ArticleAnnals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology2023
A prediction model for asthma exacerbations after stopping asthma biologics.
Article in Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Cohort vs case-control design for transformer-based prediction of asthma exacerbations in mild asthma.NPJ digital medicine · 2026Article
- Discontinuation of biologic therapy in severe asthma: Evidence and strategies for safe withdrawal: A scoping review.The World Allergy Organization journal · 2025Review
- Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.NPJ primary care respiratory medicine · 2025Article
- Impact of biologic therapy on blood cell indices and exacerbation risk in severe asthma: predictive value of the neutrophil-to-eosinophil ratio.Frontiers in allergy · 2025Article
- An Updated Systematic Review on Asthma Exacerbation Risk Prediction Models Between 2017 and 2023: Risk of Bias and Applicability.Journal of asthma and allergy · 2025Review
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Big Data Research on Severe Asthma.Tuberculosis and respiratory diseases · 2024Article
- Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes.Journal of asthma and allergy · 2024Review
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundLittle is known regarding the prediction of the risks of asthma exacerbation after stopping asthma biologics.
objectiveTo develop and validate a predictive model for the risk of asthma exacerbations after stopping asthma biologics using machine learning models.
methodsWe identified 3057 people with asthma who stopped asthma biologics in the OptumLabs Database Warehouse and considered a wide range of demographic and clinical risk factors to predict subsequent outcomes. The primary outcome used to assess success after stopping was having no exacerbations in the 6 months after stopping the biologic. Elastic-net logistic regression (GLMnet), random forest, and gradient boosting machine models were used with 10-fold cross-validation within a development (80%) cohort and validation cohort (20%).
resultsThe mean age of the total cohort was 47.1 (SD, 17.1) years, 1859 (60.8%) were women, 2261 (74.0%) were White, and 1475 (48.3%) were in the Southern region of the United States. The elastic-net logistic regression model yielded an area under the curve (AUC) of 0.75 (95% confidence interval [CI], 0.71-0.78) in the development and an AUC of 0.72 in the validation cohort. The random forest model yielded an AUC of 0.75 (95% CI, 0.68-0.79) in the development cohort and an AUC of 0.72 in the validation cohort. The gradient boosting machine model yielded an AUC of 0.76 (95% CI, 0.72-0.80) in the development cohort and an AUC of 0.74 in the validation cohort.
conclusionOutcomes after stopping asthma biologics can be predicted with moderate accuracy using machine learning methods.
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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.