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A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
Published on: April 28, 2019
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Dengue Severity Prediction in a Hyperendemic Region in Colombia
Jorge Emilio Salazar Flórez1,2, Katerine Marín Velasquez3, Luz Stella Giraldo Cardona2
1Epidemiology and Biostatistics Group, CES University, Medellín 050001, Colombia.
Viruses
|June 27, 2025
Summary
Early detection of severe dengue (SD) is vital. This study identified key predictors like neutrophils and AST, developing a model with 0.91 AUC to aid clinicians in risk assessment and triage during outbreaks.
Area of Science:
- Infectious Diseases
- Epidemiology
- Clinical Medicine
Background:
- Early detection of severe dengue (SD) is critical for preventing severe complications.
- Knowledge of early indicators for SD remains limited.
- This study addresses this gap in a hyperendemic region of Colombia.
Purpose of the Study:
- To identify predictors of severe dengue (SD) in a Colombian population.
- To develop and validate a predictive model for SD risk assessment.
Main Methods:
- Cross-sectional analysis of 233 patients (2018-2022) using WHO 2009 dengue classification.
- Statistical analysis (Fisher's exact, Mann-Whitney U) and multiple correspondence analysis.
- Logistic regression model development and validation (AUC-ROC, accuracy, sensitivity, F1, precision).
Main Results:
- Significant differences in residence, comorbidities, infection type, and symptoms between SD and non-SD groups.
- Higher median platelet, WBC, AST, and ALT levels in the SD group.
- Neutrophils, leukocytes, platelets, AST, and primary infection identified as significant SD predictors (AUC=0.91).
Conclusions:
- A validated predictive model assists clinicians in assessing SD risk.
- The model aids in optimizing patient triage, especially during dengue outbreaks.
- Improved early detection can lead to better patient outcomes and resource allocation.
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