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.
Potts
James A
JA
Center for Infectious Disease and Vaccine Research and Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA. James.Potts@umassmed.edu
Thomas
Stephen J
SJ
Srikiatkhachorn
Anon
A
Supradish
Pra-on
PO
Li
Wenjun
W
Nisalak
Ananda
A
Nimmannitya
Suchitra
S
Endy
Timothy P
TP
Libraty
Daniel H
DH
Gibbons
Robert V
RV
Green
Sharone
S
Rothman
Alan L
AL
Kalayanarooj
Siripen
S
D000293:Adolescent; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D003715:Dengue / Q000145:classification* / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D015999:Multivariate Analysis; D016017:Odds Ratio; D010820:Physicians; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D014944:World Health Organization