Predicting Time to First Rejection Episode in Lung Transplant Patients Using a Comprehensive Multi-Indicator Model

Area of Science:

• Transplantation research
• Biomarker discovery
• Immunology

Background:

• Lung transplant rejection significantly impacts long-term survival.
• Current methods lack reliable biomarkers for predicting rejection risk.

Purpose of the Study:

• To develop and validate a prognostic model using laboratory data.
• To predict the time to the first rejection episode in lung transplant recipients.

Main Methods:

• Retrospective analysis of data from 160 lung transplant recipients.
• Utilized Cox analysis, LASSO regression, and Kaplan-Meier survival analysis.
• Developed and validated a predictive riskScore model.

Main Results:

• Six laboratory indicators (Activated Partial Thromboplastin Time, IL-10, estimated intrapulmonary shunt, 50% Hemolytic Complement, IgA, Complement Component 3) identified as significant predictors.
• The riskScore model demonstrated strong predictive performance and outperformed individual indicators.
• The riskScore was validated as an independent risk factor for rejection.

Conclusions:

• The developed riskScore model accurately predicts the time to first rejection episode in lung transplant recipients.
• This model offers a promising tool for personalized risk assessment and management in lung transplantation.