Skip Navigation
Skip to contents

Diabetes Metab J : Diabetes & Metabolism Journal

Search
OPEN ACCESS

Search

Page Path
HOME > Search
1 "Fei Xu"
Filter
Filter
Article category
Keywords
Publication year
Authors
Original Article
Complications
Screening Tools Based on Nomogram for Diabetic Kidney Diseases in Chinese Type 2 Diabetes Mellitus Patients
Ganyi Wang, Biyao Wang, Gaoxing Qiao, Hao Lou, Fei Xu, Zhan Chen, Shiwei Chen
Diabetes Metab J. 2021;45(5):708-718.   Published online April 13, 2021
DOI: https://doi.org/10.4093/dmj.2020.0117
  • 6,839 View
  • 141 Download
  • 5 Web of Science
  • 6 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM.
Methods
A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools.
Results
Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ2=3.2756, P=0.9159), LBM1 (χ2=7.749, P=0.4584), LBM2 (χ2=10.023, P=0.2634), and simplified model (χ2=12.294, P=0.1387).
Conclusion
LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.

Citations

Citations to this article as recorded by  
  • Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients
    Chunxia Jiang, Xiumei Ma, Jiao Chen, Yan Zeng, Man Guo, Xiaozhen Tan, Yuping Wang, Peng Wang, Pijun Yan, Yi Lei, Yang Long, Betty Yuen Kwan Law, Yong Xu
    Diabetes, Metabolic Syndrome and Obesity.2024; Volume 17: 1051.     CrossRef
  • Changes in urinary exosomal protein CALM1 may serve as an early noninvasive biomarker for diagnosing diabetic kidney disease
    Tao Li, Tian ci Liu, Na Liu, Man Zhang
    Clinica Chimica Acta.2023; 547: 117466.     CrossRef
  • Developing screening tools to estimate the risk of diabetic kidney disease in patients with type 2 diabetes mellitus
    Xu Cao, Xiaomei Pei
    Technology and Health Care.2023; : 1.     CrossRef
  • Development and validation of a novel nomogram to predict diabetic kidney disease in patients with type 2 diabetic mellitus and proteinuric kidney disease
    Hui Zhuan Tan, Jason Chon Jun Choo, Stephanie Fook-Chong, Yok Mooi Chin, Choong Meng Chan, Chieh Suai Tan, Keng Thye Woo, Jia Liang Kwek
    International Urology and Nephrology.2022; 55(1): 191.     CrossRef
  • Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data
    Nakib Hayat Chowdhury, Mamun Bin Ibne Reaz, Sawal Hamid Md Ali, Shamim Ahmad, María Liz Crespo, Andrés Cicuttin, Fahmida Haque, Ahmad Ashrif A. Bakar, Mohammad Arif Sobhan Bhuiyan
    Journal of Personalized Medicine.2022; 12(9): 1507.     CrossRef
  • Development and assessment of diabetic nephropathy prediction model using hub genes identified by weighted correlation network analysis
    Xuelian Zhang, Yao Wang, Zhaojun Yang, Xiaoping Chen, Jinping Zhang, Xin Wang, Xian Jin, Lili Wu, Xiaoyan Xing, Wenying Yang, Bo Zhang
    Aging.2022; 14(19): 8095.     CrossRef

Diabetes Metab J : Diabetes & Metabolism Journal