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. 2025 Aug;168(2):301-316.
doi: 10.1016/j.chest.2025.04.046. Epub 2025 May 19.

Prediction Pathway for Severe Asthma Exacerbations: A Bayesian Network Analysis

Chandra Prakash Yadav  1 , Atlanta Chakraborty  2 , David B Price  3 , Laura Huey Mien Lim  1 , Yah Ru Juang  1 , Richard Beasley  4 , Mohsen Sadatsafavi  5 , Christer Janson  6 , Mariko Koh Siyue  7 , Eileen Wang  8 , Michael E Wechsler  9 , David J Jackson  10 , John Busby  11 , Liam G Heaney  12 , Paul E Pfeffer  13 , Bassam Mahboub  14 , Diahn-Warng Perng  15 , Borja G Cosio  16 , Luis Perez-de-Llano  17 , Riyad Al-Lehebi  18 , Désirée Larenas-Linnemann  19 , Mona S Al-Ahmad  20 , Chin Kook Rhee  21 , Takashi Iwanaga  22 , Enrico Heffler  23 , Giorgio Walter Canonica  23 , Richard W Costello  24 , Nikolaos G Papadopoulos  25 , Andriana I Papaioannou  26 , Celeste M Porsbjerg  27 , Carlos A Torres-Duque  28 , George C Christoff  29 , Todor A Popov  30 , Mark Hew  31 , Matthew J Peters  32 , Peter G Gibson  33 , Jorge Máspero  34 , Celine Bergeron  35 , Saraid Cerda  36 , Elvia Angelica Contreras  37 , Wenjia Chen  38
Affiliations

Prediction Pathway for Severe Asthma Exacerbations: A Bayesian Network Analysis

Chandra Prakash Yadav et al. Chest. 2025 Aug.

Abstract

Background: Accurate risk prediction of exacerbations is pivotal in severe asthma management. Multiple risk factors are at play, but the pathway of risk prediction remains unclear.

Research question: How do the interplays of clinically relevant predictors lead to severe exacerbations in patients with severe asthma?

Study design and methods: Patients with severe asthma (n = 6,814, aged ≥ 18 years), biologic naive, were identified from the Severe Asthma Registry (2017-2021). Relevant predictors covered demographics, lung function, inflammation biomarkers, health care use, medications, exacerbation history, and comorbidities. A Bayesian network, representing the prediction process of severe exacerbations, was obtained by combining expert knowledge and machine learning algorithms. Internal validation was performed. The proposed influence diagram integrated decision and utility nodes into the prediction pathway.

Results: The Bayesian network analysis revealed that blood eosinophil count, fractional exhaled nitric oxide level, and FEV1 directly influenced the transition between prior and future severe exacerbations. The presence of chronic rhinosinusitis indirectly affected such transition by directly influencing blood eosinophil count, fractional exhaled nitric oxide, and % predicted FEV1. Macrolide use independently affected history of exacerbations to influence future severe asthma exacerbations. Model discrimination was moderate in 10-fold cross-validation and leave-1-country-out cross-validation, and model calibration was high in train-test data.

Interpretation: This study identified an essential prediction pathway of severe exacerbation, which involves the influence of chronic rhinosinusitis on the immediate predictors of risk transition from current to future severe asthma exacerbations. Macrolide use was another essential prediction pathway identified. The findings support shared clinical decision-making in severe asthma treatment.

Keywords: Bayesian network; asthma; causal prediction; influence diagram; model validation; risk prediction; severe exacerbation.

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Conflict of interest statement

Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: D. B. P. has advisory board membership with Amgen, AstraZeneca (AZ), Boehringer Ingelheim (BI), Chiesi, Circassia, Mylan, Mundipharma, Novartis, Regeneron Pharmaceuticals, Sanofi Genzyme, Teva Pharmaceuticals, and Thermo Fisher; consultancy agreements with Amgen, AZ, BI, Chiesi, GlaxoSmithKline (GSK), Mylan, Mundipharma, Novartis, Pfizer, Teva Pharmaceuticals, and Theravance; grants and unrestricted funding for investigator-initiated studies (conducted through Observational and Pragmatic Research Institute Pte Ltd) from AZ, BI, Chiesi, Circassia, Mylan, Mundipharma, Novartis, Pfizer, Regeneron Pharmaceuticals, Respiratory Effectiveness Group, Sanofi Genzyme, Teva Pharmaceuticals, Theravance, and the UK National Health Service; payment for lectures/speaking engagements from AZ, BI, Chiesi, Cipla, GSK, Kyorin, Mylan, Mundipharma, Novartis, Regeneron Pharmaceuticals, Sanofi Genzyme, and Teva Pharmaceuticals; payment for the development of educational materials from Mundipharma and Novartis; payment for travel/accommodation/meeting expenses from AZ, BI, Mundipharma, Mylan, Novartis, and Thermo Fisher; funding for patient enrollment or completion of research from Novartis; stock/stock options from AKL Research and Development Ltd, which produces phytopharmaceuticals; owns 74% of the social enterprise Optimum Patient Care Ltd (Australia and UK) and 74% of Observational and Pragmatic Research Institute Pte Ltd (Singapore); 5% shareholding in Timestamp, which develops adherence monitoring technology; is peer reviewer for grant committees of the Efficacy and Mechanism Evaluation Programme, and Health Technology Assessment; and was an expert witness for GSK. M. S. has received honoraria from AZ, BI, Teva, and GSK for purposes unrelated to the content of this manuscript; and has received research funding from AZ and BI directly into his research account from AZ for unrelated projects. E. W. has received honoraria from AZ, GSK, and Genentech, and has been an investigator in studies sponsored by AZ, GSK, Genentech, Sanofi, Novartis, and Teva, for which her institution has received funding. M. E. W. reports grants and/or personal fees from Novartis, Sanofi, Regeneron, Genentech, Sentien, resTORbio, Equillium, Genzyme, Cohero Health, Teva, BI, AZ, Amgen, GSK, Cytoreason, Cerecor, Sound Biologics, Incyte, and Kinaset. D. J. J. has received speaker fees and consultancy fees from AZ, GSK, Sanofi Regeneron, and BI; and has received research funding from AZ. J. B. has received research grants from AZ and personal fees from NuvoAir, outside the submitted work. L. G. H. has received grant funding, participated in advisory boards, and given lectures at meetings supported by Amgen, AZ, BI, Chiesi, Circassia, Hoffmann-La Roche, GSK, Novartis, Theravance, Evelo Biosciences, Sanofi, and Teva; has received grants from MedImmune, Novartis UK, Roche/Genentech Inc, Amgen, Genentech/Hoffman-La Roche, AZ, MedImmune, GSK, Aerocrine, and Vitalograph; has received sponsorship for attending international scientific meetings from AZ, BI, Chiesi, GSK, and Napp Pharmaceuticals; has also taken part in asthma clinical trials sponsored by AZ, BI, Hoffmann-La Roche, and GSK, for which his institution received remuneration; and is the Academic Lead for the Medical Research Council Stratified Medicine UK Consortium in Severe Asthma, which involves industrial partnerships with a number of pharmaceutical companies including Amgen, AZ, BI, GSK, Hoffmann-La Roche, and Janssen. P. E. P. has attended advisory boards for AZ, GSK, and Sanofi; has given lectures at meetings supported by AZ and GSK; has taken part in clinical trials sponsored by AZ, GSK, Novartis, and Sanofi, for which his institution received remuneration; and has a current research grant funded by GSK. D.-W. P. received sponsorships to attend or speak at international meetings, honoraria for lecturing or attending advisory boards, and research grants from the following companies: AZ, BI, GSK, Novartis, Daiichi Sankyo, Shionogi, and Orient Pharma. B. G. C. declared grants from Chiesi and GSK; personal fees for advisory board activities from Chiesi, GSK, Novartis, Sanofi, Teva, and AZ; and payment for lectures/speaking engagements from Chiesi, Novartis, GSK, Menarini, and AZ, outside the submitted work. L. P.-d.-L. reports grants, personal fees, and nonfinancial support from AZ; personal fees and nonfinancial support from GSK; grants, personal fees, and nonfinancial support from TEVA; personal fees and nonfinancial support from Chiesi; grants, personal fees, and nonfinancial support from Sanofi; personal fees from MSD; personal fees from Techdow Pharma; grants, personal fees, and nonfinancial support from FAES; personal fees from Leo-Pharma; grants and personal fees from GEBRO; and personal fees from GILEAD, outside the submitted work. R. A.-L. has given lectures at meetings supported by AZ, BI, Novartis, GSK, and Sanofi; and participated in advisory board fees from GSK, AZ, Novartis, and Abbot. D. L. L. reports personal fees from ALK-Abelló, AZ (national and global), Bayer, Chiesi, Grunenthal, Grin, GSK (national and global), Viatris, Menarini, MSD, Novartis, Pfizer, Sanofi, Siegfried, UCB, and Carnot; and grants from AbbVie, Bayer, Lilly, Sanofi, AZ, Pfizer, Novartis, Circassia, UCB, and GSK, outside the submitted work. M. S. A.-A. has received advisory board and speaker fees from AZ, Sanofi, Novartis, and GSK; and received a grant from the Kuwait Foundation for the Advancement of Sciences (KFAS). C. K. R. has received consulting/lecture fees from MSD, AZ, GSK, Novartis, Takeda, Mundipharma, BI, Teva, Sanofi, and Bayer. T. I. received lecture fees from Kyorin, GSK, Novartis, BI, and AZ. E. H. declares personal fees from Sanofi, Regeneron, GSK, Novartis, AZ, Stallergenes, and Circassia. G. W. C. has received research grants, as well as lecture or advisory board fees, from A. Menarini, ALK-Abelló, Allergy Therapeutics, Anallergo, AZ, MedImmune, BI, Chiesi Farmaceutici, Circassia, Danone, Faes, Genentech, Guidotti Malesci, GSK, Hal Allergy, Merck, MSD, Mundipharma, Novartis, Orion, Sanofi Aventis, Sanofi, Genzyme/Regeneron, Stallergenes, UCB Pharma, Uriach Pharma, Teva, Thermo Fisher, and Valeas. R. W. C. has received honoraria for lectures from Aerogen, AZ, BI, GSK, Novartis, and Teva; is a member of advisory boards for GSK and Novartis; has received grant support from GSK and Aerogen; and has patents in the use of acoustics in the diagnosis of lung disease, assessment of adherence, and prediction of exacerbations. N. G. P. has been a speaker and/or advisory board member for Abbott, AbbVie, ALK, Asit Biotech, AZ, Biomay, BI, GSK, HAL, Faes Farma, Medscape, Menarini, MSD, Novartis, Nutricia, OM Pharma, Regeneron, Sanofi, Takeda, and Viatris. A. I. P. has received fees and honoraria from Menarini, GSK, Novartis, Elpen, BI, AZ, and Chiesi. C. M. P. has attended advisory boards for AZ, Novartis, TEVA, and Sanofi-Genzyme; has given lectures at meetings supported by AZ, Novartis, TEVA, Sanofi-Genzyme, and GSK; has taken part in clinical trials sponsored by AZ, Novartis, MSD, Sanofi-Genzyme, GSK, and Novartis; and has received educational and research grants from AZ, Novartis, TEVA, GSK, ALK, and Sanofi-Genzyme. C. A. T.-D. has received fees as advisory board participant and/or speaker from AZ, BI, GSK, Novartis, and Sanofi-Aventis; has taken part in clinical trials from AZ, Novartis, and Sanofi-Aventis; and has received unrestricted grants for investigator-initiated studies at Fundación Neumológica Colombiana from AZ, BI, GSK, Grifols, and Novartis. T. A. P. declares relevant research support from Novartis and Chiesi Pharma. M. H. declares grants and other advisory board fees (made to his institutional employer) from AZ, GSK, Novartis, Sanofi, Teva, and Seqirus, for unrelated projects. M. J. P. declares personal fees and nonfinancial support from AZ and GSK. P. G. G. has received speaker fees and grants to his institution from AZ, GSK, and Novartis. J. M. reports speaker fees, grants, or advisory boards for AZ, Sanofi, GSK, Novartis, Inmunotek, Menarini, and Noucor. C. B. reports advisory board participation of Sanofi, AZ, Takeda, and Valeo Pharma; and honoraria for presentations for GSK, AZ, Amgen, Grifols, Sanofi, Regeneron, and Valeo Pharma. S. C. declares receiving conference fees from Novartis SA de CV, GSK Mexico, AZ Mexico, and Sanofi Mexico. None declared (C. P. Y., A. C., L. H. M. L., Y. R. J., R. B., C. J., M. K. S., B. M., G. C. C., E. A. C., W. C.).

Figures

Figure 1
Figure 1
Patient flow diagram of the International Severe Asthma Registry (ISAR) cohort.
Figure 2
Figure 2
Bayesian network plots. A, Initial parent-child relationship learned; B, machine learning with expert knowledge integration; C, final model with clinically relevant arcs. BEC = blood eosinophil count; CRS = chronic rhinosinusitis; ER = emergency room; Feno = fractional exhaled nitric oxide; LAMA = long-acting muscarinic antagonist.
Figure 2
Figure 2
Bayesian network plots. A, Initial parent-child relationship learned; B, machine learning with expert knowledge integration; C, final model with clinically relevant arcs. BEC = blood eosinophil count; CRS = chronic rhinosinusitis; ER = emergency room; Feno = fractional exhaled nitric oxide; LAMA = long-acting muscarinic antagonist.
Figure 3
Figure 3
A-C, Conditional probability and counterfactual analysis. BEC = blood eosinophil count; CRS = chronic rhinosinusitis; CRS_NP = chronic rhinosinusitis with nasal polyps; CRSwoNP = chronic rhinosinusitis without nasal polyps; Feno = fractional exhaled nitric oxide.
Figure 3
Figure 3
A-C, Conditional probability and counterfactual analysis. BEC = blood eosinophil count; CRS = chronic rhinosinusitis; CRS_NP = chronic rhinosinusitis with nasal polyps; CRSwoNP = chronic rhinosinusitis without nasal polyps; Feno = fractional exhaled nitric oxide.
Figure 4
Figure 4
Influence diagram integrating nodes, cost, and utility. BEC = blood eosinophil count; CRSNP = chronic rhinosinusitis with nasal polyps; EXB = (severe) exacerbation; Feno = fractional exhaled nitric oxide.
See this image and copyright information in PMC

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