ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2017
An Evidence-Based Algorithm for Early Prognosis of Severe Dengue in the Outpatient Setting.
Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 5 syntheses or guidelines pooled it, 66 citations in OpenAlex.
- Prognostic Models in Patients with Dengue: A Systematic Review.The American journal of tropical medicine and hygiene · 2025Pooled it
- Routine blood parameters of dengue infected children and adults. A meta-analysis.Pathogens and global health · 2023Pooled it
- Risk and predictive factors for severe dengue infection: A systematic review and meta-analysis.PloS one · 2022Pooled it
- Clinical signs and symptoms associated with WHO severe dengue classification: a systematic review and meta-analysis.Emerging microbes & infections · 2021Pooled it
- Predictive markers for the early prognosis of dengue severity: A systematic review and meta-analysis.PLoS neglected tropical diseases · 2021Pooled it
- Improving early prediction for adverse dengue outcomes using data from the IDAMS prospective multicentre study.Nature communications · 2026Observational
- Early individualized risk prediction using clinical data for children during the febrile phase of dengue in outpatient settings in Vietnam and Thailand.PLOS digital health · 2026Article
- Article
- Machine Learning Nomogram for Predicting Dengue Shock Syndrome in Pediatric Patients With Dengue Fever in Vietnam.Cureus · 2025Article
- Comparison of Predictive Models for Severe Dengue: Logistic Regression, Classification Tree, and the Structural Equation Model.The Journal of infectious diseases · 2025Article
- Dengue Fever—Diagnosis, Risk Stratification, and Treatment.Deutsches Arzteblatt international · 2024Review
- Increasing the Awareness of Under-Diagnosed Tropical Cases of Dengue in Romania.Tropical medicine and infectious disease · 2023Article
- Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients.Children (Basel, Switzerland) · 2023Article
- Co-Circulation of Dengue Virus Serotypes 1, 2, and 3 during the 2022 Dengue Outbreak in Nepal: A Cross-Sectional Study.Viruses · 2023Article
- Serum biomarkers and anti-flavivirus antibodies at presentation as indicators of severe dengue.PLoS neglected tropical diseases · 2023Article
- Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness.Frontiers in digital health · 2023Article
- Prognostic Indicators for the Early Prediction of Severe Dengue Infection: A Retrospective Study in a University Hospital in Thailand.Tropical medicine and infectious disease · 2022Article
- The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality.Frontiers in digital health · 2022Article
- Article
- Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam.PLOS digital health · 2022Article
Corrections and comments
- Commented on by
Authors and funding
14 authors at 7 institutions in 5 countries.
Funding
Abstract
Background: Early prediction of severe dengue could significantly assist patient triage and case management. Methods: We prospectively investigated 7563 children with ≤3 days of fever recruited in the outpatient departments of 6 hospitals in southern Vietnam between 2010 and 2013. The primary endpoint of interest was severe dengue (2009 World Health Organization Guidelines), and predefined risk variables were collected at the time of enrollment to enable prognostic model development. Results: The analysis population comprised 7544 patients, of whom 2060 (27.3%) had laboratory-confirmed dengue; nested among these were 117 (1.5%) severe cases. In the multivariate logistic model, a history of vomiting, lower platelet count, elevated aspartate aminotransferase (AST) level, positivity in the nonstructural protein 1 (NS1) rapid test, and viremia magnitude were all independently associated with severe dengue. The final prognostic model (Early Severe Dengue Identifier [ESDI]) included history of vomiting, platelet count, AST level. and NS1 rapid test status. Conclusions: The ESDI had acceptable performance features (area under the curve = 0.95, sensitivity 87% (95% confidence interval [CI], 80%-92%), specificity 88% (95% CI, 87%-89%), positive predictive value 10% (95% CI, 9%-12%), and negative predictive value of 99% (95% CI, 98%-100%) in the population of all 7563 enrolled children. A score chart, for routine clinical use, was derived from the prognostic model and could improve triage and management of children presenting with fever in dengue-endemic areas.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.