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Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol

INTRODUCTION: While there have been several literature reviews on the performance of digital sepsis prediction technologies and clinical decision-support algorithms for adults, there remains a knowledge gap in examining the development of automated technologies for sepsis prediction in children. Thi...

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Autores principales: Tennant, Ryan, Graham, Jennifer, Mercer, Kate, Ansermino, J Mark, Burns, Catherine M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685233/
https://www.ncbi.nlm.nih.gov/pubmed/36414283
http://dx.doi.org/10.1136/bmjopen-2022-065429
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author Tennant, Ryan
Graham, Jennifer
Mercer, Kate
Ansermino, J Mark
Burns, Catherine M
author_facet Tennant, Ryan
Graham, Jennifer
Mercer, Kate
Ansermino, J Mark
Burns, Catherine M
author_sort Tennant, Ryan
collection PubMed
description INTRODUCTION: While there have been several literature reviews on the performance of digital sepsis prediction technologies and clinical decision-support algorithms for adults, there remains a knowledge gap in examining the development of automated technologies for sepsis prediction in children. This scoping review will critically analyse the current evidence on the design and performance of automated digital technologies to predict paediatric sepsis, to advance their development and integration within clinical settings. METHODS AND ANALYSIS: This scoping review will follow Arksey and O’Malley’s framework, conducted between February and December 2022. We will further develop the protocol using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. We plan to search the following databases: Association of Computing Machinery (ACM) Digital Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Google Scholar, Institute of Electric and Electronic Engineers (IEEE), PubMed, Scopus and Web of Science. Studies will be included on children >90 days postnatal to <21 years old, predicted to have or be at risk of developing sepsis by a digitalised model or algorithm designed for a clinical setting. Two independent reviewers will complete the abstract and full-text screening and the data extraction. Thematic analysis will be used to develop overarching concepts and present the narrative findings with quantitative results and descriptive statistics displayed in data tables. ETHICS AND DISSEMINATION: Ethics approval for this scoping review study of the available literature is not required. We anticipate that the scoping review will identify the current evidence and design characteristics of digital prediction technologies for the timely and accurate prediction of paediatric sepsis and factors influencing clinical integration. We plan to disseminate the preliminary findings from this review at national and international research conferences in global and digital health, gathering critical feedback from multidisciplinary stakeholders. SCOPING REVIEW REGISTRATION: https://osf.io/veqha/?view_only=f560d4892d7c459ea4cff6dcdfacb086
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spelling pubmed-96852332022-11-25 Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol Tennant, Ryan Graham, Jennifer Mercer, Kate Ansermino, J Mark Burns, Catherine M BMJ Open Health Informatics INTRODUCTION: While there have been several literature reviews on the performance of digital sepsis prediction technologies and clinical decision-support algorithms for adults, there remains a knowledge gap in examining the development of automated technologies for sepsis prediction in children. This scoping review will critically analyse the current evidence on the design and performance of automated digital technologies to predict paediatric sepsis, to advance their development and integration within clinical settings. METHODS AND ANALYSIS: This scoping review will follow Arksey and O’Malley’s framework, conducted between February and December 2022. We will further develop the protocol using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. We plan to search the following databases: Association of Computing Machinery (ACM) Digital Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Google Scholar, Institute of Electric and Electronic Engineers (IEEE), PubMed, Scopus and Web of Science. Studies will be included on children >90 days postnatal to <21 years old, predicted to have or be at risk of developing sepsis by a digitalised model or algorithm designed for a clinical setting. Two independent reviewers will complete the abstract and full-text screening and the data extraction. Thematic analysis will be used to develop overarching concepts and present the narrative findings with quantitative results and descriptive statistics displayed in data tables. ETHICS AND DISSEMINATION: Ethics approval for this scoping review study of the available literature is not required. We anticipate that the scoping review will identify the current evidence and design characteristics of digital prediction technologies for the timely and accurate prediction of paediatric sepsis and factors influencing clinical integration. We plan to disseminate the preliminary findings from this review at national and international research conferences in global and digital health, gathering critical feedback from multidisciplinary stakeholders. SCOPING REVIEW REGISTRATION: https://osf.io/veqha/?view_only=f560d4892d7c459ea4cff6dcdfacb086 BMJ Publishing Group 2022-11-22 /pmc/articles/PMC9685233/ /pubmed/36414283 http://dx.doi.org/10.1136/bmjopen-2022-065429 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Health Informatics
Tennant, Ryan
Graham, Jennifer
Mercer, Kate
Ansermino, J Mark
Burns, Catherine M
Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol
title Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol
title_full Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol
title_fullStr Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol
title_full_unstemmed Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol
title_short Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol
title_sort automated digital technologies for supporting sepsis prediction in children: a scoping review protocol
topic Health Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685233/
https://www.ncbi.nlm.nih.gov/pubmed/36414283
http://dx.doi.org/10.1136/bmjopen-2022-065429
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