Research graph
References from Application of type 2 inflammation-related biomarkers in risk prediction models for acute exacerbations in adult asthma: a systematic review. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2026 · External reference
An official American thoracic society/European respiratory society statement: asthma control and exacerbations: standardizing endpoints for clinical asthma trials and clinical practice
10.1164/rccm.200801-060st · 2009 · External reference
Asthma
10.1016/s0140-6736(17)33311-1 · 2018 · External reference
Understanding the key issues in the treatment of uncontrolled persistent asthma with type 2 inflammation
10.1183/13993003.03393-2020 · 2021 · External reference
Type 2 inflammation in asthma-present in most, absent in many
10.1038/nri3786 · 2015 · External reference
Guidance for type 2 inflammatory biomarkers
10.1016/j.resinv.2025.01.003 · 2025 · External reference
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
10.1186/s12916-014-0241-z · 2015 · External reference
Primary care asthma attack prediction models for adults: a systematic review of reported methodologies and outcomes
10.2147/jaa.s445450 · 2024 · External reference
An updated systematic review on asthma exacerbation risk prediction models between 2017 and 2023: risk of bias and applicability
10.2147/jaa.s509260 · 2025 · External reference
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
10.1136/bmj.n71 · 2021 · External reference
PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews
10.1186/s13643-020-01542-z · 2021 · External reference
Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist
10.1371/journal.pmed.1001744 · 2014 · External reference
PROBAST: a tool to assess the risk of bias and applicability of prediction model studies
10.7326/m18-1376 · 2019 · External reference
A guide to systematic review and meta-analysis of prediction model performance
10.1136/bmj.i6460 · 2017 · External reference
Novel machine learning can predict acute asthma exacerbation
10.1016/j.chest.2020.12.051 · 2021 · External reference
Predicting asthma exacerbations using machine learning models
10.1007/s12325-024-03053-y · 2025 · External reference
Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care
10.1038/s41533-025-00428-8 · 2025 · External reference
Predictors of asthma exacerbation between high and low blood eosinophil counts
10.1183/23120541.01218-2024 · 2025 · External reference
Interactive pathways of key prognostic factors in severe asthma: a Bayesian network comparison of clinical trials and real-world data
10.1016/j.chest.2026.01.009 · 2026 · External reference
The ratio of circulatory levels of sphingolipids to steroids predicts asthma exacerbations
10.1038/s41467-025-67436-7 · 2026 · External reference
Prediction pathway for severe asthma exacerbations: a Bayesian network analysis
10.1016/j.chest.2025.04.046 · 2025 · External reference
Development and validation of the risk of exacerbation in severe asthma (RESA) model
10.1016/j.jaip.2026.03.017 · 2026 · External reference
Electronic medical record-based machine learning predicts the relapse of asthma exacerbation
10.1016/j.anai.2023.04.025 · 2023 · External reference
Exacerbations in adults with asthma: a systematic review and external validation of prediction models
10.1016/j.jaip.2018.02.004 · 2018 · External reference
Assessing the performance of prediction models: a framework for traditional and novel measures
10.1097/ede.0b013e3181c30fb2 · 2010 · External reference
Variability of type 2 inflammatory markers guiding biologic therapy of severe asthma: a 5-year retrospective study from a single tertiary hospital
10.1016/j.waojou.2021.100547 · 2021 · External reference
Predictive value of blood eosinophils and exhaled nitric oxide in adults with mild asthma: a prespecified subgroup analysis of an open-label, parallel-group, randomised controlled trial
10.1016/s2213-2600(20)30053-9 · 2020 · External reference
Prospective, single-arm, longitudinal study of biomarkers in real-world patients with severe asthma
10.1016/j.jaip.2020.03.038 · 2020 · External reference
Evaluation of clinical prediction models (part 1): from development to external validation
10.1136/bmj-2023-074819 · 2024 · External reference
Calibration: the achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · 2019 · External reference
Longitudinal stability of asthma characteristics and biomarkers from the airways disease endotyping for personalized therapeutics (ADEPT) study
10.1186/s12931-016-0360-5 · 2016 · External reference
Baseline FeNO as a prognostic biomarker for subsequent severe asthma exacerbations in patients with uncontrolled, moderate-to-severe asthma receiving placebo in the LIBERTY ASTHMA QUEST study: a post-hoc analysis
10.1016/s2213-2600(21)00124-7 · 2021 · External reference
Association of elevated fractional exhaled nitric oxide concentration and blood eosinophil count with severe asthma exacerbations
10.1186/s13601-019-0282-7 · 2019 · External reference
An official ATS clinical practice guideline: interpretation of exhaled nitric oxide levels (FeNO) for clinical applications
10.1164/rccm.9120-11st · 2011 · External reference
Composite type-2 biomarker strategy versus a symptom-risk-based algorithm to adjust corticosteroid dose in patients with severe asthma: a multicentre, single-blind, parallel-group, randomised controlled trial
10.1016/s2213-2600(20)30397-0 · 2021 · External reference
Minimum sample size for developing a multivariable prediction model: part II-binary and time-to-event outcomes
10.1002/sim.7992 · 2019 · External reference
Minimum sample size for external validation of a clinical prediction model with a binary outcome
10.1002/sim.9025 · 2021 · External reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · 2006 · External reference
The cost of dichotomising continuous variables
10.1136/bmj.332.7549.1080 · 2006 · External reference
TRIPOD + AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Minimum sample size for developing a multivariable prediction model: part II-binary and time-to-event outcomes
10.1002/sim.7992 · ExternalCitation · doi-reference
Minimum sample size for external validation of a clinical prediction model with a binary outcome
10.1002/sim.9025 · ExternalCitation · doi-reference
Predicting asthma exacerbations using machine learning models
10.1007/s12325-024-03053-y · ExternalCitation · doi-reference
Electronic medical record-based machine learning predicts the relapse of asthma exacerbation
10.1016/j.anai.2023.04.025 · ExternalCitation · doi-reference
Novel machine learning can predict acute asthma exacerbation
10.1016/j.chest.2020.12.051 · ExternalCitation · doi-reference
Prediction pathway for severe asthma exacerbations: a Bayesian network analysis
10.1016/j.chest.2025.04.046 · ExternalCitation · doi-reference
Interactive pathways of key prognostic factors in severe asthma: a Bayesian network comparison of clinical trials and real-world data
10.1016/j.chest.2026.01.009 · ExternalCitation · doi-reference
Exacerbations in adults with asthma: a systematic review and external validation of prediction models
10.1016/j.jaip.2018.02.004 · ExternalCitation · doi-reference
Prospective, single-arm, longitudinal study of biomarkers in real-world patients with severe asthma
10.1016/j.jaip.2020.03.038 · ExternalCitation · doi-reference
Development and validation of the risk of exacerbation in severe asthma (RESA) model
10.1016/j.jaip.2026.03.017 · ExternalCitation · doi-reference
Guidance for type 2 inflammatory biomarkers
10.1016/j.resinv.2025.01.003 · ExternalCitation · doi-reference
Variability of type 2 inflammatory markers guiding biologic therapy of severe asthma: a 5-year retrospective study from a single tertiary hospital
10.1016/j.waojou.2021.100547 · ExternalCitation · doi-reference
Asthma
10.1016/s0140-6736(17)33311-1 · ExternalCitation · doi-reference
Predictive value of blood eosinophils and exhaled nitric oxide in adults with mild asthma: a prespecified subgroup analysis of an open-label, parallel-group, randomised controlled trial
10.1016/s2213-2600(20)30053-9 · ExternalCitation · doi-reference
Composite type-2 biomarker strategy versus a symptom-risk-based algorithm to adjust corticosteroid dose in patients with severe asthma: a multicentre, single-blind, parallel-group, randomised controlled trial
10.1016/s2213-2600(20)30397-0 · ExternalCitation · doi-reference
Baseline FeNO as a prognostic biomarker for subsequent severe asthma exacerbations in patients with uncontrolled, moderate-to-severe asthma receiving placebo in the LIBERTY ASTHMA QUEST study: a post-hoc analysis
10.1016/s2213-2600(21)00124-7 · ExternalCitation · doi-reference
Type 2 inflammation in asthma-present in most, absent in many
10.1038/nri3786 · ExternalCitation · doi-reference
The ratio of circulatory levels of sphingolipids to steroids predicts asthma exacerbations
10.1038/s41467-025-67436-7 · ExternalCitation · doi-reference
Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care
10.1038/s41533-025-00428-8 · ExternalCitation · doi-reference
Assessing the performance of prediction models: a framework for traditional and novel measures
10.1097/ede.0b013e3181c30fb2 · ExternalCitation · doi-reference
Evaluation of clinical prediction models (part 1): from development to external validation
10.1136/bmj-2023-074819 · ExternalCitation · doi-reference
TRIPOD + AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · ExternalCitation · doi-reference
The cost of dichotomising continuous variables
10.1136/bmj.332.7549.1080 · ExternalCitation · doi-reference
A guide to systematic review and meta-analysis of prediction model performance
10.1136/bmj.i6460 · ExternalCitation · doi-reference
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
10.1136/bmj.n71 · ExternalCitation · doi-reference
An official American thoracic society/European respiratory society statement: asthma control and exacerbations: standardizing endpoints for clinical asthma trials and clinical practice
10.1164/rccm.200801-060st · ExternalCitation · doi-reference
An official ATS clinical practice guideline: interpretation of exhaled nitric oxide levels (FeNO) for clinical applications
10.1164/rccm.9120-11st · ExternalCitation · doi-reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · ExternalCitation · doi-reference
Understanding the key issues in the treatment of uncontrolled persistent asthma with type 2 inflammation
10.1183/13993003.03393-2020 · ExternalCitation · doi-reference
Predictors of asthma exacerbation between high and low blood eosinophil counts
10.1183/23120541.01218-2024 · ExternalCitation · doi-reference
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
10.1186/s12916-014-0241-z · ExternalCitation · doi-reference
Calibration: the achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · ExternalCitation · doi-reference
Longitudinal stability of asthma characteristics and biomarkers from the airways disease endotyping for personalized therapeutics (ADEPT) study
10.1186/s12931-016-0360-5 · ExternalCitation · doi-reference
Association of elevated fractional exhaled nitric oxide concentration and blood eosinophil count with severe asthma exacerbations
10.1186/s13601-019-0282-7 · ExternalCitation · doi-reference
PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews
10.1186/s13643-020-01542-z · ExternalCitation · doi-reference
Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist
10.1371/journal.pmed.1001744 · ExternalCitation · doi-reference
Primary care asthma attack prediction models for adults: a systematic review of reported methodologies and outcomes
10.2147/jaa.s445450 · ExternalCitation · doi-reference
An updated systematic review on asthma exacerbation risk prediction models between 2017 and 2023: risk of bias and applicability
10.2147/jaa.s509260 · ExternalCitation · doi-reference
PROBAST: a tool to assess the risk of bias and applicability of prediction model studies
10.7326/m18-1376 · ExternalCitation · doi-reference
