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Related Concept Videos

Cytomegalovirus Disease01:27

Cytomegalovirus Disease

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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Generation of Multivirus-specific T Cells to Prevent/treat Viral Infections after Allogeneic Hematopoietic Stem Cell Transplant
08:52

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Published on: May 27, 2011

Unveiling CMV Risk Signatures After Allogeneic Stem Cell Transplantation Through Machine Learning Approaches: A

Estela Giménez1, Pablo Rodriguez-Belenguer2, Carlos Solano3,4

  • 1Microbiology Service, Hospital Clínico Universitario, Biomedical Research Institute INCLIVA, Valencia, Spain.

Journal of Medical Virology
|March 13, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict Cytomegalovirus (CMV) infections after allogeneic hematopoietic stem cell transplantation (allo-HCT). These models offer improved risk stratification for CMV DNAemia, clinically significant CMV infection, and refractory CMV infection in allo-HCT patients.

Keywords:
allogeneic hameatopietic stem cell transplatationcytomegalovirusmachine learningrefractory cmv infection

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Area of Science:

  • Hematology
  • Infectious Diseases
  • Computational Biology

Background:

  • Cytomegalovirus (CMV) infections are a significant complication post-allogeneic hematopoietic stem cell transplantation (allo-HCT), leading to increased morbidity and mortality.
  • Refractory CMV infections pose a particular challenge in allo-HCT recipients.
  • Machine learning (ML) offers potential for enhanced risk stratification in managing CMV infections.

Purpose of the Study:

  • To develop and evaluate ML models for predicting CMV DNAemia, clinically significant CMV infection (csCMV-I), and refractory CMV infection (CMV-R) in allo-HCT recipients.
  • To identify key predictive features for different CMV-related outcomes.
  • To establish a foundation for precision risk stratification in CMV management post-allo-HCT.

Main Methods:

  • Retrospective analysis of 933 allo-HCT recipients from 21 Spanish centers (2014-2018).
  • Development and evaluation of six supervised ML algorithms (Random Forest, SVM, Decision Trees, XGBoost, Elastic Net, PLS-Logistic Regression) using repeated fivefold stratified cross-validation.
  • Prediction of CMV DNAemia, csCMV-I, and CMV-R.

Main Results:

  • CMV DNAemia occurred in 53% of patients, 72% of whom developed csCMV-I, and 26% progressed to CMV-R.
  • XGBoost model showed high sensitivity (0.97) for predicting CMV DNAemia and csCMV-I.
  • SVM model achieved a sensitivity of 0.58 for predicting CMV-R.
  • Key predictors included recipient CMV serostatus, conditioning regimens, GvHD, and immunosuppressive protocols.

Conclusions:

  • ML models can identify distinct clinical profiles associated with CMV outcomes in allo-HCT.
  • The models demonstrate high sensitivity but moderate discriminatory capacity for predicting CMV events.
  • This study provides a basis for precision risk stratification, complementing current prophylaxis strategies, with a need for prospective validation.