JHLT open · 2026
Research priorities in chronic lung allograft dysfunction: A machine learning-assisted bibliometric analysis.
СтатьяНазвание и аннотация: Перевод в очереди
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El Husseini Kinan, Delin Margot, Decaux Sixtine, Goletto Tiphaine, Morer Lise, Mouren Domitille, Salpin Mathilde, Weisenburger Gaëlle, Coiffard Benjamin, Merveilleux du Vignaux Claire, Le Pavec Jérôme, Renaud-Picard Benjamin, Roux Antoine, Tissot Adrien, Villeneuve Thomas, Roussel Arnaud, Mordant Pierre, Brugière Olivier, Mal Hervé, Bunel Vincent
Аннотация
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.
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.