Dengue hemorrhagic fever grade III with diabetic ketoacidosis: a case report.

A 16-year-old, previously healthy Thai girl presented with DHF grade III. Fifteen hours after the first episode of shock, she had received an excessive amount of crystalloid isotonic solution and 20 ml per kilograms of Dextran-40 however she still had persistently rapid pulse rate and high hematocrit but also had polyuria with more than 4 ml/kg/hr of urine output. She was re-evaluated. Clinical signs showed severe dehydration with some ascites without signs of pleural effusion. Blood gas revealed increased anion gap metabolic acidosis. The cause of polyuria and metabolic acidosis was identified with hyperglycemia, ketouria and glucosuria. Afterwards she was diagnosed and treated as DHF grade III and DKA. Besides insulin administration, fluid resuscitation was very crucial. Intravenous fluid rehydration was needed while the unnecessary extra-volume could cause massive plasma leakage and later on fluid overload. Volume replacement was adjusted to degree of dehydration when signs of volume overload were monitored closely. She was out of DKA at 14 hours after the start of insulin and the intravenous fluid was stopped at 27 hours (36 hours after the first episode of shock). The final diagnosis was DHF grade III, diabetes mellitus with DKA and hepatitis.

Classification of dengue illness based on readily available laboratory data.

The aim of this study was to examine retrospective dengue-illness classification using only clinical laboratory data, without relying on X-ray, ultrasound, or percent hemoconcentration. We analyzed data from a study of children who presented with acute febrile illness to two hospitals in Thailand. Multivariable logistic regression models were used to distinguish: (1) dengue hemorrhagic fever (DHF) versus dengue fever (DF), (2) DHF versus DF + other febrile illness (OFI), (3) dengue versus OFI, and (4) severe dengue versus non-severe dengue + OFI. Data from the second hospital served as a validation set. There were 1,227 patients in the analysis. The sensitivity of the models ranged from 89.2% (dengue versus OFI) to 79.6% (DHF versus DF). The models showed high sensitivity in the validation dataset. These models could be used to calculate a probability and classify patients based on readily available clinical laboratory data, and they will need to be validated in other dengue-endemic regions.

Prediction of dengue disease severity among pediatric Thai patients using early clinical laboratory indicators.

BACKGROUND: Dengue virus is endemic in tropical and sub-tropical resource-poor countries. Dengue illness can range from a nonspecific febrile illness to a severe disease, Dengue Shock Syndrome (DSS), in which patients develop circulatory failure. Earlier diagnosis of severe dengue illnesses would have a substantial impact on the allocation of health resources in endemic countries. METHODS AND FINDINGS: We compared clinical laboratory findings collected within 72 hours of fever onset from a prospective cohort children presenting to one of two hospitals (one urban and one rural) in Thailand. Classification and regression tree analysis was used to develop diagnostic algorithms using different categories of dengue disease severity to distinguish between patients at elevated risk of developing a severe dengue illness and those at low risk. A diagnostic algorithm using WBC count, percent monocytes, platelet count, and hematocrit achieved 97% sensitivity to identify patients who went on to develop DSS while correctly excluding 48% of non-severe cases. Addition of an indicator of severe plasma leakage to the WHO definition led to 99% sensitivity using WBC count, percent neutrophils, AST, platelet count, and age. CONCLUSIONS: This study identified two easily applicable diagnostic algorithms using early clinical indicators obtained within the first 72 hours of illness onset. The algorithms have high sensitivity to distinguish patients at elevated risk of developing severe dengue illness from patients at low risk, which included patients with mild dengue and other non-dengue febrile illnesses. Although these algorithms need to be validated in other populations, this study highlights the potential usefulness of specific clinical indicators early in illness.