ArticleBMC medical informatics and decision making2022
Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2024Pooled it
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Stage-dependent interaction between circulating and tissue-resident memory CD4Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
- AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).NPJ digital medicine · 2026Article
- The Evolving Role of Artificial Intelligence in Pediatric Asthma Management: Opportunities and Challenges for Modern Healthcare.Journal of personalized medicine · 2026Review
- Multimodal EHR-Based Prediction of Pediatric Asthma Exacerbations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Exacerbation of Asthma Among Pediatric Patients Presenting to the Emergency Department.Journal of clinical medicine · 2025Review
- Using mHealth to Predict Asthma Exacerbations in Children and Adolescents (Mobile Health for Kids With Asthma): Protocol for an Observational Study.JMIR research protocols · 2025Article
- Review
- Prediction of daily childhood asthma exacerbation from ambient meteorological, environmental risk factors and respiratory viruses, Philadelphia, PA, 2011 to 2016.Environmental science and pollution research international · 2025Article
- Article
- Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review.JMIR AI · 2024Article
- Investigating the impact of extreme weather events and related indicators on cardiometabolic multimorbidity.Archives of public health = Archives belges de sante publique · 2024Article
- Avoidance of Inhaled Pollutants and Irritants in Asthma from a Salutogenic Perspective.Journal of asthma and allergy · 2024Review
- Prediction of Asthma Exacerbations in Children.Journal of personalized medicine · 2023Review
- Article
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
- Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England.Pragmatic and observational research · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAsthma exacerbations are triggered by a variety of clinical and environmental factors, but their relative impacts on exacerbation risk are unclear. There is a critical need to develop methods to identify children at high-risk for future exacerbation to allow targeted prevention measures. We sought to evaluate the utility of models using spatiotemporally resolved climatic data and individual electronic health records (EHR) in predicting pediatric asthma exacerbations.
methodsWe extracted retrospective EHR data for 5982 children with asthma who had an encounter within the Duke University Health System between January 1, 2014 and December 31, 2019. EHR data were linked to spatially resolved environmental data, and temporally resolved climate, pollution, allergen, and influenza case data. We used xgBoost to build predictive models of asthma exacerbation over 30-180 day time horizons, and evaluated the contributions of different data types to model performance.
resultsModels using readily available EHR data performed moderately well, as measured by the area under the receiver operating characteristic curve (AUC 0.730-0.742) over all three time horizons. Inclusion of spatial and temporal data did not significantly improve model performance. Generating a decision rule with a sensitivity of 70% produced a positive predictive value of 13.8% for 180 day outcomes but only 2.9% for 30 day outcomes.
conclusionsEHR data-based models perform moderately wellover a 30-180 day time horizon to identify children who would benefit from asthma exacerbation prevention measures. Due to the low rate of exacerbations, longer-term models are likely to be most clinically useful.
trial registrationNot applicable.
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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.