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BACKGROUND: Dengue fever is a re-emerging viral disease commonly occurring in tropical and subtropical areas. The clinical features and abnormal laboratory test results of dengue infection are similar to those of other febrile illnesses; hence, its accurate and timely diagnosis for providing appropriate treatment is difficult. Delayed diagnosis may be associated with inappropriate treatment and higher risk of death. Early and correct diagnosis can help improve case management and optimise the use of resources such as hospital staff, beds, and intensive care equipment. The goal of this study was to develop a predictive model to characterise dengue severity based on early clinical and laboratory indicators using data mining and statistical tools. METHODS: We retrieved data from a study of febrile illness in children at Angkor Hospital for Children, Cambodia. Of 1225 febrile episodes recorded, 198 patients were confirmed to have dengue. A classification and regression tree (CART) was used to construct a predictive decision tree for severe dengue, while logistic regression analysis was used to independently quantify the significance of each parameter in the decision tree. RESULTS: A decision tree algorithm using haematocrit, Glasgow Coma Score, urine protein, creatinine, and platelet count predicted severe dengue with a sensitivity, specificity, and accuracy of 60.5%, 65% and 64.1%, respectively. CONCLUSIONS: The decision tree we describe, using five simple clinical and laboratory indicators, can be used to predict severe cases of dengue among paediatric patients on admission. This algorithm is potentially useful for guiding a patient-monitoring plan and outpatient management of fever in resource-poor settings.

Original publication

DOI

10.1186/s12887-018-1078-y

Type

Journal article

Journal

BMC Pediatr

Publication Date

13/03/2018

Volume

18

Keywords

Cambodia, Children, Classification tree, Data mining, Dengue, Severity, Adolescent, Child, Child, Preschool, Clinical Decision-Making, Decision Trees, Dengue, Female, Humans, Infant, Infant, Newborn, Logistic Models, Male, Predictive Value of Tests, Retrospective Studies, Sensitivity and Specificity, Severe Dengue, Severity of Illness Index