Cargando…
Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol
INTRODUCTION: Chronic liver disease is a growing cause of morbidity and mortality in the UK. Acute presentation with advanced disease is common and prioritisation of resources to those at highest risk at earlier disease stages is essential to improving patient outcomes. Existing prognostic tools are...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9062789/ https://www.ncbi.nlm.nih.gov/pubmed/35501093 http://dx.doi.org/10.1136/bmjopen-2021-053204 |
_version_ | 1784699024454975488 |
---|---|
author | Chouhan, Manil D Taylor, Stuart Andrew Bhagwanani, Anisha Munday, Charlotte Pinnock, Mark A Parry, Tom Hu, Yipeng Barratt, Dean Yu, Dominic Mookerjee, Rajeshwar P Halligan, Steve Mallett, Sue |
author_facet | Chouhan, Manil D Taylor, Stuart Andrew Bhagwanani, Anisha Munday, Charlotte Pinnock, Mark A Parry, Tom Hu, Yipeng Barratt, Dean Yu, Dominic Mookerjee, Rajeshwar P Halligan, Steve Mallett, Sue |
author_sort | Chouhan, Manil D |
collection | PubMed |
description | INTRODUCTION: Chronic liver disease is a growing cause of morbidity and mortality in the UK. Acute presentation with advanced disease is common and prioritisation of resources to those at highest risk at earlier disease stages is essential to improving patient outcomes. Existing prognostic tools are of limited accuracy and to date no imaging-based tools are used in clinical practice, despite multiple anatomical imaging features that worsen with disease severity. In this paper, we outline our scoping review protocol that aims to provide an overview of existing prognostic factors and models that link anatomical imaging features with clinical endpoints in chronic liver disease. This will provide a summary of the number, type and methods used by existing imaging feature-based prognostic studies and indicate if there are sufficient studies to justify future systematic reviews. METHODS AND ANALYSIS: The protocol was developed in accordance with existing scoping review guidelines. Searches of MEDLINE and Embase will be conducted using titles, abstracts and Medical Subject Headings restricted to publications after 1980 to ensure imaging method relevance on OvidSP. Initial screening will be undertaken by two independent reviewers. Full-text data extraction will be undertaken by three pretrained reviewers who have participated in a group data extraction session to ensure reviewer consensus and reduce inter-rater variability. Where needed, data extraction queries will be resolved by reviewer team discussion. Reporting of results will be based on grouping of related factors and their cumulative frequencies. Prognostic anatomical imaging features and clinical endpoints will be reported using descriptive statistics to summarise the number of studies, study characteristics and the statistical methods used. ETHICS AND DISSEMINATION: Ethical approval is not required as this study is based on previously published work. Findings will be disseminated by peer-reviewed publication and/or conference presentations. |
format | Online Article Text |
id | pubmed-9062789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-90627892022-05-12 Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol Chouhan, Manil D Taylor, Stuart Andrew Bhagwanani, Anisha Munday, Charlotte Pinnock, Mark A Parry, Tom Hu, Yipeng Barratt, Dean Yu, Dominic Mookerjee, Rajeshwar P Halligan, Steve Mallett, Sue BMJ Open Radiology and Imaging INTRODUCTION: Chronic liver disease is a growing cause of morbidity and mortality in the UK. Acute presentation with advanced disease is common and prioritisation of resources to those at highest risk at earlier disease stages is essential to improving patient outcomes. Existing prognostic tools are of limited accuracy and to date no imaging-based tools are used in clinical practice, despite multiple anatomical imaging features that worsen with disease severity. In this paper, we outline our scoping review protocol that aims to provide an overview of existing prognostic factors and models that link anatomical imaging features with clinical endpoints in chronic liver disease. This will provide a summary of the number, type and methods used by existing imaging feature-based prognostic studies and indicate if there are sufficient studies to justify future systematic reviews. METHODS AND ANALYSIS: The protocol was developed in accordance with existing scoping review guidelines. Searches of MEDLINE and Embase will be conducted using titles, abstracts and Medical Subject Headings restricted to publications after 1980 to ensure imaging method relevance on OvidSP. Initial screening will be undertaken by two independent reviewers. Full-text data extraction will be undertaken by three pretrained reviewers who have participated in a group data extraction session to ensure reviewer consensus and reduce inter-rater variability. Where needed, data extraction queries will be resolved by reviewer team discussion. Reporting of results will be based on grouping of related factors and their cumulative frequencies. Prognostic anatomical imaging features and clinical endpoints will be reported using descriptive statistics to summarise the number of studies, study characteristics and the statistical methods used. ETHICS AND DISSEMINATION: Ethical approval is not required as this study is based on previously published work. Findings will be disseminated by peer-reviewed publication and/or conference presentations. BMJ Publishing Group 2022-05-02 /pmc/articles/PMC9062789/ /pubmed/35501093 http://dx.doi.org/10.1136/bmjopen-2021-053204 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 | Radiology and Imaging Chouhan, Manil D Taylor, Stuart Andrew Bhagwanani, Anisha Munday, Charlotte Pinnock, Mark A Parry, Tom Hu, Yipeng Barratt, Dean Yu, Dominic Mookerjee, Rajeshwar P Halligan, Steve Mallett, Sue Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol |
title | Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol |
title_full | Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol |
title_fullStr | Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol |
title_full_unstemmed | Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol |
title_short | Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol |
title_sort | imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol |
topic | Radiology and Imaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9062789/ https://www.ncbi.nlm.nih.gov/pubmed/35501093 http://dx.doi.org/10.1136/bmjopen-2021-053204 |
work_keys_str_mv | AT chouhanmanild imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT taylorstuartandrew imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT bhagwananianisha imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT mundaycharlotte imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT pinnockmarka imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT parrytom imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT huyipeng imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT barrattdean imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT yudominic imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT mookerjeerajeshwarp imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT halligansteve imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol AT mallettsue imagingfeaturesforthepredictionofclinicalendpointsinchronicliverdiseaseascopingreviewprotocol |