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Published on: August 7, 2017
Latent class analysis to identify clinical profiles among indigenous infants with bronchiolitis
Hongqi Niu1, Anne Bernadette Chang1,2, Victor Maduabuchi Oguoma1
1Child Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia.
Insights
Latent class analysis identified five distinct severe bronchiolitis profiles in Australian Indigenous infants. One profile specifically indicated higher risk for bronchiectasis, guiding future targeted interventions.
Area of Science:
- Pediatric Respiratory Medicine
- Infectious Diseases
- Genetics and Genomics
Background:
- Bronchiolitis is a heterogeneous syndrome requiring better phenotyping for targeted interventions.
- Australian Indigenous infants are at higher risk for severe bronchiolitis and subsequent bronchiectasis.
- Accurate phenotyping is crucial for understanding disease trajectories and developing effective treatments.
Purpose of the Study:
- To identify distinct clinical profiles of severe bronchiolitis in hospitalized Australian Indigenous infants.
- To utilize latent class analysis (LCA) to stratify infants based on clinical, viral, and bacterial data.
- To determine if LCA can identify infants at risk for developing bronchiectasis.
Main Methods:
- Prospective collection of clinical, viral, and nasopharyngeal bacterial data from 164 Indigenous infants hospitalized with bronchiolitis.
- Application of multiple correspondence analysis (MCA) followed by latent class analysis (LCA).
- Selection of the best-fitting LCA model using adjusted Bayesian information criteria and entropy R².
Main Results:
- Five distinct clinical profiles of severe bronchiolitis were identified.
- Profile A (23.8%) characterized by prematurity, low birth weight, and prior respiratory issues.
- Profile C (7.0%) represented the most severe disease with oxygen requirement, bronchiectasis, and bacterial detection; Profile E (32.2%) included bronchiectasis, RSV, and bacterial co-infections.
Conclusions:
- Latent class analysis effectively identified five distinct clinical profiles in severe bronchiolitis among Indigenous infants.
- A specific profile was associated with a higher likelihood of bronchiectasis.
- These identified phenotypes can inform the development of targeted interventions for severe bronchiolitis and future bronchiectasis risk.
Objective:
Better phenotyping of the heterogenous bronchiolitis syndrome may lead to targeted future interventions. This study aims to identify severe bronchiolitis profiles among hospitalized Australian Indigenous infants, a population at risk of bronchiectasis, using latent class analysis (LCA).
Methods:
We included prospectively collected clinical, viral, and nasopharyngeal bacteria data from 164 Indigenous infants hospitalized with bronchiolitis from our previous studies. We undertook multiple correspondence analysis (MCA) followed by LCA. The best-fitting model for LCA was based on adjusted Bayesian information criteria and entropy R2 .
Results:
We identified five clinical profiles. Profile-A's (23.8% of cohort) phenotype was previous preterm (90.7%), low birth-weight (89.2%) and weight-for-length z-score <-1 (82.7% from combining those with z-score between -1 and -2 and those in the z-score of <-2 group) previous respiratory hospitalization (39.6%) and bronchiectasis on chest high-resolution computed tomography scan (35.4%). Profile-B (25.3%) was characterized by the oxygen requirement (100%) and marked accessory muscle use (45.5%). Infants in profile-C (7.0%) had the most severe disease, with oxygen requirement and bronchiectasis in 100%, moderate accessory muscle use (85% vs 0%-51.4%) and bacteria detected (93.1% vs 56.7%-72.0%). Profile-D (11.6%) was dominated by rhinovirus (49.4%), mild accessory muscle use (73.8%), and weight-for-length z-score <-2 (36.0%). Profile-E (32.2%) included bronchiectasis (13.8%), RSV (44.0%), rhinovirus (26.3%) and any bacteria (72%).
Conclusion:
Using LCA in Indigenous infants with severe bronchiolitis, we identified five clinical profiles with one distinct profile for bronchiectasis. LCA can characterize distinct phenotypes for severe bronchiolitis and infants at risk for future bronchiectasis, which may inform future targeted interventions.
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