Sensitivity and Specificity of a Novel Classifier for the Early Diagnosis of Dengue

Nguyen Minh Tuan, Ho Thi Nhan, Nguyen Van Vinh Chau, Nguyen Thanh Hung, Ha Manh Tuan, Ta Van Tram, Nguyen Le Da Ha, Phan Loi, Han Khoi Quang, Duong Thi Hue Kien, Sonya Hubbard, Tran Nguyen Bich Chau, Bridget Wills, Marcel Wolbers, Cameron P. Simmons

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

Abstract

Dengue is the commonest arboviral disease of humans. An early and accurate diagnosis of dengue can support clinical management, surveillance and disease control and is central to achieving the World Health Organisation target of a 50% reduction in dengue case mortality by 2020. 5729 children with fever of <72hrs duration were enrolled into this multicenter prospective study in southern Vietnam between 2010-2012. A composite of gold standard diagnostic tests identified 1692 dengue cases. Using statistical methods, a novel Early Dengue Classifier (EDC) was developed that used patient age, white blood cell count and platelet count to discriminate dengue cases from non-dengue cases. The EDC had a sensitivity of 74.8% (95%CI: 73.0-76.8%) and specificity of 76.3% (95%CI: 75.2-77.6%) for the diagnosis of dengue. As an adjunctive test alongside NS1 rapid testing, sensitivity of the composite test was 91.6% (95%CI: 90.4-92.9%). We demonstrate that the early diagnosis of dengue can be enhanced beyond the current standard of care using a simple evidence-based algorithm. The results should support patient management and clinical trials of specific therapies.

Original languageEnglish
Article numbere0003638
Number of pages15
JournalPLoS Neglected Tropical Diseases
Volume9
Issue number4
DOIs
Publication statusPublished - 2 Apr 2015
Externally publishedYes

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