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Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a
Daniil Lisik1, Gregorio Paolo Milani2,3, Michael Salisu4
1Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden daniil.lisik@gmail.com.
Insights
This study reviews machine learning approaches to identify distinct childhood asthma and allergy trajectories, moving beyond the traditional atopic march. It synthesizes findings on risk factors and outcomes to guide future research in personalized medicine.
Area of Science:
- Pediatric Allergy and Immunology
- Computational Biology
- Epidemiology
Background:
- The 'atopic march' describes a common progression of asthma and allergies in children.
- Emerging evidence suggests multiple distinct disease phenotypes and trajectories exist.
- Understanding these diverse pathways is crucial for effective management.
Purpose of the Study:
- To systematically review machine learning-based trajectory studies of childhood asthma and allergies.
- To summarize the characteristics, risk factors, and outcomes of identified trajectories.
- To critically appraise methodologies and suggest future research directions.
Main Methods:
- Comprehensive literature search across 10 databases (2013-2023) for observational studies.
- Independent screening, data extraction, and quality/bias assessment using a custom tool.
- Narrative synthesis, tabulation, visualization, and meta-analysis of risk factors and outcomes.
Main Results:
- Synthesis of machine learning-identified asthma/allergy trajectories in children and adolescents.
- Summary of associated risk factors and clinical outcomes.
- Critical appraisal of methodological trends and limitations.
Conclusions:
- Machine learning offers valuable insights into distinct asthma/allergy trajectories beyond the atopic march.
- Further research is needed to refine risk stratification and personalized management strategies.
- Standardized methodologies and validation are essential for replicability.
Introduction:
Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the 'atopic march'. Recent reports indicate, however, that these diseases are composed of multiple distinct phenotypes, with possibly differential trajectories. We aim to synthesise the current literature in the field of machine learning-based trajectory studies of asthma/allergies in children and adolescents, summarising the frequency, characteristics and associated risk factors and outcomes of identified trajectories and indicating potential directions for subsequent research in replicability, pathophysiology, risk stratification and personalised management. Furthermore, methodological approaches and quality will be critically appraised, highlighting trends, limitations and future perspectives.
Methods And Analyses:
10 databases (CAB Direct, CINAHL, Embase, Google Scholar, PsycInfo, PubMed, Scopus, Web of Science, WHO Global Index Medicus and WorldCat Dissertations and Theses) will be searched for observational studies (including conference abstracts and grey literature) from the last 10 years (2013-2023) without restriction by language. Screening, data extraction and assessment of quality and risk of bias (using a custom-developed tool) will be performed independently in pairs. The characteristics of the derived trajectories will be narratively synthesised, tabulated and visualised in figures. Risk factors and outcomes associated with the trajectories will be summarised and pooled estimates from comparable numerical data produced through random-effects meta-analysis. Methodological approaches will be narratively synthesised and presented in tabulated form and figure to visualise trends.
Ethics And Dissemination:
Ethical approval is not warranted as no patient-level data will be used. The findings will be published in an international peer-reviewed journal.
Prospero Registration Number:
CRD42023441691.
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