Abstract
Objective
To systematically evaluate how type 2 (T2) inflammation-related biomarkers are incorporated into multivariable models that predict future acute exacerbations in adults with asthma, and to appraise model performance, validation, calibration, risk of bias, and clinical applicability.
Methods
PubMed, Embase, Web of Science, the Cochrane Library, and Scopus were searched from inception to May 2026, followed by a supplementary PubMed update through 31 July 2026. Eligible reports contained at least one adult cohort used to develop, validate, or update a longitudinal multivariable model for a future exacerbation outcome, incorporated at least one T2-related biomarker, and reported model performance or validation. Mixed-age cohorts were not counted as independent adult evidence; only performance evaluated in an adult cohort contributed to the main synthesis. Association-only studies, current-attack classification models, post-exacerbation disposition models, and post-index relapse models outside the prespecified baseline-risk setting were excluded. Data extraction followed CHARMS, and risk of bias and applicability were assessed using PROBAST.
Results
Eight reports published from 2021 to 2026 were included. Eligible adult cohorts ranged from 540 to 1,331,934 participants; one report also included a mixed-age randomized-trial comparator (
n
= 345). Modeling approaches included logistic and elastic-net regression, gradient-boosting and deep-learning algorithms, Bayesian networks, and a mixed-effects count model. Eosinophil-related variables appeared in all eight reports but were not retained in every final model; FeNO was included in two multinational severe-asthma registry models, and serum IgE appeared as a network node in one cross-source analysis and was retained in an independently replicated metabolomics model. The metabolomics model reported AUCs of 0.901 and 0.893 in discovery and replication cohorts, whereas an RCT-derived Bayesian network achieved an AUC of 0.68 when validated in the adult ISAR cohort. Six reports were rated at high overall risk of bias, mainly because of analysis-domain limitations.
Conclusion
T2-related biomarkers may add complementary information to adult asthma exacerbation-risk models, but the available evidence does not yet establish BEC, FeNO, or IgE as independently sufficient predictors or support routine implementation of an existing model. Future studies should quantify incremental biomarker value beyond established clinical predictors and prioritize adequate sample size, external validation, calibration, clinical utility, transparent handling of missing biomarker data, and reproducible model presentation.
Systematic Review Registration
https://www.crd.york.ac.uk/prospero/
, PROSPERO CRD420261415469.