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Assessment of Vascular Function in Patients With Chronic Kidney Disease
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Assessment of Vascular Function in Patients With Chronic Kidney Disease

Published on: June 16, 2014

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Unsupervised k-means clustering identified routine clinical biomarkers for classifying chronic kidney disease in

Liang Gong1, Zhanglin Hu2, Xiuying Lin1

  • 1Department of Clinical Laboratory, Ningde Municipal Hospital of Ningde Normal University, Ningde, Fujian, China.

Laboratory Medicine
|November 26, 2025
PubMed

Insights

A new 5-biomarker panel effectively distinguishes chronic kidney disease (CKD) from non-CKD in heart failure (HF) patients. This panel also predicts future kidney function changes, offering valuable prognostic insights.

Area of Science:

  • Nephrology
  • Cardiology
  • Biomarker Discovery

Background:

  • Distinguishing chronic kidney disease (CKD) in patients with chronic heart failure (HF) is clinically important.
  • Routine biomarkers are needed for accurate CKD diagnosis and prognosis in HF patients.

Purpose of the Study:

  • To identify a diagnostic panel of routine clinical biomarkers to differentiate CKD from non-CKD in chronic HF patients.
  • To predict kidney function changes over one year in this patient population.

Main Methods:

  • K-means clustering was applied to data from 432 chronic HF patients.
  • A 5-biomarker panel (albumin, lymphocyte percentage, hemoglobin, cholesterol, apolipoprotein A) was identified.
  • Diagnostic and predictive performance was assessed using area under the curve (AUC).

Main Results:

  • The 5-biomarker panel achieved 71.59% concordance with actual CKD diagnoses.
  • The panel distinguished CKD from non-CKD with an AUC of 0.81.
  • It predicted kidney function improvement (AUC=0.87) and worsening (AUC=0.75).

Conclusions:

  • A panel of routine biomarkers can improve CKD detection in chronic HF patients.
  • This biomarker panel offers prognostic value beyond traditional measures like estimated glomerular filtration rate.
Abstract

Keywords:
biomarkerchronic heart failurechronic kidney diseasemachine learningprognostic model

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