Identifying and predicting severe bronchiolitis profiles at high risk for developing asthma: Analysis of three prospective cohorts

Abstract

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.

Keywords: asthma; bronchiolitis; latent class analysis; phenotypes; prediction; virus.

Figure 1

Analytic workflow. The analytic cohort consists of a total of 3,081 infants (age < 1 year) hospitalized with bronchiolitis (severe bronchiolitis) from three multicenter prospective cohort studies: MARC-30 USA, MARC-35, and MARC-30 Finland. Details of the study design, setting, participants, and methods of data collection are summarized in Table E1.; 1 To identify bronchiolitis profiles, we performed a latent class analysis (LCA) using the data of medical history, clinical course, and viral etiology in the three cohorts combined. We selected nine variables based on previous studies and data availability across three cohorts. Then, we optimized the LCA model with the use of the Bayesian information criterion and the mean class membership probability, and identified four mutually-exclusive bronchiolitis profiles.. 1 To examine the longitudinal relationship of the identified bronchiolitis profiles with the risk for developing asthma by age 6-7 years in MARC-35 and MARC-30 Finland, we constructed multivariable logistic regression models adjusting for potential confounders. As infants with a profile B clinically resembled “classic” bronchiolitis and had the largest profile size, this profile served as the reference group. 3 We developed a rule (or a decision tree) to accurately predict the profile at the highest risk for asthma (i.e., profile A) through a binary recursive partitioning analysis in the derivation cohort (MARC-30 USA). Then, we examined the predictive performance in two validation cohorts (MARC-35 and MARC-30 Finland) separately. Abbreviation: MARC, Multicenter Airway Research Collaboration.

Figure 2

Multivariable associations of severe bronchiolitis profiles with risk for developing asthma by age 6,7 years, according to cohorts., * Multivariable-adjusted logistic regression models adjusting for age at baseline, sex, and race/ethnicity in MARC-35 and age at baseline and sex in MARC-30 Finland. No missing data. Arrow indicates that the 95% CI of the odds ratio exceeds the lower limit of the x-axis. Abbreviations: CI, confidence interval; MARC, Multicenter Airway Research Collaboration.

Figure 3

Development and validation of 4-predicter model predicting profile at the highest risk for developing asthma., A. Recursive partitioning analysis. Only the derivation cohort (MARC-30 USA) is shown in the tree. The area of the circle is proportional to the log₂-transformed size of each branch. The darker orange part in the circle represents the proportion of profile A in each branch. A Receiver-operating-characteristic curves. To maximize the sum of sensitivity and specificity for all the possible values of the cut-off point, the Youden index method was applied for the following analysis. B Prediction performance Overall classification counts and characteristics are shown for both the derivation (MARC-30 USA) and validation (MARC-35 and MARC-30 Finland, separately) cohorts.

Figure 4

Development and validation of 5-predicter model predicting profile at the highest risk for developing asthma, A. Recursive partitioning analysis, Only the derivation cohort (MARC-30 USA) is shown in the tree. The area of the circle is proportional to the log₂-transformed size of each branch. The darker orange part in the circle represents the proportion of profile A in each branch. B. Receiver-operating-characteristic curves, To maximize the sum of sensitivity and specificity for all the possible values of the cut-off point, the Youden index method was applied for the following analysis. C. Prediction performance, Overall classification counts and characteristics are shown for both the derivation (MARC-30 USA) and validation (MARC-35 and MARC-30 Finland, separately) cohorts. Abbreviations: AUC, area under the receiver-operating-characteristic curve; CI, confidence interval; MARC, Multicenter Airway Research Collaboration; NPV; negative predictive value; PPV, positive predictive value; RSV, respiratory syncytial virus.