Abstract
BackgroundDengue 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.MethodsWe 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.ResultsA 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.ConclusionsThe 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.
Highlights
Dengue fever is a re-emerging viral disease commonly occurring in tropical and subtropical areas
Out of 1180 enrolled children, there were 69 deaths, the causes of which were: clinical pneumonia with no organism/virus identified (12 cases, 27.5%), dengue virus infection, and melioidosis. 941 non-dengue episodes and 86 episodes with no samples available were excluded from this analysis
Our model showed that a serum creatinine level > 84 mmol/l (4.6 mg/dl) was associated with severe dengue, a value similar to that found in Thai paediatric patients with dengue haemorrhagic fever (DHF), whose mean serum creatinine was 4.9 mg/dl
Summary
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; its accurate and timely diagnosis for providing appropriate treatment is difficult. Dengue fever causes a high burden of disease and mortality across tropical and subtropical regions in Southeast Asia, Africa, the Western Pacific, and the Americas [1]. During the early stages of dengue, the presence of nonspecific febrile illness makes precise diagnosis strikingly. If not appropriately managed, may lead to rapid death, in children [7, 8]. The lack of necessary laboratory facilities, in remote, rural areas, may cause difficultly in discriminating dengue infection from OFI [9]. Dengue is one of the most common vector-borne diseases in Southeast Asia, and one of the most important mosquito-borne viral diseases with an epidemic potential in the world [10]
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