lancet-header
Preprints with The Lancet is part of SSRN´s First Look, a place where journals and other research experts identify content of interest prior to publication. These preprint papers are not peer-reviewed. Authors have either opted in at submission to The Lancet family of journals to post their preprints on Preprints with The Lancet, or submitted directly via SSRN. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These papers should not be used for clinical decision making or reporting of research to a lay audience without indicating that this is preliminary research that has not been peer-reviewed. For more information see the Comment published in The Lancet, or visit The Lancet´s FAQ page, and for any feedback please contact preprints@lancet.com

RDDI Screening: A Noninvasive Pathological Prediction Model in Chronic Kidney Diseases

18 Pages Posted: 26 Jul 2019

See all articles by Ying-jin Zhang

Ying-jin Zhang

Central South University - Department of Nephrology

Yuan Yang

Central South University - Department of Nephrology

Chang Wang

Central South University - Department of Nephrology

Li Xiao

Central South University - Department of Nephrology

Aihua Zhang

Guizhou Medical University (Guiyang Medical University)

Fu-You Liu

Central South University - Department of Nephrology

Hong Liu

Central South University - Department of Nephrology

Lin Sun

Central South University - Department of Nephrology

More...

Abstract

Here, we proposed a predictive model of discriminating pathological types in chronic kidney diseases (CKD) patients with mild lesion in glomeruli (ML), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN) or immunoglobulin A nephropathy (IgA) based on the previous study. In the model, a statistical function defined as renal diseases development index (RDDI) was introduced, and was calculated by the first onset age X (Duration+Compensation Duration) X adjustment coefficient in CKD patient, used to screen preliminarily pathological types of CKD patients. Based on the screening of RDDI, another differential judgement model proposed by the previous study was combined to establish a predictive model of noninvasive judgement in CKD pathological types. Intriguingly, the predictive model showed higher identification efficiency and better availability than the previous study, especially for the discrimination of FSGS type. In conclusion, the model of RDDI screening provides an alternative reference of statistical predicting CKD pathological types when renal puncture is unfeasible.

Funding: This work was sponsored by “Big Data of Chronic Kidney Diseases” Project of Central South University (2013-2018), Natural Science Foundations of China (Grant: 81430077, U1812403).

Declaration of Interest: The authors declare that they have no competing interests.

Ethical Approval: The study is classified as a exploration of statistical method and is approved by medical ethics committee of the Second Xiangya Hospital in China.

Keywords: Renal diseases development index; Noninvasive pathological prediction; Chronic kidney diseases

Suggested Citation

Zhang, Ying-jin and Yang, Yuan and Wang, Chang and Xiao, Li and Zhang, Aihua and Liu, Fu-You and Liu, Hong and Sun, Lin, RDDI Screening: A Noninvasive Pathological Prediction Model in Chronic Kidney Diseases (July 22, 2019). Available at SSRN: https://ssrn.com/abstract=3424188 or http://dx.doi.org/10.2139/ssrn.3424188

Ying-jin Zhang

Central South University - Department of Nephrology

Changsha
China

Yuan Yang

Central South University - Department of Nephrology

Hunan, 410011
China

Chang Wang

Central South University - Department of Nephrology

Hunan, 410011
China

Li Xiao

Central South University - Department of Nephrology

Hunan, 410011
China

Aihua Zhang

Guizhou Medical University (Guiyang Medical University)

Guiyang, Guizhou 550004
China

Fu-You Liu

Central South University - Department of Nephrology

Changsha
China

Hong Liu

Central South University - Department of Nephrology ( email )

Changsha
China

Lin Sun (Contact Author)

Central South University - Department of Nephrology ( email )

Changsha
China

Click here to go to TheLancet.com

Paper statistics

Abstract Views
114
Downloads
4