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

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.

Fig 1. Time course of “Platelet count” variable from study day one to five in two groups (DHF and Non-DHF).

The data are shown as raw values (A) and means by group by study day (B) with the shaded areas representing 95% confident intervals of the means. The mean values (B) are also zoomed in to show the differences and trajectories of the two groups (C).

Fig 2. Overview of the dataset.

Fig 3. Performance results of the models trained with combined multi-site data A.

General Hospital (GH) scenario B. Primary care unit (PCU) scenario.

Fig 4. Evaluation of models with test data aligned by the day of defervescence in GH (A) and PCU (B) settings.

Defervescence in 0 days means that the day of defervescence is the same as the day the prediction is made, whereas Defervescence in x days means that the prediction is done x days before defervescence.