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Clinical Trial
. 2015 Apr 2;9(4):e0003638.
doi: 10.1371/journal.pntd.0003638. eCollection 2015 Apr.

Sensitivity and specificity of a novel classifier for the early diagnosis of dengue

Nguyen Minh Tuan  1 , Ho Thi Nhan  2 , Nguyen Van Vinh Chau  3 , Nguyen Thanh Hung  1 , Ha Manh Tuan  4 , Ta Van Tram  5 , Nguyen Le Da Ha  6 , Phan Loi  7 , Han Khoi Quang  8 , Duong Thi Hue Kien  2 , Sonya Hubbard  9 , Tran Nguyen Bich Chau  2 , Bridget Wills  10 , Marcel Wolbers  10 , Cameron P Simmons  11
Affiliations
Clinical Trial

Sensitivity and specificity of a novel classifier for the early diagnosis of dengue

Nguyen Minh Tuan et al. PLoS Negl Trop Dis. .

Abstract

Background: 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.

Methods: 5729 children with fever of <72 hrs 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.

Results: 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%).

Conclusions: 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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Performance of the Early Dengue Classifier (EDC) in all subjects.
Figure A displays possible sensitivity/specificity trade-offs for different cut-off values and the distance from the corresponding points on the ROC curve to the upper left corner (perfect model). Figure B displays the receiver operating characteristic (ROC) curve. Figure C is a calibration plot. It displays a scatterplot-smoother of predicted versus observed risks (dotted line), predicted versus observed risks for ten patient strata of equal size grouped according to predicted risks (triangles) and the ideal identity line (dashed line). The rugs at the bottom of the graphs characterize the distribution of predicted risks in true dengue and non-dengue cases, respectively.
Fig 2
Fig 2. Nomogram of the Early Dengue Classifier (EDC) to predict the risk of dengue.
A horizontal line from a predictor value to the “Points” axis assigns points to the 3 required variables age, platelet count (PLT), and white blood cell count (WBC). The sum of these points (total points) can then be translated to the corresponding predicted risk of dengue. As an example, a 9-year-old patient with a PLT of 100x103/mm3, and a WBC of 5x103/mm3 has a score of 15+32+84 = 131, and the corresponding risk of dengue is about 70%. Note: As <1% of patients had platelet (PLT) count >500x103/mm3 or white blood cell (WBC) count >30x103/mm3, for better visualization, PLT and WBC counts were truncated at 500x103/mm3 and 30x103/mm3 respectively.
See this image and copyright information in PMC

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