Use of structural equation models to predict dengue illness phenotype

Background Early recognition of dengue, particularly patients at risk for plasma leakage, is important to clinical management. The objective of this study was to build predictive models for dengue, dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS) using structural equation modelling (SEM), a statistical method that evaluates mechanistic pathways. Methods/Findings We performed SEM using data from 257 Thai children enrolled within 72 h of febrile illness onset, 156 with dengue and 101 with non-dengue febrile illnesses. Models for dengue, DHF, and DSS were developed based on data obtained three and one day(s) prior to fever resolution (fever days -3 and -1, respectively). Models were validated using data from 897 subjects who were not used for model development. Predictors for dengue and DSS included age, tourniquet test, aspartate aminotransferase, and white blood cell, % lymphocytes, and platelet counts. Predictors for DHF included age, aspartate aminotransferase, hematocrit, tourniquet test, and white blood cell and platelet counts. The models showed good predictive performances in the validation set, with area under the receiver operating characteristic curves (AUC) at fever day -3 of 0.84, 0.67, and 0.70 for prediction of dengue, DHF, and DSS, respectively. Predictive performance was comparable using data based on the timing relative to enrollment or illness onset, and improved closer to the critical phase (AUC 0.73 to 0.94, 0.61 to 0.93, and 0.70 to 0.96 for dengue, DHF, and DSS, respectively). Conclusions Predictive models developed using SEM have potential use in guiding clinical management of suspected dengue prior to the critical phase of illness.
Author summary Dengue virus infection is one of the most critical public health issues, particularly in tropical and subtropical regions. This study developed statistical predictive models using the data obtained from 257 Thai children for dengue, dengue hemorrhagic fever, and dengue shock syndrome using structural equation modelling (SEM). We performed SEM based on clinical and laboratory factors on three and one day(s) prior to fever resolution. Our SEM models showed that age, tourniquet test, aspartate aminotransferase, and white blood cell, % lymphocytes, and platelet counts on three days prior to fever resolution were important risk factors for dengue and dengue hemorrhagic fever. Age, aspartate aminotransferase, hematocrit, tourniquet test, and white blood cell and platelet counts were important risk factors for dengue shock syndrome. Our predictive models showed good performances in the validation subjects (n = 897) who were not used for SEM, and thus we concluded that our predictive models can be practically used to guide clinical management of suspected dengue patients. Our study also showed that SEM can be used to predict the developments or severities of other illnesses.