Lee Hyelan, Srikiatkhachorn Anon, Kalayanarooj Siripen, Farmer Aaron R, Park Sangshin
Graduate School of Urban Public Health, University of Seoul, Republic of Korea.
Department of Urban Big Data Convergence, University of Seoul, Republic of Korea.
J Infect Dis. 2025 Feb 4;231(1):241-250. doi: 10.1093/infdis/jiae366.
This study aimed to compare the predictive performance of 3 statistical models-logistic regression, classification tree, and structural equation model (SEM)-in predicting severe dengue illness.
We adopted a modified classification of dengue illness severity based on the World Health Organization's 1997 guideline. We constructed predictive models using demographic factors and laboratory indicators on the day of fever occurrence, with data from 2 hospital cohorts in Thailand (257 Thai children). Different predictive models for each category of severe dengue illness were developed employing logistic regression, classification tree, and SEM. The model's discrimination abilties were analyzed with external validation data sets from 55 and 700 patients not used in model development.
From external validation based on predictors on the day of presentation to the hospital, the area under the receiver operating characteristic curve was from 0.65 to 0.84 for the regression models from 0.73 to 0.85 for SEMs. Classification tree models showed good results of sensitivity (0.95 to 0.99) but poor specificity (0.10 to 0.44).
Our study showed that SEM is comparable to logistic regression or classification tree, which was widely used for predicting severe forms of dengue.
本研究旨在比较3种统计模型——逻辑回归、分类树和结构方程模型（SEM）——预测重症登革热疾病的性能。
我们基于世界卫生组织1997年的指南采用了一种改良的登革热疾病严重程度分类方法。我们利用发热当天的人口统计学因素和实验室指标构建预测模型，数据来自泰国的2个医院队列（257名泰国儿童）。采用逻辑回归、分类树和SEM为每类重症登革热疾病建立不同的预测模型。利用未用于模型构建的55名和700名患者的外部验证数据集分析模型的辨别能力。
根据患者入院当天预测指标的外部验证，回归模型的受试者操作特征曲线下面积为0.65至0.84，结构方程模型为0.73至0.85。分类树模型显示出良好的灵敏度结果（0.95至0.99），但特异度较差（0.10至0.44）。
我们的研究表明，结构方程模型与广泛用于预测重症登革热的逻辑回归或分类树相当。
