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Related Concept Videos

Cytomegalovirus Disease01:27

Cytomegalovirus Disease

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Cytomegalovirus (CMV) disease is caused by human cytomegalovirus, a double-stranded DNA virus of the Herpesviridae family. While primary CMV infection is often asymptomatic in immunocompetent individuals, the virus can cause severe disease in neonates and immunocompromised patients. CMV is the most common cause of congenital viral infection in the United States, and a major pathogen in solid organ and hematopoietic stem cell transplant recipients.CMV is transmitted via bodily fluids, sexual...
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Kidney Transplant I: Introduction01:28

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A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...
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Related Experiment Video

Updated: May 5, 2026

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Interpretable SVM Model for Predicting CMV Infection in Seropositive Kidney Transplant Recipients: A Single-Center

Guangli Zhong1, Yujie Tang1, Runtao Feng1

  • 1Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, People's Republic of China.

Infection and Drug Resistance
|May 4, 2026
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Summary

Machine learning models accurately predict cytomegalovirus (CMV) infection risk in kidney transplant recipients who are CMV-seropositive (R+). Key predictors include T-cell subsets, aiding personalized prevention and monitoring strategies for CMV infection.

Keywords:
cytomegalovirusinfectionkidney transplantationmachine learningpredictive model

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Area of Science:

  • Nephrology
  • Immunology
  • Transplantation Medicine

Background:

  • Cytomegalovirus (CMV) infection poses a significant risk post-kidney transplant.
  • Current risk stratification for CMV in seropositive (R+) recipients is inadequate.
  • Novel predictive tools are needed to manage CMV infection in R+ kidney transplant patients.

Purpose of the Study:

  • To develop and validate machine learning models for precise CMV infection risk prediction in R+ kidney transplant recipients.
  • To integrate clinical and immunological variables for enhanced risk assessment.
  • To identify key predictors of CMV infection in this vulnerable patient group.

Main Methods:

  • A cohort of 162 R+ kidney transplant recipients was studied.
  • Patients were divided into training (70%) and validation (30%) sets.
  • Machine learning models, including Support Vector Machine (SVM), were employed, with feature selection via Boruta and interpretability via SHAP analysis.

Main Results:

  • 51.2% of R+ patients developed CMV DNAemia.
  • Key predictors identified included T-cell subsets (CD8+, CD4+, CD4+CD27-), recipient age, cold ischemia time, donor type, and prevention strategy.
  • Lower CD4+ and CD8+ T-cell counts were associated with higher CMV infection risk. The SVM model demonstrated strong discrimination (AUC=0.821).

Conclusions:

  • An interpretable SVM model effectively identifies R+ kidney transplant recipients at high risk for CMV infection.
  • This model supports the potential for individualized prophylactic and monitoring strategies.
  • External validation in prospective cohorts is recommended to confirm findings.