Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents : protocol for a systematic review
Lisik, Daniil ; Milani, Gregorio Paolo ; Salisu, Michael ; Özuygur Ermis, Saliha Selin ; Goksör, Emma ; Basna, Rani LU
; Wennergren, Göran
; Kankaanranta, Hannu
and Nwaru, Bright I.
(2024)
In BMJ Open
14(8).
p.1-8
- Abstract
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... (More)
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.
(Less)
- author
- Lisik, Daniil
; Milani, Gregorio Paolo
; Salisu, Michael
; Özuygur Ermis, Saliha Selin
; Goksör, Emma
; Basna, Rani
LU
; Wennergren, Göran
; Kankaanranta, Hannu
and Nwaru, Bright I.
- organization
- publishing date
- 2024-08-30
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Allergy, Asthma, Meta-Analysis, Risk Factors, Systematic Review
- in
- BMJ Open
- volume
- 14
- issue
- 8
- article number
- e080263
- pages
- 1 - 8
- publisher
- BMJ Publishing Group
- external identifiers
-
- pmid:39214659
- scopus:85203113171
- ISSN
- 2044-6055
- DOI
- 10.1136/bmjopen-2023-080263
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.
- id
- 875ab5c6-1b7a-4b0d-985d-09728121f8d4
- date added to LUP
- 2024-09-13 09:54:13
- date last changed
- 2024-09-14 03:00:06
@article{875ab5c6-1b7a-4b0d-985d-09728121f8d4,
abstract = {{<p>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.</p>}},
author = {{Lisik, Daniil and Milani, Gregorio Paolo and Salisu, Michael and Özuygur Ermis, Saliha Selin and Goksör, Emma and Basna, Rani and Wennergren, Göran and Kankaanranta, Hannu and Nwaru, Bright I.}},
issn = {{2044-6055}},
keywords = {{Allergy; Asthma; Meta-Analysis; Risk Factors; Systematic Review}},
language = {{eng}},
month = {{08}},
number = {{8}},
pages = {{1--8}},
publisher = {{BMJ Publishing Group}},
series = {{BMJ Open}},
title = {{Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents : protocol for a systematic review}},
url = {{http://dx.doi.org/10.1136/bmjopen-2023-080263}},
doi = {{10.1136/bmjopen-2023-080263}},
volume = {{14}},
year = {{2024}},
}