Severe bronchial asthma: probability of frequent exacerbations considering occupational exposure

UDC: 
616.248: 614.256.5
DOI: 
10.21668/health.risk/2026.2.11.eng
Authors: 

A.I. Borisova1, E.S. Galimova2, E.R. Abdrakhmanova1,2, A.B. Bakirov1,2, I.I. Zaidullin1, E.F. Kabirova1, D.O. Karimov1, A.A. Distanova1, E.T. Valeeva1,2, Yu.G. Aznabaeva1,2

Organization: 

1Ufa Research Institute of Occupational Health and Human Ecology, 94 Stepana Kuvykina Str., Ufa, 450106, Russian Federation
2Bashkir State Medical University, 3 Lenina Str., Ufa, 450008, Russian Federation

Abstract: 

Bronchial asthma remains one of the most common chronic inflammatory diseases of the airways and represents a sig-nificant medical and social problem due to highly frequent exacerbations, hospitalizations, and declining quality of life. Severe asthma is a particularly challenging clinical condition, in which frequent exacerbations (three or more episodes per year) place a major burden on the healthcare system and are associated with adverse outcomes. Therefore, development of scales for assessing the probability of frequent exacerbations based on accessible clinical, functional, and occupational parameters is a relevant task for identifying patient groups requiring more detailed examination and follow-up by specialized physicians.

The aim of this study was to develop a scale for assessing the probability of frequent exacerbations in patients with severe bronchial asthma. We analyzed the examination results of 174 patients with severe bronchial asthma aged 18 to 71 years. The modeling outcome was defined as “frequent exacerbations” as three or more episodes during the previous year. A hybrid machine-learning algorithm was used to construct the model: gradient boosting was applied to select the most significant predictors. The list of predictors included sex, age, duration of bronchial asthma, occupational exposure, body mass index, gastroesophageal reflux disease, eosinophil level, and FEV1. Model validation was performed using 5-fold stratified cross-validation.

The key factors associated with the probability of frequent exacerbations were occupational exposure to allergens or irritants in the workplace, reduced pulmonary function, and presence of comorbid pathology. Stratification by tertiles of predicted probability made it possible to identify groups with low, moderate, and high probability of frequent exacerbations. According to the developed scale, the combination of FEV1 < 40 %, gastroesophageal reflux disease, occupational exposure, and a high eosinophil level increased the probability of frequent exacerbations up to 97 %. The results were visualized as a heat map.

We proposed a scale for assessing the probability of frequent exacerbations in patients with severe bronchial asthma, intended primarily for primary care physicians, including therapists and general practitioners. Its application is aimed at timely identification of patients with moderate and high probability of severe bronchial asthma exacerbations for subsequent referral to specialized physicians, including allergists-immunologists and pulmonologists. This approach may contribute to reducing hospitalization rates and may have significant pharmacoeconomic relevance.

Keywords: 
severe bronchial asthma, frequent exacerbations, exacerbation probability, occupational exposure, machine learning, personalized medicine
Borisova A.I., Galimova E.S., Abdrakhmanova E.R., Bakirov A.B., Zaidullin I.I., Kabirova E.F., Karimov D.O., Distanova A.A., Valeeva E.T., Aznabaeva Yu.G. Severe bronchial asthma: probability of frequent exacerbations considering occupational exposure. Health Risk Analysis, 2026, no. 2, pp. 116–124. DOI: 10.21668/health.risk/2026.2.11.eng
References: 
  1. Antonov N.S., Sakharova G.M., Rusakova L.I., Salagay O.O. Dynamics of the incidence of respiratory diseases among the population of the Russian Federation in 2010–2022. Meditsina, 2023, vol. 11, no. 3, pp. 1–17 (in Russian).
  2. Global strategy for asthma management and prevention (2024 update). Global Initiative for Asthma. Available at: https://ginasthma.org/wp-content/uploads/2024/05/GINA-2024-Strategy-Repo... (March 23, 2026).
  3. Paredes M., Osorio J., García de la Fuente A., Rodríguez E., Picado C., Ojanguren I., Arismendi E. Severe asthma exacerbations: from risk factors to precision management strategies. J. Clin. Med., 2026, vol. 15, no. 2, pp. 857. DOI: 10.3390/jcm15020857
  4. Busse W.W. Consequences of severe asthma exacerbations. Curr. Opin. Allergy Clin. Immunol., 2023, vol. 23, no. 1, pp. 44–50. DOI: 10.1097/ACI.0000000000000870
  5. Liu A., Zhang Y., Yadav C.P., Chen W. An updated systematic review on asthma exacerbation risk prediction models between 2017 and 2023: risk of bias and applicability. J. Asthma Allergy, 2025, vol. 18, pp. 579–589. DOI: 10.2147/JAA.S509260
  6. Kaur R., Chupp G. Phenotypes and endotypes of adult asthma: moving toward precision medicine. J. Allergy Clin. Immunol., 2019, vol. 144, no. 1, pp. 1–12. DOI: 10.1016/j.jaci.2019.05.031
  7. Couillard S., Laugerud A., Jabeen M., Ramakrishnan S., Melhorn J., Hinks T., Pavord I. Derivation of a prototype asthma attack risk scale centred on blood eosinophils and exhaled nitric oxide. Thorax, 2022, vol. 77, no. 2, pp. 199–202. DOI: 10.1136/thoraxjnl-2021-217325
  8. Meulmeester F.L., Mailhot-Larouche S., Celis-Preciado C., Lemaire-Paquette S., Ramakrishnan S., Wechsler M.E., Brusselle G., Corren J. [et al.]. Inflammatory and clinical risk factors for asthma attacks (ORACLE2): a patient-level meta-analysis of control groups of 22 randomised trials. Lancet Respir. Med., 2025, vol. 13, no. 6, pp. 505–516. DOI: 10.1016/S2213-2600(25)00037-2
  9. Tibble H., Sheikh A., Tsanas A. Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care. NPJ Prim. Care Respir. Med., 2025, vol. 35, no. 1, pp. 24. DOI: 10.1038/s41533-025-00428-8
  10. Shkitin S.O., Bereznikov A.V., Berseneva E.A., Onufriychuk Y.O. The methodology of prognostication of severe ex-acerbation of bronchial asthma and asthmatic status. Problemy sotsial'noi gigieny, zdravookhraneniya i istorii meditsiny, 2021, vol. 29, no. 6, pp. 1556–1562. DOI: 10.32687/0869-866X-2021-29-6-1556-1562 (in Russian).
  11. Kravchenko N.Yu., Molostova T.N., Belevsky A.S., Makaryants N.N., Kuneevskaya I.V., Gaychieva Z.N. Specific characteristics of exacerbation development in patients with different phenotypes of severe asthma. RMZh. Meditsinskoe obozrenie, 2023, vol. 7, no. 2, pp. 96–102. DOI: 10.32364/2587-6821-2023-7-2-96-102 (in Russian).
  12. Thomson N.C. Frequent exacerbators in severe asthma: focus on clinical and transcriptional factors. Clin. Transl. Med., 2022, vol. 12, no. 5, pp. e860. DOI: 10.1002/ctm2.860
  13. Kupczyk M., ten Brinke A., Sterk P.J., Bel E.H., Papi A., Chanez P., Nizankowska-Mogilnicka E., Gjomarkaj M. [et al.]. Frequent exacerbators – a distinct phenotype of severe asthma. Clin. Exp. Allergy, 2014, vol. 44, no. 2, pp. 212–221. DOI: 10.1111/cea.12179
  14. Yang F., Busby J., Heaney L.G., Menzies-Gow A., Pfeffer P.E., Jackson D.J., Mansur A.H., Siddiqui S. [et al.]. Fac-tors associated with frequent exacerbations in the UK severe asthma registry. J. Allergy Clin. Immunol. Pract., 2021, vol. 9, no. 7, pp. 2691–2701.e1. DOI: 10.1016/j.jaip.2020.12.062
  15. Katser A.B., Demko I.V., Sobko E.A., Grishkov A.V. Esophageal pathology and bronchial asthma: pathogenetic in-teractions and possibilities for therapy optimization. Profilakticheskaya meditsina, 2024, vol. 27, no. 11, pp. 129–134. DOI: 10.17116/profmed202427111129 (in Russian).
  16. Yadav C.P., Chakraborty A., Price D.B., Lim L.H.M., Juang Y.R., Beasley R., Sadatsafavi M., Janson C. [et al.]. Pre-diction pathway for severe asthma exacerbations: a Bayesian Network analysis. Chest, 2025, vol. 168, no. 2, pp. 301–316. DOI: 10.1016/j.chest.2025.04.046
  17. Collins G.S., Moons K.G.M., Dhiman P., Riley R.D., Beam A.L., Van Calster B., Ghassemi M., Liu X. [et al.].
    TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 2024, vol. 385, pp. e078378. DOI: 10.1136/bmj-2023-078378
  18. Moons K.G.M., Wolff R.F., Riley R.D., Whiting P.F., Westwood M., Collins G.S., Reitsma J.B., Kleijnen J., Mallett S. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann. Intern. Med., 2019, vol. 170, no. 1, pp. W1–W33. DOI: 10.7326/M18-1377
  19. Luchinin A.S. Prognostic Models in Medicine. Klinicheskaya onkogematologiya. Fundamental'nye issledovaniya i klinicheskaya praktika, 2023, vol. 16, no. 1, pp. 27–36. DOI: 10.21320/2500-2139-2023-16-1-27-36
Received: 
24.04.2026
Approved: 
04.06.2026
Accepted for publication: 
27.06.2026

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