Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Excellence Center for Critical Care Nephrology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
J Med Virol. 2024 Aug;96(8):e29874. doi: 10.1002/jmv.29874.
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.
登革出血热（Dengue shock syndrome，DSS）显著恶化了登革热感染患儿的预后。本研究旨在开发一种简单的临床工具，以预测 DSS 发生的风险。对 1987 年至 2007 年间在泰国朱拉隆功国王纪念医院接受治疗的 2221 例确诊登革热感染的泰国儿童队列进行了研究。此外，还使用了先前发表的包含 2301 例登革热感染越南儿童的数据集，以建立一个合并数据集，并将其随机分为训练（n=3182）、测试（n=697）和验证（n=643）数据集。本研究比较了逻辑回归和其他机器学习算法，以确定预测 DSS 最准确的模型。共纳入 4522 例患儿，包括 899 例 DSS 患儿（758 例泰国儿童和 143 例越南儿童），平均年龄为 9.8±3.4 岁。在 12 个候选临床参数中，贝叶斯模型平均算法保留了 5 个预测变量的最具预测性子集，包括体重、呕吐史、肝脾大小、血细胞比容水平和血小板计数。在测试数据集的曲线下面积（Area Under the Curve，AUC）值为 0.85（95%CI：0.81-0.90）时，逻辑回归优于随机森林、XGBoost 和支持向量机算法，AUC 值分别为 0.82（0.77-0.88）、0.82（0.76-0.88）和 0.848（0.81-0.89）。在验证数据集的最佳截断值处，该模型具有 0.71（0.62-0.80）的敏感性、0.84（0.81-0.88）的特异性和 0.82（0.78-0.85）的准确性，在年龄和性别亚组分析中表现一致。开发了一个基于逻辑回归的列线图，以方便该模型的应用。本研究建立了一种简单而稳健的 DSS 预测临床模型，特别适用于资源有限环境中的儿童。
