Clinical prognostic models for severe dengue: a systematic review protocol

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

Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

“…However, recent systematic reviews only examined the relevance and usefulness of Internet-based surveillance systems in emerging tropical disease management [8,14], and they did not focus specifically on dengue management. Furthermore, recent systematic reviews on dengue analyzed monitoring [15], vaccine efficacy [16], epidemiological trends [17,18], the overall disease burden [19][20][21] and clinical prognosis models [22], but they did not discuss these new methods to improve dengue management.…”

A Study of Prognostic Markers for Dengue Infection

“…1,11 Early diagnosis of patients and predicting prognosis of infection is crucial for better patient outcome. 7 The study evaluated clinical, laboratory, and radiological factors predicting prognosis in dengue infection. The present study included patients with NS1 antigen and IgM antibodies.…”

“…7 Potential predictors of prognosis comprise clinical symptoms and laboratory parameters, including fever, body ache, skin rash, platelet/blood cell count, biomarkers, including liver enzymes, and markers of vascular functions. 7 Various clinical, radiological, laboratory and immunological parameters predict the severity of dengue, permitting early disease management and reduction in morbidity and mortality. 8 Limited studies have analyzed dengue in different demographic settings in the Indian subcontinent.…”

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support

“…This can increase acceptance and make the overall project successful [2]. The tasks that can be efficiently solved by decision support systems vary from the implementation of urban climate action plans [3] and support of road planning to the diagnosis of rare diseases [4].…”