Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis

Abstract

Background: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.

Objective: This study aims to systematically evaluate and quantify the performance of machine learning (ML) algorithms in predicting the risk of hospitalisation and emergency department (ED) admission for acute asthma exacerbations in children.

Methods: We performed a systematic review with meta-analysis, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The risk of bias and applicability for eligible studies was assessed according to the prediction model study risk of bias assessment tool (PROBAST). The protocol of our systematic review was registered in the International Prospective Register of Systematic Reviews.

Results: Our meta-analysis included seven articles encompassing a total of 17 ML-based prediction models. We found a pooled area under the curve (AUC) of 0.67 (95% CI 0.61-0.73; I²=99%; p<0.0001 for heterogeneity) for models predicting ED admission, indicating moderate accuracy. Notably, models predicting child hospitalisation demonstrated a higher pooled AUC of 0.79 (95% CI 0.76-0.82; I²=95%; p<0.0001 for heterogeneity), suggesting good discriminatory power.

Conclusion: This study provides the most comprehensive assessment of AI-based algorithms in predicting paediatric asthma exacerbations to date. While these models show promise and ML-based hospitalisation prediction models, in particular, demonstrate good accuracy, further external validation is needed before these models can be reliably implemented in real-life clinical practice.

FIGURE 1

Flow diagram for identification of studies according to PRISMA guidelines. ML: machine learning.

FIGURE 2

a) Pooled area under the receiver operating curve (AUROC) (forest plot) of machine learning (ML) models to predict emergency department (ED) admission for asthma exacerbations. b) Pooled AUROC (forest plot) of ML models to predict hospitalisation for asthma exacerbations. DT: decision tree; EDT: ensemble of decision trees; GB: gradient boosting; IB1, IB10: instance-based model with 1 and 10 nearest neighbours, respectively; LASSO: least absolute shrinkage and selection operator; LR: logistic regression; NB: naive Bayes; SVM: support vector machine; RF: random forest.