# Early individualized risk prediction using clinical data for children during the febrile phase of dengue in outpatient settings in Vietnam and Thailand

## TL;DR

This study creates and validates models to predict severe dengue in children during outpatient visits in Vietnam and Thailand, using clinical data to improve early triage and resource allocation.

## Contribution

The study introduces externally validated dengue risk prediction models using outpatient data across two countries, outperforming WHO warning signs.

## Key findings

- Lasso-selected logistic regression was the best model for predicting dengue shock syndrome and moderate plasma leakage.
- Models outperformed WHO warning signs in discrimination and calibration for predicting severe dengue.
- Key predictors included hematocrit, platelet count, and lymphocyte count for dengue shock syndrome.

## Abstract

Dengue severity prediction models are usually developed using hospitalized patient data, but triage and hospital admission are mainly evaluated in outpatient settings. This study developed models using clinical and laboratory data from patients in outpatient settings during the febrile phase. Data from two cohort studies in Vietnam and Thailand were used to develop and validate six models: logistic regression with warning signs, Lasso-selected logistic regression, random forest, extreme gradient boosted classification, support vector machine, and artificial neural network. Models predicted dengue shock syndrome (DSS) as the primary endpoint and moderate plasma leakage and/or DSS as the secondary endpoint. We assessed model performance, discrimination, and calibration, using sensitivity, specificity, accuracy, Brier score, AUROC, CITL, calibration slope, calibration plots, and decision curve analysis. The optimal model was the Lasso-selected logistic regression for predicting DSS and the combined endpoint of moderate plasma leakage and/or DSS (Brier score: 0.044 [95% CI 0.043, 0.044] and 0.104 [95% CI 0.104, 0.105]; AUROC: 0.789 [95% CI 0.787, 0.791] and 0.741 [95% CI 0.740, 0.742]). We identified hematocrit, platelet count, lymphocyte count, and aspartate aminotransferase as predictors for DSS, and abdominal pain or tenderness, vomiting, mucosal bleeding, white blood cell count, lymphocyte count, platelet count, aspartate aminotransferase, and serum albumin as predictors for the secondary endpoint. Logistic regression and machine learning models using clinical and laboratory data during the febrile phase can support early prediction of severe disease in outpatient settings. Integrating risk prediction models into a decision support system could improve triage and optimize healthcare and resource allocation in endemic and resource-limited areas.

Most dengue risk models are developed from hospitalized patient data, despite triage occurring in outpatient settings. Few studies have examined early outpatient predictors, and none have undergone external validation across countries. In this study, we developed and validated dengue risk prediction models using logistic regression and machine learning with outpatient data from Vietnam and Thailand. Our prior systematic review and expert consultation informed predictor selection. The models outperformed the WHO warning signs alone in predicting dengue shock syndrome and moderate plasma leakage, demonstrating better discrimination and calibration. Models incorporating four to eight routinely collected clinical parameters show promise for guiding early triage and improving care allocation, especially in resource-limited, dengue-endemic settings.