TY - JOUR
T1 - Interactive Pathways of Key Prognostic Factors in Severe Asthma
T2 - A Bayesian Network Comparison of Clinical Trials and Real-World Data
AU - Yadav, Chandra Prakash
AU - Lim, Laura Huey Mien
AU - Price, David
AU - Roy, Rupsa
AU - Juang, Yah Ru
AU - Beasley, Richard
AU - Janson, Christer
AU - Koh, Mariko Siyue
AU - Wang, Eileen
AU - Wechsler, Michael E.
AU - Jackson, David J.
AU - Busby, John
AU - Heaney, Liam G.
AU - Pfeffer, Paul E.
AU - Mahboub, Bassam
AU - Perng, Diahn Warng
AU - Cosio, Borja G.
AU - Perez-de-Llano, Luis
AU - Al-Lehebi, Riyad
AU - Larenas-Linnemann, Désirée
AU - Al-Ahmad, Mona
AU - Rhee, Chin Kook
AU - Iwanaga, Takashi
AU - Heffler, Enrico
AU - Canonica, Giorgio Walter
AU - Costello, Richard
AU - Papadopoulos, Nikolaos G.
AU - Papaioannou, Andriana I.
AU - Porsbjerg, Celeste M.
AU - Torres-Duque, Carlos A.
AU - Christoff, George C.
AU - Popov, Todor A.
AU - Hew, Mark
AU - Peters, Matthew
AU - Gibson, Peter G.
AU - Maspero, Jorge
AU - Bergeron, Celine
AU - Cerda, Saraid
AU - Contreras, Elvia Angelica
AU - Chen, Wenjia
N1 - Publisher Copyright:
© 2026 The Author(s)
PY - 2026/5
Y1 - 2026/5
N2 - Background: The way in which risk predictors combine and contribute to severe asthma exacerbations may differ between clinical trials and real-world settings. Research Question: How do the interactive pathways of risk predictors leading to severe asthma exacerbations compare under clinical trials vs real-world settings? Study Design and Methods: The analysis involved 345 patients with severe asthma from the placebo arms of 2 international randomized controlled trials (RCTs), compared with 6,814 biologic-naïve patients from the International Severe Asthma Registry (ISAR). Sixteen key risk predictors, including demographics, biomarkers, lung function, health care use, exacerbation history, long-term oral corticosteroid use, asthma control, and nasal polyps, were covered. The outcome was the occurrence of severe asthma exacerbations over the 365 days after study enrollment. Bayesian networks (BNs), obtained from machine learning combined with expert knowledge, elucidated significant interplay processes of risk predictors that led to severe asthma exacerbations. External validation was performed in each cohort. Results: The RCTs revealed 44 significant arcs (ie, probabilistic interdependency) between 16 risk factors, whereas the ISAR showed 170. Despite this difference, the main downstream prediction pathways were consistent across both settings, with 2 key pathways: total serum IgE level influenced blood eosinophils to predict future severe exacerbations, and severe exacerbation history directly predicted future severe exacerbations. In external validation, RCT-BN generalized well to ISAR patients (area under the receiver operating characteristic curve, 0.68), whereas ISAR-BN underperformed in RCT patients (area under the receiver operating characteristic curve, 0.50), and ISAR-BN demonstrated better calibration. Interpretation: Our results show that the core pathways predicting severe asthma exacerbations were similar in both RCTs and real-world settings, with comparable predictive performance.
AB - Background: The way in which risk predictors combine and contribute to severe asthma exacerbations may differ between clinical trials and real-world settings. Research Question: How do the interactive pathways of risk predictors leading to severe asthma exacerbations compare under clinical trials vs real-world settings? Study Design and Methods: The analysis involved 345 patients with severe asthma from the placebo arms of 2 international randomized controlled trials (RCTs), compared with 6,814 biologic-naïve patients from the International Severe Asthma Registry (ISAR). Sixteen key risk predictors, including demographics, biomarkers, lung function, health care use, exacerbation history, long-term oral corticosteroid use, asthma control, and nasal polyps, were covered. The outcome was the occurrence of severe asthma exacerbations over the 365 days after study enrollment. Bayesian networks (BNs), obtained from machine learning combined with expert knowledge, elucidated significant interplay processes of risk predictors that led to severe asthma exacerbations. External validation was performed in each cohort. Results: The RCTs revealed 44 significant arcs (ie, probabilistic interdependency) between 16 risk factors, whereas the ISAR showed 170. Despite this difference, the main downstream prediction pathways were consistent across both settings, with 2 key pathways: total serum IgE level influenced blood eosinophils to predict future severe exacerbations, and severe exacerbation history directly predicted future severe exacerbations. In external validation, RCT-BN generalized well to ISAR patients (area under the receiver operating characteristic curve, 0.68), whereas ISAR-BN underperformed in RCT patients (area under the receiver operating characteristic curve, 0.50), and ISAR-BN demonstrated better calibration. Interpretation: Our results show that the core pathways predicting severe asthma exacerbations were similar in both RCTs and real-world settings, with comparable predictive performance.
KW - evidence-based health care
KW - machine learning
KW - respiratory diseases
KW - risk prediction
UR - https://www.scopus.com/pages/publications/105035798491
U2 - 10.1016/j.chest.2026.01.009
DO - 10.1016/j.chest.2026.01.009
M3 - Article
C2 - 41587637
AN - SCOPUS:105035798491
SN - 0012-3692
VL - 169
SP - 1183
EP - 1197
JO - Chest
JF - Chest
IS - 5
ER -