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Article
Prediction of dengue disease severity among pediatric Thai patients using early clinical laboratory indicators
Preventive and Behavioral Medicine Publications and Presentations
  • James A. Potts, University of Massachusetts Medical School
  • Robert V. Gibbons, Armed Forces Research Institute for Medical Sciences
  • Alan L. Rothman, University of Massachusetts Medical School
  • Anon Srikiatkhachorn, University of Massachusetts Medical School
  • Stephen J. Thomas, Armed Forces Research Institute of Medical Sciences
  • Pra-On Supradish, Queen Sirikit National Institute of Child Health
  • Stephenie C. Lemon, University of Massachusetts Medical School
  • Daniel H. Libraty, University of Massachusetts Medical School
  • Sharone Green, University of Massachusetts Medical School
  • Siripen Kalayanarooj, Queen Sirikit National Institute of Child Health
UMMS Affiliation
Center for Infectious Disease and Vaccine Research; Graduate School of Biomedical Sciences, Clinical and Population Health Research Program; Department of Medicine, Division of Preventive and Behavioral Medicine; Department of Medicine, Division of Infectious Diseases and Immunology
Date
8-7-2010
Document Type
Article
Subjects
Adolescent; Age Factors; Algorithms; Aspartate Aminotransferases; Child; Child, Preschool; Cohort Studies; Dengue; Dengue Virus; Female; Hematocrit; Humans; Infant; Leukocyte Count; Leukocytes; Male; Platelet Count; Prognosis; Prospective Studies; Sensitivity and Specificity; Severity of Illness Index; Thailand
Abstract
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.
Rights and Permissions
Citation: PLoS Negl Trop Dis. 2010 Aug 3;4(8):e769. Link to article on publisher's site
Related Resources
Link to Article in PubMed
PubMed ID
20689812
Citation Information
James A. Potts, Robert V. Gibbons, Alan L. Rothman, Anon Srikiatkhachorn, et al.. "Prediction of dengue disease severity among pediatric Thai patients using early clinical laboratory indicators" Vol. 4 Iss. 8 (2010) ISSN: 1935-2727 (Linking)
Available at: http://works.bepress.com/stephenie_lemon/10/