Fujiogi, M., Dumas, O., Hasegawa, K., Jartti, T., & Camargo, C. A. (2022). Identifying and predicting severe bronchiolitis profiles at high risk for developing asthma: Analysis of three prospective cohorts. EClinicalMedicine, 43, 101257. https://doi.org/10.1016/j.eclinm.2021.101257
Identifying and predicting severe bronchiolitis profiles at high risk for developing asthma : analysis of three prospective cohorts
|Author:||Fujiogi, Michimasa1; Dumas, Orianne2; Hasegawa, Kohei1;|
1Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, 125 Nashua Street, Suite 920, Boston, MA 02114-1101, USA
2Équipe d'Épidémiologie Respiratoire Intégrative, Université Paris-Saclay, UVSQ, Université Paris-Sud, Inserm, CESP, Villejuif 94807, France
3PEDEGO Research Unit, Medical Research Center, University of Oulu, Oulu, Finland
4Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, Oulu, Finland
5Department of Pediatrics and Adolescent Medicine, University of Turku and Turku University Hospital, Turku, Finland
|Online Access:||PDF Full Text (PDF, 1.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022060141599
|Publish Date:|| 2022-06-29
Background: Bronchiolitis is the leading cause of infants hospitalization in the U.S. and Europe. Additionally, bronchiolitis is a major risk factor for the development of childhood asthma. Growing evidence suggests heterogeneity within bronchiolitis. We sought to identify distinct, reproducible bronchiolitis subgroups (profiles) and to develop a decision rule accurately predicting the profile at the highest risk for developing asthma.
Methods: In three multicenter prospective cohorts of infants (age < 12 months) hospitalized for bronchiolitis in the U.S. and Finland (combined n = 3081) in 2007–2014, we identified clinically distinct bronchiolitis profiles by using latent class analysis. We examined the association of the profiles with the risk for developing asthma by age 6–7 years. By performing recursive partitioning analyses, we developed a decision rule predicting the profile at highest risk for asthma, and measured its predictive performance in two separate cohorts.
Findings: We identified four bronchiolitis profiles (profiles A–D). Profile A (n = 388; 13%) was characterized by a history of breathing problems/eczema and non–respiratory syncytial virus (non-RSV) infection. In contrast, profile B (n = 1064; 34%) resembled classic RSV-induced bronchiolitis. Profile C (n = 993; 32%) was comprised of the most severely ill group. Profile D (n = 636; 21%) was the least-ill group. Profile A infants had a significantly higher risk for asthma, compared to profile B infants (38% vs. 23%, adjusted odds ratio [adjOR] 2⋅57, 95%confidence interval [CI] 1⋅63–4⋅06). The derived 4-predictor (RSV infection, history of breathing problems, history of eczema, and parental history of asthma) decision rule strongly predicted profile A—e.g., area under the curve [AUC] of 0⋅98 (95%CI 0⋅97–0⋅99), sensitivity of 1⋅00 (95%CI 0⋅96–1⋅00), and specificity of 0⋅90 (95%CI 0⋅89–0⋅93) in a validation cohort.
Interpretation: In three prospective cohorts of infants with bronchiolitis, we identified clinically distinct profiles and their longitudinal relationship with asthma risk. We also derived and validated an accurate prediction rule to determine the profile at highest risk. The current results should advance research into the development of profile-specific preventive strategies for asthma.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3121 General medicine, internal medicine and other clinical medicine
3123 Gynaecology and paediatrics
This study was supported by grants from the National Institutes of Health (Bethesda, MD): U01 AI-067693, U01 AI-087881, R01 AI-127507, R01 AI-134940, and R01 AI-137091, and UG3/UH3 OD-023253.
© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)