Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study
Issued Date
2025-08-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-105012408917
Journal Title
Plos One
Volume
20
Issue
8 August
Rights Holder(s)
SCOPUS
Bibliographic Citation
Plos One Vol.20 No.8 August (2025)
Suggested Citation
Yin M.S., Haddawy P., Meth P., Srikaew A., Wavemanee C., Niyom S.L., Sriraksa K., Limpitikul W., Kittirat P., Angkasekwinai N., Navanukroh O., Mapralub A., Pakdee S., Kaewpuak C., Tangthawornchaikul N., Malasit P., Avirutnan P., Mairiang D. Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study. Plos One Vol.20 No.8 August (2025). doi:10.1371/journal.pone.0327360 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111661
Title
Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study
Corresponding Author(s)
Other Contributor(s)
Abstract
Dengue virus (DENV) infection is a major global health problem. While DENV infection rarely results in serious complications, the more severe illness dengue hemorrhagic fever (DHF) has a significant mortality rate due to the associated plasma leakage that may lead to hypovolemic shock. Proper care thus requires identifying patients with DHF among those with suspected dengue so that they can be provided with adequate and prompt fluid replacement. In this study we used seventeen years of pediatric patient data from a prospective cohort study in two hospitals in Thailand to develop models to predict DHF among patients with suspected dengue infection. We produced models for a general hospital setting and for a primary care unit setting lacking lab facilities. The best model using combined data from both hospitals achieved an AUC of 0.90 for the general hospital setting and 0.79 for the primary care unit setting. We then investigated the generalizability of the models by training models with data from one hospital and testing them with data from the other. For some models, we found a significant reduction in performance. Possible sources of this are differences in how attributes are defined or measured and differences in the hematological parameters of the two patient populations. We conclude that while high accuracy prediction of DHF is possible, care must be taken when applying DHF predictive models from one clinical setting to another.