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DOI 10.1183/16000617.0160-2024

Медицинская библиотека · European respiratory review : an official journal of the European Respiratory Society · 2025

Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.

Lisik Daniil, Özuygur Ermis Saliha Selin, Milani Gregorio Paolo, Spolidoro Giulia Carla Immacolata, Ercan Selin, Salisu Michael, Odetola Faozyat, Ghiglioni Daniele Giovanni, Pylov Danylo, Goksör Emma, Basna Rani, Wennergren Göran, Kankaanranta Hannu, Nwaru Bright I

Систематический обзорСтатьяМетаанализПеревод в очереди

Аннотация

INTRODUCTION

Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the methodology.

METHODS

10 databases were searched. Screening, data extraction and quality assessment were performed in pairs. Trajectory characteristics were tabulated and visualised. Associated risk factor and outcome estimates were pooled using a random-effects meta-analysis.

RESULTS

89 studies were included. Early-onset (infancy) persistent, mid-onset (∼2-5 years) persistent, early-onset early-resolving (within ∼2 years) and early-onset mid-resolving (by ∼3-6 years) wheezing and eczema, respectively, were the most commonly identified disease trajectories. Intermediate/transient trajectories were rare. Male sex was associated with a higher risk of most wheezing trajectories and possibly with early-resolving eczema, while being slightly protective against mid-onset persistent eczema. Parental disease/genetic markers were associated with persistent trajectories of wheezing and eczema, respectively. Prenatal (and less so postnatal) tobacco smoke exposure was associated with most wheezing trajectories, as were lower respiratory tract infections in infancy (particularly with the early-onset resolving patterns). Most studies (69%) were of low methodological quality (particularly in modelling approaches and reporting). Few studies investigated allergic multimorbidity, allergic rhinitis and food allergy.

CONCLUSIONS

Childhood asthma/wheezing and eczema can be characterised by a few relatively consistent trajectories, with some actionable risk factors such as pre-/postnatal smoke exposure. Improved computational methodology is warranted to better assess generalisability and elucidate the validity of intermediate/transient trajectories. Likewise, allergic multimorbidity and trajectories of allergic rhinitis and food allergy need to be further elucidated.

Для цитирования: Lisik Daniil, Özuygur Ermis Saliha Selin, Milani Gregorio Paolo, Spolidoro Giulia Carla Immacolata, Ercan Selin, Salisu Michael, Odetola Faozyat, Ghiglioni Daniele Giovanni, Pylov Danylo, Goksör Emma, Basna Rani, Wennergren Göran, Kankaanranta Hannu, Nwaru Bright I. Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.. European respiratory review : an official journal of the European Respiratory Society. 2025. DOI: 10.1183/16000617.0160-2024.

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