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Published on: November 4, 2010
Preventing unscheduled hospitalisations from asthma: a retrospective cohort study using routine primary and secondary
Nikita Simms-Williams1, Prasad Nagakumar2,3, Rasiah Thayakaran1
1Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
Insights
This study investigates factors linked to asthma hospital admissions in the UK. Findings will help identify high-risk patients and improve asthma management strategies.
Area of Science:
- Respiratory Medicine
- Epidemiology
- Health Services Research
Background:
- Asthma is a prevalent chronic respiratory disease with significant morbidity and mortality.
- The UK faces high asthma-related morbidity and mortality rates despite existing guidelines.
- Novel strategies are needed to prevent asthma attacks and hospital admissions.
Purpose of the Study:
- To examine demographic and clinical factors associated with asthma hospital admissions in children and adults.
- To describe asthma phenotypes in patients requiring hospital care.
- To validate existing asthma risk prediction models for primary care use.
Main Methods:
- Retrospective cohort study using the Clinical Practice Research Datalink (CPRD) Aurum database.
- Primary outcome: asthma-related hospital admissions. Secondary outcomes include oral corticosteroid prescriptions and post-discharge care.
- Statistical analyses include Poisson regression, latent class analysis, and logistic regression for risk model validation.
Main Results:
- Analysis of demographic and clinical factors associated with asthma hospital admissions.
- Identification of distinct asthma phenotypes among high-risk patient groups.
- External validation of existing asthma risk prediction models to identify optimal performers.
Conclusions:
- Understanding risk factors and phenotypes can improve asthma management and reduce hospital admissions.
- Validated risk prediction models can aid in identifying high-risk individuals in primary care.
- This research aims to inform strategies for preventing asthma exacerbations and improving patient outcomes.
Introduction:
Asthma is the most common chronic respiratory disease in children and adults. Asthma results in significant disease-related morbidity, healthcare costs and, in some cases, death. Despite efforts through implementation of national guidelines to improve asthma care, the UK has one of the highest asthma-related morbidity and mortality rates in the western world. New approaches are necessary to prevent asthma attacks in children and adults. The objectives of this study are to assess the association between demographic and clinical factors and asthma-related hospital admissions in children and adults, describe the epidemiology of asthma phenotypes among hospital attenders, and externally validate existing asthma risk prediction models.
Methods And Analysis:
This is a retrospective cohort study of children and adults with asthma. Data will be extracted from the Clinical Practice Research Datalink (CPRD) Aurum database, which holds anonymised primary care data for over 13 million actively registered patients and covers approximately 19% of the UK population. The primary outcome will be asthma-related hospital admissions. The secondary outcomes will be prescriptions of short courses of oral corticosteroids (as a surrogate measure for asthma exacerbations), a composite outcome measure including hospital admissions and prescriptions of short courses of oral corticosteroids and delivery of asthma care management following hospital discharge. The primary analysis will use a Poisson regression model to assess the association between demographic and clinical risk factors and the primary and secondary outcomes. Latent class analysis will be used to identify distinct subgroups, which will further our knowledge on potential phenotypes of asthma among patients at high risk of asthma-related hospital admissions. A Concordance statistic (C-statistic) and logistic regression model will also be used to externally validate existing risk prediction models for asthma-related hospitalisations to allow for the optimal model to be identified and evaluated provide evidence for potential use of the optimal performing risk prediction model in primary care.
Ethics And Dissemination:
This study was approved by the CPRD Independent Scientific Advisory Committee (reference number: 21_000512). Findings from this study will be published in a peer-reviewed journal and disseminated at national and international conferences.
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