Performance of Three Asthma Predictive Tools in a Cohort of Infants Hospitalized With Severe Bronchiolitis

Ronaldo C Fabiano Filho; Ruth J Geller; Ludmilla Candido Santos; Janice A Espinola; Lacey B Robinson; Kohei Hasegawa; Carlos A Camargo Jr

doi:10.3389/falgy.2021.758719

Performance of Three Asthma Predictive Tools in a Cohort of Infants Hospitalized With Severe Bronchiolitis

Front Allergy. 2021 Oct 22:2:758719. doi: 10.3389/falgy.2021.758719. eCollection 2021.

Authors

Ronaldo C Fabiano Filho¹, Ruth J Geller¹, Ludmilla Candido Santos¹, Janice A Espinola¹, Lacey B Robinson^{1

2}, Kohei Hasegawa^{1

3}, Carlos A Camargo Jr^{1

2

3}

Affiliations

¹ Emergency Medicine Network, Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States.
² Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Boston, MA, United States.
³ Department of Emergency Medicine, Harvard Medical School, Boston, MA, United States.

Abstract

Childhood asthma develops in 30-40% of children with severe bronchiolitis but accurate prediction remains challenging. In a severe bronchiolitis cohort, we applied the Asthma Predictive Index (API), the modified Asthma Predictive Index (mAPI), and the Pediatric Asthma Risk Score (PARS) to predict asthma at age 5 years. We applied the API, mAPI, and PARS to the 17-center cohort of infants hospitalized with severe bronchiolitis during 2011-2014 (35th Multicenter Airway Research Collaboration, MARC-35). We used data from the first 3 years of life including parent interviews, chart review, and specific IgE testing to predict asthma at age 5 years, defined as parent report of clinician-diagnosed asthma. Among 875/921 (95%) children with outcome data, parent-reported asthma was 294/875 (34%). In MARC-35, a positive index/score for stringent and loose API, mAPI, and PARS were 24, 68, 6, and 55%, respectively. The prediction tools' AUCs (95%CI) ranged from 0.57 (95%CI 0.54-0.59) to 0.68 (95%CI 0.65-0.71). The positive likelihood ratios were lower in MARC-35 compared to the published results from the original cohorts. In this high-risk population of infants hospitalized with severe bronchiolitis, API, mAPI, and PARS had sub-optimal performance (AUC <0.8). Highly accurate (AUC >0.8) asthma prediction tools are desired in infants hospitalized with severe bronchiolitis.

Keywords: asthma; bronchiolitis; children; infancy; predictive tools; wheezing.