Discrimination of Chronic Kidney Disease and Diabetic Nephropathy and Analysis of Their Related Influencing Factors
Xiumin Liu,1 Yinpei Guo,2 Jing Wu,1 Nan Yao,2 Han Wang,2 Bo Li2 1Department of Clinical Laboratory, The Second Affiliated Hospital of Jilin University, Changchun, People’s Republic of China; 2Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun...
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2020
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| Acceso en línea: | https://doaj.org/article/9cabdab147874b5096d6646af208b7f8 |
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| Sumario: | Xiumin Liu,1 Yinpei Guo,2 Jing Wu,1 Nan Yao,2 Han Wang,2 Bo Li2 1Department of Clinical Laboratory, The Second Affiliated Hospital of Jilin University, Changchun, People’s Republic of China; 2Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, People’s Republic of ChinaCorrespondence: Bo LiDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, No. 1163 Xinmin Street, Chaoyang District, Changchun City, Jilin Province, People’s Republic of ChinaTel/Fax +8643185619451Email li_bo@jlu.edu.cnPurpose: Clinically there are not many clinical indicators to differentiate diabetic kidney disease (DKD) and chronic kidney disease (CKD). Data from laboratory inspections on admission of clinical patients were used to complete the relationship and discrimination analysis of the two diseases.Patients and Methods: All subjects were taken from the Department of Nephrology of the Second Hospital of Jilin University from January 2019 to September 2020 with clinical diagnosis of CKD or diabetic nephropathy and no other diseases. After querying the hospital’s medical record system, the basic demographic information was obtained, and data on cardiovascular, metabolism, renal function, blood function, and other relevant indicators were extracted as well. IBM SPSS 24.0 software was used for data collation and analysis.Results: A total of 1726 inpatients (986 males and 740 females) over 18 years old were included, 1407 were CKD patients, 319 were DKD patients. Female accounted for 55.4% in CKD patients, 64.6% in DKD patients. Compared to men, women may be more susceptible to DKD (OR=2.234). DKD patients were more likely to be have higher DP, GLU, eGFR, TCHO, and abnormal TVU (OR=1.746, 3.404, 1.107, 3.004, 14.03) while VB12 was the relative risk factor for CKD; thus, low VB12 level is more likely to happen in CKD patients (OR=0.054, OR95%CI: 0.005– 0.552, P=0.014) compared with DKD patients. The stepwise discriminant analysis was completed, only 11 of the 34 variables had discriminative significance. The discriminant score (DS) was set to explore its test efficiency of DKD prediction by drawing ROC curve. Discriminant formula used for CKD and DN identification was given in the study.Conclusion: Female, higher DP, fasting blood GLU and TCHO level seemed to be more indicative for DKD, while lower eGFR level and VB12 deficiency were more likely to point to CKD. Doctors can refer to the discriminant formula to assist in the differential diagnosis of the two diseases after completing the detection of DP, fasting blood GLU, Cys-C, eGFR, TVU, TCHO, FA, VB12, CK, and CK–Mb.Keywords: chronic kidney disease, diabetic kidney disease, multivariate logistic regression, ROC curve, discriminant analysis |
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