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Clinical prognostic models for severe dengue: a systematic review protocol

Background: Dengue is a common mosquito-borne, with high morbidity rates recorded in the annual. Dengue contributes to a major disease burden in many tropical countries. This demonstrates the urgent need in developing effective approaches to identify severe cases early. For this purpose, many multiv...

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Autores principales: Dao Phuoc, Thang, Khuong Quynh, Long, Vien Dang Khanh, Linh, Ong Phuc, Thinh, Le Sy, Hieu, Le Ngoc, Tu, Phung Khanh, Lam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694715/
https://www.ncbi.nlm.nih.gov/pubmed/31448337
http://dx.doi.org/10.12688/wellcomeopenres.15033.2
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author Dao Phuoc, Thang
Khuong Quynh, Long
Vien Dang Khanh, Linh
Ong Phuc, Thinh
Le Sy, Hieu
Le Ngoc, Tu
Phung Khanh, Lam
author_facet Dao Phuoc, Thang
Khuong Quynh, Long
Vien Dang Khanh, Linh
Ong Phuc, Thinh
Le Sy, Hieu
Le Ngoc, Tu
Phung Khanh, Lam
author_sort Dao Phuoc, Thang
collection PubMed
description Background: Dengue is a common mosquito-borne, with high morbidity rates recorded in the annual. Dengue contributes to a major disease burden in many tropical countries. This demonstrates the urgent need in developing effective approaches to identify severe cases early. For this purpose, many multivariable prognostic models using multiple prognostic variables were developed to predict the risk of progression to severe outcomes. The aim of the planned systematic review is to identify and describe the existing clinical multivariable prognostic models for severe dengue as well as examine the possibility of combining them. These findings will suggest directions for further research of this field. Methods: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta – Analyses Protocol (PRISMA-P). We will conduct a comprehensive search of Pubmed, Embase, and Web of Science. Eligibility criteria include being published in peer-review journals, focusing on human subjects and developing the multivariable prognostic model for severe dengue, without any restriction on language, location and period of publication, and study design. The reference list will be captured and removed from duplications. We will use the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to extract data and Prediction study risk of bias assessment tool (PROBAST) to assess the study quality. Discussion: This systematic review will describe the existing prediction models, summarize the current status of prognostic research on dengue, and report the possibility to combine the models to optimize the power of each paradigm. PROSPERO registration: CRD42018102907
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spelling pubmed-66947152019-08-23 Clinical prognostic models for severe dengue: a systematic review protocol Dao Phuoc, Thang Khuong Quynh, Long Vien Dang Khanh, Linh Ong Phuc, Thinh Le Sy, Hieu Le Ngoc, Tu Phung Khanh, Lam Wellcome Open Res Study Protocol Background: Dengue is a common mosquito-borne, with high morbidity rates recorded in the annual. Dengue contributes to a major disease burden in many tropical countries. This demonstrates the urgent need in developing effective approaches to identify severe cases early. For this purpose, many multivariable prognostic models using multiple prognostic variables were developed to predict the risk of progression to severe outcomes. The aim of the planned systematic review is to identify and describe the existing clinical multivariable prognostic models for severe dengue as well as examine the possibility of combining them. These findings will suggest directions for further research of this field. Methods: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta – Analyses Protocol (PRISMA-P). We will conduct a comprehensive search of Pubmed, Embase, and Web of Science. Eligibility criteria include being published in peer-review journals, focusing on human subjects and developing the multivariable prognostic model for severe dengue, without any restriction on language, location and period of publication, and study design. The reference list will be captured and removed from duplications. We will use the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to extract data and Prediction study risk of bias assessment tool (PROBAST) to assess the study quality. Discussion: This systematic review will describe the existing prediction models, summarize the current status of prognostic research on dengue, and report the possibility to combine the models to optimize the power of each paradigm. PROSPERO registration: CRD42018102907 F1000 Research Limited 2019-08-02 /pmc/articles/PMC6694715/ /pubmed/31448337 http://dx.doi.org/10.12688/wellcomeopenres.15033.2 Text en Copyright: © 2019 Dao Phuoc T et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Study Protocol
Dao Phuoc, Thang
Khuong Quynh, Long
Vien Dang Khanh, Linh
Ong Phuc, Thinh
Le Sy, Hieu
Le Ngoc, Tu
Phung Khanh, Lam
Clinical prognostic models for severe dengue: a systematic review protocol
title Clinical prognostic models for severe dengue: a systematic review protocol
title_full Clinical prognostic models for severe dengue: a systematic review protocol
title_fullStr Clinical prognostic models for severe dengue: a systematic review protocol
title_full_unstemmed Clinical prognostic models for severe dengue: a systematic review protocol
title_short Clinical prognostic models for severe dengue: a systematic review protocol
title_sort clinical prognostic models for severe dengue: a systematic review protocol
topic Study Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694715/
https://www.ncbi.nlm.nih.gov/pubmed/31448337
http://dx.doi.org/10.12688/wellcomeopenres.15033.2
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