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Version 2. Wellcome Open Res. 2019 Aug 2 [revised 2019 Aug 2];4:12. doi: 10.12688/wellcomeopenres.15033.2. eCollection 2019.

Clinical prognostic models for severe dengue: a systematic review protocol.

Author information

1
University of Medicine and Pharmacy, Ho Chi Minh City, 700000, Vietnam.
2
Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.

Abstract

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.

KEYWORDS:

decision support techniques; dengue; dengue hemorrhagic fever; dengue shock syndrome; forecasting; model; prediction; prognosis; severe dengue

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