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Validation of childhood asthma predictive tools: A systematic review
Silvia Colicino1, Daniel Munblit2,3,4,5, Cosetta Minelli1
1National Heart and Lung Institute, Imperial College London, London, UK.
Summary
Current asthma prediction tools show poor accuracy and inconsistent performance. External validation reveals significant variation in sensitivity and positive predictive values, questioning their clinical usefulness for children.
Area of Science:
- Pediatric Pulmonology
- Clinical Epidemiology
- Biostatistics
Background:
- Uncertainty exists regarding the clinical utility of current asthma predictive tools.
- External validation is crucial for assessing the performance, reproducibility, and generalizability of these tools across diverse populations and settings.
Purpose of the Study:
- To critically appraise asthma predictive tools that have undergone external validation.
Main Methods:
- A systematic literature search of MEDLINE and EMBASE (1946-2017) was conducted for childhood asthma prediction models.
- Focused on externally validated tools and their original development studies, excluding non-English and non-original research.
- PROSPERO registration number: CRD42016035727.
Main Results:
- Eight studies were included, utilizing statistical methods like logistic regression.
- Only three tools (Asthma Predictive Index, PIAMA, Leicester asthma prediction tool) were externally validated.
- Significant heterogeneity was found in outcome definitions and predictor combinations; objective asthma diagnosis measures were lacking.
Conclusions:
- Externally validated asthma predictive tools demonstrate poor predictive accuracy.
- Performance metrics, including sensitivity and positive predictive value, varied considerably, limiting their clinical applicability.
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