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Abstract Dengue shock syndrome (DSS) substantially worsens the prognosis of children with dengue infection. This study aimed to develop a simple clinical tool to predict the risk of DSS. A cohort of 2221 Thai children with a confirmed dengue infection who were admitted to King Chulalongkorn Memorial Hospital between 1987 and 2007 was conducted. Another data set from a previous publication comprising 2,301 Vietnamese children with dengue infection was employed to create a pooled data set, which was randomly split into training ( n = 3182), testing ( n = 697) and validating ( n = 643) datasets. Logistic regression was compared to alternative machine learning algorithms to derive the most predictive model for DSS. 4522 children, including 899 DSS cases (758 Thai and 143 Vietnamese children) with a mean age of 9.8 ± 3.4 years, were analyzed. Among the 12 candidate clinical parameters, the Bayesian Model Averaging algorithm retained the most predictive subset of five covariates, including body weight, history of vomiting, liver size, hematocrit levels, and platelet counts. At an Area Under the Curve (AUC) value of 0.85 (95% CI: 0.81–0.90) in testing data set, logistic regression outperformed random forest, XGBoost and support vector machine algorithms, with AUC values being 0.82 (0.77–0.88), 0.82 (0.76–0.88), and 0.848 (0.81–0.89), respectively. At its optimal threshold, this model had a sensitivity of 0.71 (0.62–0.80), a specificity of 0.84 (0.81–0.88), and an accuracy of 0.82 (0.78–0.85) on validating data set with consistent performance across subgroup analyses by age and gender. A logistic regression‐based nomogram was developed to facilitate the application of this model. This work introduces a simple and robust clinical model for DSS prediction that is well‐tailored for children in resource‐limited settings.
Tran et al. (Thu,) studied this question.