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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Sep 15, 2025; 16(9): 104290
Published online Sep 15, 2025. doi: 10.4239/wjd.v16.i9.104290
Published online Sep 15, 2025. doi: 10.4239/wjd.v16.i9.104290
Development and validation of a hypoglycemia risk prediction tool for hospitalized patients with type 2 diabetes mellitus treated with insulin
Yao Zhang, Xi-Ling Hu, Yan-Ming Chen, Xiao-Di Guo, Shu-Hong Liu, Department of Endo
Yao Zhang, Department of Endocrinology and Metabolism, Zhaoqing Hospital, The Third Affiliated Hospital of Sun Yat-sen University, Zhaoqing 526000, Guangdong Province, China
Xi-Ling Hu, Department of Nursing, Zhaoqing Hospital, The Third Affiliated Hospital of Sun Yat-sen University, Zhaoqing 526000, Guangdong Province, China
Wei-Ran Xu, School of Nursing, Peking University, Beijing 100191, China
Ling-Ling Gao, Faculty of Health Sciences and Sports, Macao Polytechnic University, Macau, China
Co-first authors: Yao Zhang and Xi-Ling Hu.
Co-corresponding authors: Shu-Hong Liu and Ling-Ling Gao.
Author contributions: Zhang Y was involved in conceptualization, methodology, writing, and editing; Hu XL, Xu WR, and Guo XD were involved in data curation, data analysis, and writing original draft preparation; Chen YM, Guo XD, Liu SH, and Gao LL were involved in conceptualization, methodology, reviewing, and editing. Zhang Y and Hu XL contributed equally to this work as co-first authors. First, the two co-corresponding authors of this study jointly assume responsibility for interpreting and clarifying the research findings in the paper and bear accountability for the accuracy and completeness of its content. Furthermore, the designation of dual corresponding authors reflects the deep integration between medical academic research and clinical practice, facilitating resource sharing and knowledge exchange, which contributes to advancing research depth and broadening the dissemination of outcomes. Professor Gao LL from the Faculty of Health Sciences and Sports, Macao Polytechnic University, Macau, China, ensured methodological rigor in predictive model construction through statistical optimization and research design, thereby guaranteeing the accuracy and scientific validity of the results. Meanwhile, Head Nurse Liu SH from the Department of Endocrinology and Metabolism at the Third Affiliated Hospital of Sun Yat-sen University leveraged extensive clinical expertise to identify key risk factors in real-world clinical settings and validate the clinical applicability of assessment tools. This collaborative guidance model effectively bridges the requirements of evidence-based medical research with clinical translation applications, satisfying both academic journals' demands for innovative research and healthcare settings' expectations for practical utility. Both experts have made substantial contributions to study design, data analysis, and final review processes, fulfilling the International Committee of Medical Journal Editors' criteria for corresponding authorship. This cooperative exemplifies complementary strengths between theoretical framework development and practical clinical validation.
Supported by Medical Scientific Research Foundation of Guangdong Province of China, No. A2023183 and No. A2024530; Nursing Innovation Development Research Project, No. YJYZ202304; National Natural Science Foundation of China, No. 72204277; Guangdong Basic and Applied Basic Research Foundation, No. 2025A1515012706; and 3rd Affiliated Hospital of Sun Yat-sen University, Clinical Research Program, No. YHJH202404.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University (Guangzhou, China).
Informed consent statement: All study participants or their legal guardian provided informed written consent about personal and medical data collection prior to study enrolment.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: No additional data are available.
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: Ling-Ling Gao, Professor, Faculty of Health Sciences and Sports, Macao Polytechnic University, MengTak Building, Rua de Luis Gonzaga Gomes, Macau, China.
Received: December 24, 2024
Revised: April 18, 2025
Accepted: August 11, 2025
Published online: September 15, 2025
Processing time: 265 Days and 6.7 Hours
Revised: April 18, 2025
Accepted: August 11, 2025
Published online: September 15, 2025
Processing time: 265 Days and 6.7 Hours
Core Tip
Core Tip: The hypoglycemia risk-prediction model was developed using the logistic regression and nomogram models. The model was validated and calibrated using the receiver operating characteristic curves and the Hosmer-Lemeshow goodness of fit test. The incidence of hypoglycemia was 44.9%. The model included eight independent hypoglycemia risk factors. The hypoglycemia risk prediction model for hospitalized T2 diabetes mellitus patients treated with insulin showed high reliability and discrimination ability.