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Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning.

Publication ,  Journal Article
Hui, D; Sun, Y; Xu, S; Liu, J; He, P; Deng, Y; Huang, H; Zhou, X; Li, R
Published in: International urology and nephrology
March 2023

The heterogeneity of Type 2 Diabetes Mellitus (T2DM) complicated with renal diseases has not been fully understood in clinical practice. The purpose of the study was to propose potential predictive factors to identify diabetic kidney disease (DKD), nondiabetic kidney disease (NDKD), and DKD superimposed on NDKD (DKD + NDKD) in T2DM patients noninvasively and accurately.Two hundred forty-one eligible patients confirmed by renal biopsy were enrolled in this retrospective, analytical study. The features composed of clinical and biochemical data prior to renal biopsy were extracted from patients' electronic medical records. Machine learning algorithms were used to distinguish among different kidney diseases pairwise. Feature variables selected in the developed model were evaluated.Logistic regression model achieved an accuracy of 0.8306 ± 0.0057 for DKD and NDKD classification. Hematocrit, diabetic retinopathy (DR), hematuria, platelet distribution width and history of hypertension were identified as important risk factors. Then SVM model allowed us to differentiate NDKD from DKD + NDKD with accuracy 0.8686 ± 0.052 where hematuria, diabetes duration, international normalized ratio (INR), D-Dimer, high-density lipoprotein cholesterol were the top risk factors. Finally, the logistic regression model indicated that DD-dimer, hematuria, INR, systolic pressure, DR were likely to be predictive factors to identify DKD with DKD + NDKD.Predictive factors were successfully identified among different renal diseases in type 2 diabetes patients via machine learning methods. More attention should be paid on the coagulation factors in the DKD + NDKD patients, which might indicate a hypercoagulable state and an increased risk of thrombosis.

Duke Scholars

Published In

International urology and nephrology

DOI

EISSN

1573-2584

ISSN

0301-1623

Publication Date

March 2023

Volume

55

Issue

3

Start / End Page

687 / 696

Related Subject Headings

  • Urology & Nephrology
  • Retrospective Studies
  • Machine Learning
  • Humans
  • Hematuria
  • Diabetic Nephropathies
  • Diabetes Mellitus, Type 2
  • 3202 Clinical sciences
  • 1103 Clinical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Hui, D., Sun, Y., Xu, S., Liu, J., He, P., Deng, Y., … Li, R. (2023). Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning. International Urology and Nephrology, 55(3), 687–696. https://doi.org/10.1007/s11255-022-03322-1
Hui, Dongna, Yiyang Sun, Shixin Xu, Junjie Liu, Ping He, Yuhui Deng, Huaxiong Huang, Xiaoshuang Zhou, and Rongshan Li. “Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning.International Urology and Nephrology 55, no. 3 (March 2023): 687–96. https://doi.org/10.1007/s11255-022-03322-1.
Hui D, Sun Y, Xu S, Liu J, He P, Deng Y, et al. Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning. International urology and nephrology. 2023 Mar;55(3):687–96.
Hui, Dongna, et al. “Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning.International Urology and Nephrology, vol. 55, no. 3, Mar. 2023, pp. 687–96. Epmc, doi:10.1007/s11255-022-03322-1.
Hui D, Sun Y, Xu S, Liu J, He P, Deng Y, Huang H, Zhou X, Li R. Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning. International urology and nephrology. 2023 Mar;55(3):687–696.
Journal cover image

Published In

International urology and nephrology

DOI

EISSN

1573-2584

ISSN

0301-1623

Publication Date

March 2023

Volume

55

Issue

3

Start / End Page

687 / 696

Related Subject Headings

  • Urology & Nephrology
  • Retrospective Studies
  • Machine Learning
  • Humans
  • Hematuria
  • Diabetic Nephropathies
  • Diabetes Mellitus, Type 2
  • 3202 Clinical sciences
  • 1103 Clinical Sciences