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Development Of the VAMPCT Score for Predicting Mortality in CKD Patients with COVID-19

Chaofan Li1, Yue Niu1, Xinyan Pan2

  • 1Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing Key Laboratory of Digital Intelligent TCM for the Prevention and Treatment of Pan-vascular Diseases, Key Disciplines of National Administration of Traditional Chinese Medicine (zyyzdxk-2023310), Beijing 100853, China.

International Journal of Medical Sciences
|June 16, 2025
PubMed

Insights

A new machine learning score, VAMPCT, accurately predicts COVID-19 mortality in chronic kidney disease (CKD) patients. This tool aids in identifying high-risk individuals for better clinical management.

Area of Science:

  • Nephrology
  • Infectious Diseases
  • Artificial Intelligence in Medicine

Background:

  • Patients with chronic kidney disease (CKD) face elevated mortality risk from coronavirus disease 2019 (COVID-19).
  • Current methods for identifying high-risk CKD patients with COVID-19 are suboptimal.
  • There is a need for improved clinical tools to predict mortality in this vulnerable population.

Purpose of the Study:

  • To develop and validate a machine learning (ML)-based score for predicting acute COVID-19 mortality in CKD patients.
  • To identify key clinical and laboratory factors associated with mortality in this cohort.
  • To create a user-friendly scoring system for clinical application.

Main Methods:

  • Prospective enrollment of 219 CKD inpatients with COVID-19 from December 2022 to January 2023.
  • Feature selection using least absolute shrinkage and selection operator (LASSO) and stepwise methods.
  • Development and comparison of ML models including logistic regression, SVM, random forest, and XGBoost.
  • Construction of a predictive score based on logistic regression using identified key factors.

Main Results:

  • The study included 219 CKD patients with a high mortality rate of 25.1%.
  • The Support Vector Machine (SVM) model demonstrated strong predictive performance (AUC 0.946).
  • The developed VAMPCT score, incorporating COVID-19 vaccination status, age, monocyte percentage, prothrombin activity, cardiac troponin T, and total bilirubin, achieved a high AUC of 0.960 for predicting 3-month mortality.

Conclusions:

  • Machine learning models show excellent performance in predicting 3-month mortality for CKD patients with COVID-19.
  • The VAMPCT score offers a clinically practical and accurate method for risk stratification.
  • This tool can aid clinicians in managing high-risk CKD patients during the COVID-19 pandemic.

Keywords:
COVID-19chronic kidney diseasecohort studymachine learningmortality.

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