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Selected Works of Alan Rothman
Article
Classification of dengue illness based on readily available laboratory data
Preventive and Behavioral Medicine Publications
  • James A. Potts, University of Massachusetts Medical School
  • Stephen J. Thomas, Armed Forces Research Institute of Medical Sciences
  • Anon Srikiatkhachorn, University of Massachusetts Medical School
  • Pra-On Supradish, Queen Sirikit National Institute of Child Health
  • Wenjun Li, University of Massachusetts Medical School
  • Ananda Nisalak, Armed Forces Research Institute of Medical Sciences
  • Suchitra Nimmannitya, Queen Sirikit National Institute of Child Health
  • Timothy P. Endy, Upstate Medical University
  • Daniel H. Libraty, University of Massachusetts Medical School
  • Robert V. Gibbons, Armed Forces Research Institute of Medical Sciences
  • Sharone Green, University of Massachusetts Medical School
  • Alan Rothman, University of Massachusetts Medical School
  • Siripen Kalayanarooj, Queen Sirikit National Institute of Child Health
Link
UMMS Affiliation
Center for Infectious Disease and Vaccine Research; Department of Medicine, Division of Infectious Diseases and Immunology; Department of Medicine, Division of Preventive and Behavioral Medicine
Publication Date
2010-10-5
Document Type
Article
Subjects
Adolescent; Algorithms; Child; Child, Preschool; Dengue; Female; Humans; Infant; Male; Multivariate Analysis; Odds Ratio; Physicians; Retrospective Studies; Risk Factors; Sensitivity and Specificity; Severity of Illness Index; World Health Organization
Disciplines
Abstract

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.

DOI of Published Version
10.4269/ajtmh.2010.10-0135
Source
Potts JA, Thomas SJ, Srikiatkhachorn A, Supradish PO, Li W, Nisalak A, Nimmannitya S, Endy TP, Libraty DH, Gibbons RV, Green S, Rothman AL, Kalayanarooj S. Classification of dengue illness based on readily available laboratory data. Am J Trop Med Hyg. 2010 Oct;83(4):781-8. doi: 10.4269/ajtmh.2010.10-0135.
Related Resources
Link to Article in PubMed
PubMed ID
20889865
Citation Information
James A. Potts, Stephen J. Thomas, Anon Srikiatkhachorn, Pra-On Supradish, et al.. "Classification of dengue illness based on readily available laboratory data" Vol. 83 Iss. 4 (2010) ISSN: 0002-9637 (Linking)
Available at: http://works.bepress.com/alan_rothman/31/