An Updated Systematic Review on Asthma Exacerbation Risk Prediction Models Between 2017 and 2023: Risk of Bias and Applicability

Anqi Liu, Yue Zhang, Chandra Prakash Yadav, Wenjia Chen Saw Swee Hock School of Public Health, National University of Singapore, SingaporeCorrespondence: Wenjia Chen, Tahir Foundation Building, National University of Singapore, 12 Science Drive 2, #10-01, Singapore, 117549, Email [email protected]: Accurate risk prediction of exacerbations in asthma patients promotes personalized asthma management.Objective: This systematic review aimed to provide an update and critically appraise the quality and usability of asthma exacerbation prediction models which were developed since 2017.Methods: In the Embase and PubMed databases, we performed a systematic search for studies published in English between May 2017 and August 2023, and identified peer-reviewed publications regarding the development of prognostic prediction models for the risk of asthma exacerbations in adult patients with asthma. We then applied the Prediction Risk of Bias Assessment tool (PROBAST) to assess the risk of bias and applicability of the included models.Results: Of 415 studies screened, 10 met eligibility criteria, comprising 41 prediction models. Among them, 7 (70%) studies used real-world data (RWD) and 3 (30%) were based on trial data to derive the models, 7 (70%) studies applied machine learning algorithms, and 2 (20%) studies included biomarkers like blood eosinophil count and fractional exhaled nitric oxide in the model. PROBAST indicated a generally high risk of bias (80%) in these models, which mainly originated from the sample selection (“Participant” domain, 6 studies) and statistical analysis (“Analysis” domain, 7 studies). Meanwhile, 5 (50%) studies were rated as having a high concern in applicability due to model complexity.Conclusion: Despite the use of big health data and advanced ML, asthma risk prediction models from 2017– 2023 had high risk of bias and limited practical use. Future efforts should enhance generalizability and practicality for real-world implementation.Keywords: adults, asthma, exacerbation, prediction model, risk