Latent Class Analysis Identifies Pulmonary Function Trajectory Phenotypes in Lung Transplant Recipients with Chronic...

Area of Science:

• Pulmonary Medicine
• Transplantation Immunology
• Biostatistics

Background:

• Chronic lung allograft dysfunction (CLAD) is a major cause of lung transplant failure.
• Understanding FEV1 trajectories post-CLAD is crucial for prognostication.
• Early identification of risk phenotypes is needed for better patient management.

Purpose of the Study:

• To identify data-driven FEV1 trajectory phenotypes following CLAD.
• To associate these phenotypes with patient factors and graft loss.
• To develop a classification approach for predicting outcomes in prospective lung transplant recipients.

Main Methods:

• Analysis of adult first lung recipients with probable CLAD from two multicenter cohorts (CTOT-20 and LTOG).
• Characterization of FEV1 trajectories using joint latent class mixed models, accounting for informative censoring.
• Development and application of a classification and regression tree (CART) model for risk stratification.

Main Results:

• Four distinct early FEV1 trajectory classes were identified, showing significant differences in nine-month graft loss.
• Similar patterns were reproduced in a separate cohort, with an additional class showing early FEV1 improvement.
• A CART model demonstrated good discrimination and consistent class assignment across cohorts, with clear risk gradients identified.

Conclusions:

• Reproducible and clinically meaningful early post-CLAD FEV1 trajectory phenotypes with differential graft loss risks were identified.
• These phenotypes and a classification tool can support risk stratification and improve prognostication.
• The findings may enhance clinical trial enrichment and patient management strategies.