Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis
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...
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| Format: | Article |
| Language: | English |
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European Respiratory Society
2024-11-01
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| Series: | European Respiratory Review |
| Online Access: | http://err.ersjournals.com/content/33/174/240118.full |
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| author | Martina Votto Annalisa De Silvestri Lorenzo Postiglione Maria De Filippo Sara Manti Stefania La Grutta Gian Luigi Marseglia Amelia Licari |
| author_facet | Martina Votto Annalisa De Silvestri Lorenzo Postiglione Maria De Filippo Sara Manti Stefania La Grutta Gian Luigi Marseglia Amelia Licari |
| author_sort | Martina Votto |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b535aede6c424db79e26890b9280437d |
| institution | DOAJ |
| issn | 0905-9180 1600-0617 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | European Respiratory Society |
| record_format | Article |
| series | European Respiratory Review |
| spelling | doaj-art-b535aede6c424db79e26890b9280437d2025-08-20T02:42:57ZengEuropean Respiratory SocietyEuropean Respiratory Review0905-91801600-06172024-11-013317410.1183/16000617.0118-20240118-2024Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysisMartina Votto0Annalisa De Silvestri1Lorenzo Postiglione2Maria De Filippo3Sara Manti4Stefania La Grutta5Gian Luigi Marseglia6Amelia Licari7 Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy Biometry and Clinical Epidemiology, Scientific Direction, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy Pediatric Unit, Department of Human Pathology in Adult and Developmental Age “Gaetano Barresi”, University of Messina, Messina, Italy Institute of Translational Pharmacology (IFT), National Research Council of Italy (CNR), Palermo, Italy Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy 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.http://err.ersjournals.com/content/33/174/240118.full |
| spellingShingle | Martina Votto Annalisa De Silvestri Lorenzo Postiglione Maria De Filippo Sara Manti Stefania La Grutta Gian Luigi Marseglia Amelia Licari Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis European Respiratory Review |
| title | Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis |
| title_full | Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis |
| title_fullStr | Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis |
| title_full_unstemmed | Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis |
| title_short | Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis |
| title_sort | predicting paediatric asthma exacerbations with machine learning a systematic review with meta analysis |
| url | http://err.ersjournals.com/content/33/174/240118.full |
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