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Development of prognostic model for patients at CKD stage 3a and 3b in South Central China using computational intelligence

Clinical and Experimental Nephrology volume 24, pages 865–875 (2020)Cite this article

Abstract

Background

Chronic kidney disease (CKD) stage 3 was divided into two subgroups by eGFR (45 mL/ min 1.73 m2). There is difference in prevalence of CKD, racial differences, economic development, genetic, and environmental backgrounds between China and Western countries.

Methods

We used a computational intelligence model (CKD stage 3 Modeling, CSM) to distinguish CKD stage 3 with CKD stage 3a/3b by data distribution rules, pearson correlation coefficient (PCC), spearman correlation (SCC) analysis, logistic regression (LR), random forest (RF), support vector machine (SVM), and neural network (Nnet) to develop Prognostic Model for patients with CKD stage 3a/3b in South Central China. Furthermore, we used RF to discover risk factors of progression of CKD stage 3a and 3b to CKD stage 5. 1090 cases of CKD stage 3 patients in Xiangya Hospital were collected. Among them, 455 patients progressed to CKD stage 5 in a median follow-up of 4 years (IQR 4.295, 4.489).

Results

We found that the common risk factors for progression of CKD stage 3a/3b to CKD stage 5 included albumin, creatinine, total protein, etc. Proteinuria, direct bilirubin, hemoglobin, etc. accounted for the progression from stage CKD stage 3a to stage 5. The risk factors for CKD stage 3b progression to stage 5 included low-density lipoprotein cholesterol, diabetes, eosinophil percentage, etc.

Conclusions

CSM could be used as a point-of-care test to screen patients at high risk for disease progression, might allowing individualized therapeutic management.

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Acknowledgment

This work was supported in part by the Hunan Provincial Natural Science Foundation of China under Grant no. 2018JJ5056 and by the QUALCOMM university-sponsored program and Xiangya Clinical Big Data Project of Central South University. We thank Yidu Cloud (Beijing) Technology Co., Ltd. for technical assistance.

Author information

Author notes

  1. Qiongjing Yuan and Haixia Zhang contribute equal as first authors.

Affiliations

  1. Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China

    Qiongjing Yuan, Haixia Zhang, Yanyun Xie & Xiangcheng Xiao

  2. Department of Nephrology, Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou, 215000, Jiangsu, China

    Haixia Zhang

  3. Department of Pathology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China

    Wei Lin

  4. Changsha Aeronautical Vocational and Technical College, Changsha, 410014, Hunan, China

    Liangang Peng

  5. Bitvalue Technology (Hunan) Company Limited, Xiangjiang Road, Changsha, 410082, China

    Liming Wang

  6. Mobile Health Ministry of Education-China Mobile Joint Laboratory, Xiangya Hospital, Central South University, Changsha, 410008, China

    Weihong Huang

  7. Network Information Center, Xiangya Hospital, Central South University, Xiangya Road, Changsha, 410008, China

    Song Feng

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  1. Qiongjing Yuan
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Correspondence to Song Feng or Xiangcheng Xiao.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the Ethics Committee (Xiangya Hospital, Central South University, No. 2018121290) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Yuan, Q., Zhang, H., Xie, Y. et al. Development of prognostic model for patients at CKD stage 3a and 3b in South Central China using computational intelligence. Clin Exp Nephrol 24, 865–875 (2020). https://doi.org/10.1007/s10157-020-01909-5

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  • DOI: https://doi.org/10.1007/s10157-020-01909-5

Keywords

  • CKD stage 3 modeling
  • Chronic kidney disease
  • Computational intelligence
  • End-stage renal disease