo-Xing Ling, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
Chun-Quan Sun, Office of Drug Clinical Trial Institution, Health Management Center (Preventive Treatment of Disease) Tsinghua University Yuquan Hospital, Beijing 100040, China
Co-corresponding authors: Xing-Zhong Feng and Chun-Quan Sun.
Author contributions: Sun CF, Sun CQ and Feng XZ conceptualized and designed the research; Sun CF, Gao HJ, Feng XZ and Sun CQ searched comprehensive literature; Sun CF, Lin YH and Ling GX performed data analysis; Sun CF, Sun CQ and Feng XZ wrote the paper; All the authors have read and approved the final manuscript. Both Feng XZ and Sun CQ have played important and indispensable roles in the study design, data interpretation and manuscript preparation as the co-corresponding authors. Sun CQ applied for and obtained the funds for this research project. Sun CQ conceptualized, designed, and supervised the whole process of the project. He searched the literature, revised and submitted the early version of the manuscript and the current version of the manuscript. Feng XZ was instrumental and responsible for data re-analysis and re-interpretation, figure plotting. This collaboration between Feng XZ and Sun CQ is crucial for the publication of this manuscript.
Supported by Capital’s Funds for Health Improvement and Research, No. 2024-4-4135.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: Chun-Quan Sun, Office of Drug Clinical Trial Institution, Health Management Center (Preventive Treatment of Disease) Tsinghua University Yuquan Hospital, No. 5 Shijingshan Road, Shijingshan District, Beijing 100040, China. scq1218@163.com
Received: September 12, 2024 Revised: December 17, 2024 Accepted: February 12, 2025 Published online: April 15, 2025 Processing time: 170 Days and 4.7 Hours
Abstract
BACKGROUND
The trend of risk prediction models for diabetic peripheral neuropathy (DPN) is increasing, but few studies focus on the quality of the model and its practical application.
AIM
To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.
METHODS
A meticulous search was conducted in PubMed, EMBASE, Cochrane, CNKI, Wang Fang DATA, and VIP Database to identify studies published until October 2023. The included and excluded criteria were applied by the researchers to screen the literature. Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool. Disagreements were resolved through consultation with a third investigator. Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies. Additionally, the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.
RESULTS
The systematic review included 14 studies with a total of 26 models. The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938. All studies had high risks of bias, mainly due to participants, outcomes, and analysis. The most common predictors included glycated hemoglobin, age, duration of diabetes, lipid abnormalities, and fasting blood glucose.
CONCLUSION
The predictor model presented good differentiation, calibration, but there were significant methodological flaws and high risk of bias. Future studies should focus on improving the study design and study report, updating the model and verifying its adaptability and feasibility in clinical practice.
Core Tip: This study conducted a systematic review and comprehensive evaluation of 26 prediction models for diabetic peripheral neuropathy (DPN) from 14 studies. Despite demonstrating satisfactory differentiation and calibration, the predictor models were found to exhibit significant methodological deficiencies and high risks of bias, primarily related to participant selection, outcome measurement, and data analysis. The most common identified predictors were glycated hemoglobin, age, diabetes duration, lipid abnormalities, and fasting blood glucose. Future research should prioritize improving study design and reporting, updating DPN prediction models, and validating their clinical adaptability and feasibility, which is crucial for enhancing the reliability and practical application of these models.
