Prediction of Dengue Disease Severity among Pediatric Thai Patients Using Early Clinical Laboratory Indicators

Abstract

BackgroundDengue 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 FindingsWe 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.ConclusionsThis 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.

A Three-Component Biomarker Panel for Prediction of Dengue Hemorrhagic Fever

“…In a cohort of Thai children presenting within 72 h of symptoms, CART analysis was developed to identify features that predict severe dengue illness. 18 The best CART model produced had a 97% sensitivity for predicting severe dengue illness, but only correctly excluded 48% of non-severe cases. 18 The splitting variables identified included white blood cell, monocytes, platelet counts, and hematocrit.…”

“…18 The best CART model produced had a 97% sensitivity for predicting severe dengue illness, but only correctly excluded 48% of non-severe cases. 18 The splitting variables identified included white blood cell, monocytes, platelet counts, and hematocrit. 18 Another CART model that differentiated acute dengue from non-dengue febrile illness identified thrombocytopenia as an important discriminating variable.…”

“…18 The splitting variables identified included white blood cell, monocytes, platelet counts, and hematocrit. 18 Another CART model that differentiated acute dengue from non-dengue febrile illness identified thrombocytopenia as an important discriminating variable. 19 Our study confirms the prognostic importance of reduced platelet counts for DHF.…”

Correlation of C-reactive protein and neutrophil counts as early indicators of severe dengue in children

“…3 Dengue has a broad clinical manifestation, ranging from non-specific febrile illness, dengue fever (DF) with and without warning signs and severe dengue, according to 2009 WHO revised classification. 4 It is a single stranded, non-segmented RNA virus, belongs to genus flavivirus, family flaviviridae. Early recognition of dengue is mostly challenging as, the initial symptoms are often nonspecific, viremia may be below detectable levels and serological confirmation in dengue is late in the course of illness.…”

Developing a Decision Support Testing Algorithm to Detect Severity Level of Dengue

“…A classification and regression tree (CART) analysis using the parameters white blood cell count, percent monocytes, platelet count and haematocrit was designed based on a cohort of Thai children at 72 hours from onset of illness. This study achieved a 97 % sensitivity in detecting patients who proceeded into DSS (Potts, et al, 2010). CART decision tree based on platelets, IL-10 and lymphocyte resulted in a model with an accuracy of 84.6 % for DHF and 84.0% for DF (Brasier, et al, 2012).…”