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Cited by 49Chronic Kidney Disease and DiabetesArtificial Intelligence in HealthcareRenal Diseases and Glomerulopathies

New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients

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Zhang, WeiGuang, Liu, XiaoMin, Dong, ZheYi, Wang, Qian, Pei, ZhiYong, Chen, YiZhi, Zheng, Ying, Wang, Yong +5 more·2022·Frontiers in Endocrinology
DOI 10.3389/fendo.2022.913021PMC PMC9279696
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Abstract

Background: The disease pathology for diabetes mellitus patients with chronic kidney disease (CKD) may be diabetic nephropathy (DN), non-diabetic renal disease (NDRD), or DN combined with NDRD. Considering that the prognosis and treatment of DN and NDRD differ, their differential diagnosis is of significance. Renal pathological biopsy is the gold standard for diagnosing DN and NDRD. However, it is invasive and cannot be implemented in many patients due to contraindications. This article constructed a new noninvasive evaluation model for differentiating DN and NDRD. Methods: We retrospectively screened 1,030 patients with type 2 diabetes who has undergone kidney biopsy from January 2005 to March 2017 in a single center. Variables were ranked according to importance, and the machine learning methods (random forest, RF, and support vector machine, SVM) were then used to construct the model. The final model was validated with an external group (338 patients, April 2017–April 2019). Results: In total, 929 patients were assigned. Ten variables were selected for model development. The areas under the receiver operating characteristic curves (AUCROCs) for the RF and SVM methods were 0.953 and 0.947, respectively. Additionally, 329 patients were analyzed for external validation. The AUCROCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively. Conclusion: We successfully constructed a predictive model for DN and NDRD using machine learning methods, which were better than our regression methods. Clinical Trial Registration: ClinicalTrial.gov, NCT03865914.

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  • Zhang et al. reported that RF and SVM using clinical data to predict and differentiate DN and non-diabetic renal disease achieved AUCs of 0.953 and 0.947 in internal validation, and 0.920 and 0.911 in external validation.

    “the AUCs for the RF and SVM methods were 0.953 and 0.947, respectively (internal validation); the AUCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively”

    From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Zhang et al.113

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Authors
Zhang, W., Liu, X., Dong, Z., Wang, Q., Pei, Z., Chen, Y., Zheng, Y., Wang, Y., Chen, P., Feng, Z., Sun, X., Cai, G., & Chen, X.
Affiliations
  • Chinese PLA General Hospital (CN)
  • Beijing Computing Center (CN)
Corresponding author
  • Cai, Guangyan
  • Chen, XiangMei
ORCID
Venue
Frontiers in Endocrinology
Publisher
Frontiers Media SA
Published
2022-06-30
Volume
13
Pages
913021-913021
ISSN
1664-2392 (ISSN-L)
DOI
10.3389/fendo.2022.913021
PMC
PMC9279696
OpenAlex
W4283727427
Cited by
49
References
0
Citations by year
2026: 9 · 2025: 17 · 2024: 13 · 2023: 9 · 2022: 1
FWCI
6.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Endocrinology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (32141005), 8160050078, 2015CB553605, 81601211, YSPTZX202026, 2016YFC1305500, D171100002817002)
  • National Key Research and Development Program of China (2016YFC1305503, YSPTZX202026, 2015CB553605, 2016YFC1305500, 32141005)
Topics
  • Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Nephrology › Renal Diseases and Glomerulopathies
Keywords
diabetic nephropathy, non-diabetic kidney disease, differential diagnosis, type 2 diabetes, chronic kidney disease, renal pathology, random forest, support vector machine, machine learning prediction models
MeSH terms
China, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Humans, Retrospective Studies, Asian People

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