Prognostic Indicators for the Early Prediction of Severe Dengue Infection: A Retrospective Study in a University Hospital in Thailand
Issued Date
2022-08-01
Resource Type
eISSN
24146366
Scopus ID
2-s2.0-85136858663
Journal Title
Tropical Medicine and Infectious Disease
Volume
7
Issue
8
Rights Holder(s)
SCOPUS
Bibliographic Citation
Tropical Medicine and Infectious Disease Vol.7 No.8 (2022)
Suggested Citation
Srisuphanunt M., Puttaruk P., Kooltheat N., Katzenmeier G., Wilairatana P. Prognostic Indicators for the Early Prediction of Severe Dengue Infection: A Retrospective Study in a University Hospital in Thailand. Tropical Medicine and Infectious Disease Vol.7 No.8 (2022). doi:10.3390/tropicalmed7080162 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84942
Title
Prognostic Indicators for the Early Prediction of Severe Dengue Infection: A Retrospective Study in a University Hospital in Thailand
Other Contributor(s)
Abstract
This study aimed to develop simple diagnostic guidelines which would be useful for the early detection of severe dengue infections. Retrospective data of patients with dengue infection were reviewed. Patients with diagnosed dengue infection were categorized in line with the International Statistical Classification of Diseases (ICD-10): A90, dengue fever; A91, dengue hemorrhagic fever; and A910, dengue hemorrhagic fever with shock. A total of 302 dengue-infected patients were enrolled, of which 136 (45%) were male and 166 (55%) were female. Multivariate analysis was conducted to determine independent diagnostic predictors of severe dengue infection and to convert simple diagnostic guidelines into a scoring system for disease severity. Coefficients for significant predictors of disease severity generated by ordinal multivariable logistic regression analysis were transformed into item scores. The derived total scores ranged from 0 to 38.6. The cut-off score for predicting dengue severity was higher than 14, with an area under the receiver operating curve (AUROC) of 0.902. The predicted positive value (PPV) was 68.7% and the negative predictive value (NPV) was 94.1%. Our study demonstrates that several diagnostic parameters can be effectively combined into a simple score sheet with predictive value for the severity evaluation of dengue infection.