Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study

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.

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Publication Details

Journal
PLoS ONE
Published
2025-08-04
DOI
https://doi.org/10.1371/journal.pone.0327360
Citations
2
Primary Topic
Mosquito-borne diseases and control
Type
article
Field-Weighted Citation Impact
3.76
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article

Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study

Nattaya Tangthawornchaikul, Prida Malasit, Peter Haddawy, Panisadee Avirutnan et al.
2 citations
PLoS ONE
Mosquito-borne diseases and control
3.76
article

Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study

Nattaya Tangthawornchaikul, Prida Malasit, Peter Haddawy, Panisadee Avirutnan, Oranich Navanukroh, Kanokwan Sriraksa, Nasikarn Angkasekwinai, Myat Su Yin, Dumrong Mairiang, Saranath Lawpoolsri Niyom, Wannee Limpitikul, Panhavath Meth, Araya Srikaew, Chonnikarn Wavemanee, Preedawadee Kittirat, Arunee Mapralub, Supansa Pakdee, Chotika Kaewpuak
article en
2 citations

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.

PLoS ONEVol. 20(8)
National Science and Technology Development Agency (TH), Siriraj Hospital (TH), Khon Kaen University (TH), University of Bremen (DE), Songkhla Rajabhat University (TH), Mahidol University (TH), Songklanagarind Hospital (TH), Mahidol Oxford Tropical Medicine Research Unit (TH)
Good health and well-being
Openalex Percentile: Top 7%
Mosquito-borne diseases and control
3.76
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