Research priorities in chronic lung allograft dysfunction: A machine learning-assisted bibliometric analysis
Year: 2026
Type: article
Abstract: Background: Chronic lung allograft dysfunction (CLAD), the leading cause of death beyond year one post-transplant, encompasses bronchiolitis obliterans syndrome (BOS), restrictive allograft syndrome (RAS), and mixed allograft syndrome (MAS), formalized by the 2019 ISHLT consensus. How thematic priorities have evolved and whether research effort tracks phenotype burden have not been systematically examined. Methods: From 1,851 CLAD-related papers (Web of Science, 1990-2025) assembled after AI-assisted relevance screening (validated against blinded expert adjudication), machine learning-based topic modeling using a biomedical language model identified thematic clusters; temporal trajectories were classified by statistical trend analysis; and a phenotype representation index (RI = corpus proportion ÷ clinical prevalence) was computed against 2 reference cohorts with bootstrap confidence intervals. Results: Twenty-four thematic clusters were identified (13 declining, 8 stable, 3 rising, 1 emerging). Rising topics included DSA/antibody-mediated rejection (119 papers), restrictive phenotype classification (126 papers), and translational CLAD pathogenesis (143 papers), reflecting momentum predating the 2019 consensus. Donor-derived cell-free DNA was the sole emerging topic (21 papers). Among 950 phenotype-focused papers, BOS was proportionally represented (RI 1.13 vs Leuven; 1.05 vs Toronto). RAS representation was denominator-dependent (RI 0.75-0.99). MAS was substantially under-represented in both cohorts (RI 0.33 vs Leuven; 0.41 vs Toronto). Conclusions: Transformer-based topic modeling reveals a directional shift toward immunological mechanisms and phenotype-specific research that classical keyword approaches cannot resolve. Research effort is broadly proportionate to clinical burden for BOS but substantially below for MAS, a gap possibly attributable to pathophysiological differences, diagnostic complexity, limited case volume, and recency of phenotype formalization that multicenter studies may address.
Source: JHLT Open
Authors: Kinan El Husseini, Margot Delin, Sixtine Decaux, Tiphaine Goletto, Lise Morer, Domitille Mouren, Mathilde Salpin, Gaëlle Weisenburger, Benjamin Coiffard, Claire Merveilleux du Vignaux, Jérôme Le Pavec, Benjamin Renaud-Picard, Antoine Roux, Adrien Tissot, Thomas Villeneuve, Arnaud Roussel, Pierre Mordant, Olivier Brugière, Hervé Mal, Vincent Bunel
Institutions: Inserm, Université Paris Cité, Assistance Publique – Hôpitaux de Paris, Centre de Recherche sur l'Inflammation, Hôpital Bichat-Claude-Bernard, Aix-Marseille Université, Hôpital Nord, Hospices Civils de Lyon, Hôpital Louis Pradel, Hôpital Marie Lannelongue, Hôpital Paris Saint-Joseph, Hôpitaux Universitaires de Strasbourg, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Virologie et Immunologie Moléculaires, Paris Cardiovascular Research Center, Hôpital Foch, Centre Hospitalier Universitaire de Nantes, Institut du Thorax, Center for Research in Transplantation and Translational Immunology, Nantes Université, Centre National de la Recherche Scientifique, Centre Hospitalier Universitaire de Toulouse, Institut Toulousain des Maladies Infectieuses et Inflammatoires, Université de Toulouse
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W7171244170