Exploration of pathological prediction of chronic kidney diseases by a novel theory of bi-directional probability

Yuan Yang, Min Luo, Li Xiao, Xue Jing Zhu, Chang Wang, Xiao Fu, Shu Guang Yuan, Fang Xiao, Hong Liu, Zheng Dong, Fu You Liu, Lin Sun

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In the clinic, the pathological types of chronic kidney diseases (CKD) are considered references for choosing treatment protocols. From a statistical viewpoint, a non-invasive method to predict pathological types of CKD is a focus of our work. In the current study, following a frequency analysis of the clinical indices of 588 CKD patients in the department of nephrology, a third-grade class-A hospital, a novel theory is proposed: "bi-directional cumulative probability dichotomy". Further, two models for the prediction and differential diagnosis of CKD pathological type are established. The former indicates an occurrence probability of the pathological types, and the latter indicates an occurrence of CKD pathological type according to logistic binary regression. To verify the models, data were collected from 135 patients, and the results showed that the highest accuracy rate on membranous nephropathy (MN-100%), followed by IgA nephropathy (IgAN-83.33%) and mild lesion type (MLN-73.53%), whereas lower prediction accuracy was observed for mesangial proliferative glomerulonephritis (0%) and focal segmental sclerosis type (21.74%). The models of bi-directional probability prediction and differential diagnosis indicate a good prediction value in MN, IgAN and MLN and may be considered alternative methods for the pathological discrimination of CKD patients who are unable to undergo renal biopsy.

Original languageEnglish (US)
Article number32151
JournalScientific Reports
Volume6
DOIs
StatePublished - Aug 25 2016
Externally publishedYes

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Chronic Renal Insufficiency
Differential Diagnosis
Membranous Glomerulonephritis
Nephrology
Sclerosis
Clinical Protocols
Glomerulonephritis
Immunoglobulin A
Logistic Models
Kidney
Biopsy

ASJC Scopus subject areas

  • General

Cite this

Exploration of pathological prediction of chronic kidney diseases by a novel theory of bi-directional probability. / Yang, Yuan; Luo, Min; Xiao, Li; Zhu, Xue Jing; Wang, Chang; Fu, Xiao; Yuan, Shu Guang; Xiao, Fang; Liu, Hong; Dong, Zheng; Liu, Fu You; Sun, Lin.

In: Scientific Reports, Vol. 6, 32151, 25.08.2016.

Research output: Contribution to journalArticle

Yang, Y, Luo, M, Xiao, L, Zhu, XJ, Wang, C, Fu, X, Yuan, SG, Xiao, F, Liu, H, Dong, Z, Liu, FY & Sun, L 2016, 'Exploration of pathological prediction of chronic kidney diseases by a novel theory of bi-directional probability', Scientific Reports, vol. 6, 32151. https://doi.org/10.1038/srep32151
Yang, Yuan ; Luo, Min ; Xiao, Li ; Zhu, Xue Jing ; Wang, Chang ; Fu, Xiao ; Yuan, Shu Guang ; Xiao, Fang ; Liu, Hong ; Dong, Zheng ; Liu, Fu You ; Sun, Lin. / Exploration of pathological prediction of chronic kidney diseases by a novel theory of bi-directional probability. In: Scientific Reports. 2016 ; Vol. 6.
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