SynthesisPloS one2022
Risk and predictive factors for severe dengue infection: A systematic review and meta-analysis.
Synthesis in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Incidence of acute pancreatitis among hospitalized patients with dengue: A systematic review and meta-analysis.PLoS neglected tropical diseases · 2026Pooled it
- Early biomarkers for prediction of severe manifestations of dengue fever: a systematic review and a meta-analysis.Scientific reports · 2023Pooled it
- Serological and Immunological Markers in Dengue Fever: A Retrospective ELISA-Based Pilot Study from Trinidad and Tobago.Diagnostics (Basel, Switzerland) · 2026Article
- Multicenter validation of a simple clinical risk score for early prediction of severe dengue among adults in Thailand.BMC infectious diseases · 2026Article
- Infectious adverse events in patients treated with biotherapies in a tropical Amazonian context, French Guiana.BMC infectious diseases · 2026Article
- Metabolic and cardiovascular comorbidities as predictors of severe dengue in Vietnamese adults: a retrospective study, 2022-2024.Osong public health and research perspectives · 2026Article
- Expanded Dengue and the Digestive System: A Systematic Review and Meta-Analysis.Tropical medicine and infectious disease · 2026Review
- Mapping immune responses to TAK-003 dengue vaccine: immunogenicity across the clinical development program.Frontiers in immunology · 2026Review
- Early and late biomarkers as predictors of severe dengue: a comprehensive umbrella review protocol.BMJ open · 2025Article
- Perturbed pediatric circulating metabolome in mild and severe dengue disease.Journal of virology · 2025Article
- Risk factors predicting dengue hospitalization and disease severity in children and adolescents living in an endemic area of arbovirus transmission in Northeast Brazil (2017-2020).BMC public health · 2025Article
- Dengue encephalitis: what's new?Current opinion in infectious diseases · 2025Review
- Machine learning-based models for prediction of in-hospital mortality in patients with dengue shock syndrome.World journal of methodology · 2025Article
- Prognostic Indicators of Severe Dengue Infection in Adult Patients in Thailand.Tropical medicine and infectious disease · 2025Article
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- Article
- Clinical spectrum and risk factors of severe dengue infection: findings from the 2023 dengue outbreak in Bangladesh.BMC infectious diseases · 2025Article
- Machine Learning Nomogram for Predicting Dengue Shock Syndrome in Pediatric Patients With Dengue Fever in Vietnam.Cureus · 2025Article
- Machine learning for predicting severe dengue in Puerto Rico.Infectious diseases of poverty · 2025Article
- Ultrasound evaluation of gallbladder wall thickness for predicting severe dengue: a systematic review and meta-analysis.The ultrasound journal · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDengue is a major public health issue worldwide and severe dengue (SD) is life threatening. It is critical to triage patients with dengue infection in the early stage. However, there is limited knowledge on early indicators of SD. The objective of this study is to identify risk factors for the prognosis of SD and try to find out some potential predictive factors for SD from dengue fever (DF) in the early of infection.
methodsThe PubMed, Cochrane Library and Web of Science databases were searched for relevant studies from June 1999 to December 2020. The pooled odds ratio (OR) or standardized mean difference (SMD) with 95% confidence intervals (CI) of identified factors was calculated using a fixed or random effect model in the meta-analysis. Tests for heterogeneity, publication bias, subgroup analyses, meta-regression, and a sensitivity analysis were further performed.
findingsA total of 6,848 candidate articles were retrieved, 87 studies with 35,184 DF and 8,173 SD cases met the eligibility criteria. A total of 64 factors were identified, including population and virus characteristics, clinical symptoms and signs, laboratory biomarkers, cytokines, and chemokines; of these factors, 34 were found to be significantly different between DF and SD, while the other 30 factors were not significantly different between the two groups after pooling the data from the relevant studies. Additionally, 9 factors were positive associated with SD within 7 days after illness when the timing subgroup analysis were performed.
conclusionsPractical factors and biomarkers for the identification of SD were established, which will be helpful for a prompt diagnosis and early effective treatment for those at greatest risk. These outcomes also enhance our knowledge of the clinical manifestations and pathogenesis of SD.
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