ArticleRespiratory research2017
Subtypes of asthma based on asthma control and severity: a latent class analysis.
Article in Respiratory research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review.Journal of asthma and allergy · 2025Review
- Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics.Journal of asthma and allergy · 2024Article
- Association between antibiotics and asthma risk among adults aged over 40 years: a nationally representative retrospective cohort study.BMJ open respiratory research · 2023Article
- Subtypes of Adult-Onset Asthma at the Time of Diagnosis: A Latent Class Analysis.International journal of environmental research and public health · 2023Article
- Asthma control in Kazakhstan: need for urgent action.BMC pulmonary medicine · 2023Article
- Characterizing clinical pediatric obesity subtypes using electronic health record data.PLOS digital health · 2022Article
- Subtypes of Asthma and Cold Weather-Related Respiratory Symptoms.International journal of environmental research and public health · 2022Article
- A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods.Diagnostics (Basel, Switzerland) · 2021Review
- Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.International journal of medical sciences · 2021Review
- Understanding the dynamics of asthma symptoms between childhood and adolescence using latent transition analysis.International journal of public health · 2020Article
- Epidemiology of Asthma in Children and Adults.Frontiers in pediatrics · 2019Review
- Comparison of hypothesis- and data-driven asthma phenotypes in NHANES 2007-2012: the importance of comprehensive data availability.Clinical and translational allergy · 2019Article
- Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol.NPJ primary care respiratory medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAsthma subtyping is a complex new field of study. Usually both etiological and outcome factors of asthma have been used simultaneously for subtyping thus making the interpretation of the results difficult. Identification of subtypes of asthma based on questionnaire data only will be useful for both treatment of asthma and for research. Our objective was to identify asthma subtypes that capture both asthma control and severity based on easily accessible variables.
methodsWe applied latent class analysis for the 1995 adult asthmatics, 692 men and 1303 women, of the Northern Finnish Asthma Study (NoFAS). The classifying variables included use of asthma medication within the last 12 months, St. George's Respiratory Questionnaire score, and asthma-related healthcare use within the last 12 months. Covariates adjusted for included COPD, allergic rhinitis/allergic eczema, BMI, age and sex. All information was based on self-administered questionnaires.
resultsWe identified four subtypes for women: Controlled, mild asthma (41% of participants); Partly controlled, moderate asthma (24%); Uncontrolled asthma, unknown severity (26%), and Uncontrolled, severe asthma (9%). For men we identified three subtypes: Controlled, mild asthma (31%); Poorly controlled asthma, unknown severity (53%); and Partly controlled, severe asthma (17%). For almost 96% of the subjects this subtyping was accurate. The covariates fitted in the model were based on clinical judgment and were good predictors of class membership.
conclusionsOur results show that it is possible to form meaningful and accurate asthma subtypes based on questionnaire data, and that separate classification should be applied for men and women.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.