Title

Classification of dengue illness based on readily available laboratory data

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

Date

10-5-2010

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

Infectious Disease | Virus Diseases

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.

Comments

Citation: 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