Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes

Lijun Ma, Holly Tibble*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Prognostic models hold great potential for predicting asthma exacerbations, providing opportunities for early intervention, and are a popular area of current research. However, it is unclear how models should be compared and contrasted, given their differences in both design and performance, particularly with a view to potential implementation in routine practice. This systematic review aimed to identify novel predictive models of asthma attacks in adults, and compare differences in construction related to populations, outcome definitions, prediction time horizons, algorithms, validation, and performance estimation.

25 studies were identified for comparison, with varying definitions of asthma attacks and prediction event time horizons ranging from 15 days to 30 months. The most commonly used algorithm was logistic regression (20/25 studies), however none of the six which tested multiple algorithms identified it as highest performing algorithm. The effect of various study design characteristics on performance was evaluated in order to provide context to the limitations of highly performing models.

Models used a variety of constructs, which affected both their performance and their viability for implementation in routine practice. Consultation with stakeholders is necessary to identify priorities for model refinement and to create a benchmark of acceptable performance for implementation in clinical practice.
Original languageEnglish
Pages (from-to)181-194
Number of pages15
JournalJournal of Asthma and Allergy
Issue number2024
Publication statusPublished - 14 Mar 2024

Keywords / Materials (for Non-textual outputs)

  • Clinical decision support
  • machine learning
  • prediction modelling
  • asthma exacerbation
  • systematic review


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