Latent Class Analysis Identifies Pulmonary Function Trajectory Phenotypes in Lung Transplant Recipients with Chronic Allograft Dysfunction
Year: 2026
Type: preprint
Abstract: Background: We aimed to identify data-driven FEV1 trajectory phenotypes post-chronic lung allograft dysfunction (CLAD), relate these phenotypes to patient factors and future graft loss, and develop a classification approach for prospective patients. Methods: We studied adult first lung recipients with probable CLAD from two prospective multicenter cohorts: CTOT-20 (n=206) and LTOG (n=1418). FEV1 trajectories over the first nine months post-CLAD were characterized using joint latent class mixed models, jointly modelling time-to-graft loss to account for informative censoring. Models were fit independently in both cohorts and also only among LTOG bilateral recipients. A classification and regression tree (CART) model was derived in LTOG bilateral recipients and applied to CTOT-20 bilateral recipients. Findings: Four distinct early FEV1 trajectory classes were identified in CTOT-20, with large differences in nine-month graft loss (72·3%, 31·1%, 2·2%, 0%). In LTOG, similar trajectory patterns were reproduced, with an additional class demonstrating early post-CLAD FEV1 improvement. Among bilateral recipients, trajectory classes showed a clear risk gradient, including a high-risk class with 100% graft loss and a low-risk class with no early graft loss. A CART model incorporating clinical and spirometric variables demonstrated good discrimination in LTOG bilateral recipients (multiclass AUC 0·85) and consistent class assignment and trajectory patterns when applied to CTOT-20. Interpretation: We identified reproducible, clinically meaningful early post-CLAD FEV1 trajectory phenotypes with differential graft loss risk. These phenotypes and a pragmatic classification tool may support risk stratification, trial enrichment, and improved prognostication for patients and clinicians. Funding: National Institutes of Health, Cystic Fibrosis Foundation.
Source: medRxiv
Authors: Megan L. Neely, Daniel M. Wojdyla, Hwanhee Hong, Peijin Wang, Michaela R. Anderson, Katelyn Arroyo, John A. Belperio, Luke Benvenuto, Marie Budev, Michael P. Combs, Gundeep Dhillon, Jesse Y. Hsu, Laurel Kalman, Tereza Martinu, John McDyer, Michelle Oyster, Krishna Pandya, John M. Reynolds, Jeeyon G. Rim, David W. Roe, Pali D. Shah, Jonathan P. Singer, Lianne G. Singer, Laurie D. Snyder, Wayne Tsuang, S. Samuel Weigt, Jason D. Christie, Scott M. Palmer, Jamie L. Todd
Institutions: Duke University, Duke Medical Center, University of Pennsylvania, University of California, Los Angeles, Columbia University Irving Medical Center, Cleveland Clinic, University of Michigan, Michigan Medicine, Stanford Health Care, Stanford Medicine, Stanford University, University Health Network, University of Toronto, University of Pittsburgh, Indiana University Health, Indiana University Hospital, Johns Hopkins University, University of California, San Francisco
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7155358940
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