Classification of dengue illness based on readily available laboratory data
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
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
Infectious Disease | Virus Diseases
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.