Accuracy of dengue clinical diagnosis with and without NS1 antigen rapid test: Comparison between human and Bayesian network model decision.

Chaitawat Sa-Ngamuang,Sopon Iamsirithaworn,Watcharapong Piyaphanee,Viravarn Luvira,Saranath Lawpoolsri,Peter Haddawy,Laith Yakob

doi:10.1371/journal.pntd.0006573

Abstract

Differentiating dengue patients from other acute febrile illness patients is a great challenge among physicians. Several dengue diagnosis methods are recommended by WHO. The application of specific laboratory tests is still limited due to high cost, lack of equipment, and uncertain validity. Therefore, clinical diagnosis remains a common practice especially in resource limited settings. Bayesian networks have been shown to be a useful tool for diagnostic decision support. This study aimed to construct Bayesian network models using basic demographic, clinical, and laboratory profiles of acute febrile illness patients to diagnose dengue. Data of 397 acute undifferentiated febrile illness patients who visited the fever clinic of the Bangkok Hospital for Tropical Diseases, Thailand, were used for model construction and validation. The two best final models were selected: one with and one without NS1 rapid test result. The diagnostic accuracy of the models was compared with that of physicians on the same set of patients. The Bayesian network models provided good diagnostic accuracy of dengue infection, with ROC AUC of 0.80 and 0.75 for models with and without NS1 rapid test result, respectively. The models had approximately 80% specificity and 70% sensitivity, similar to the diagnostic accuracy of the hospital’s fellows in infectious disease. Including information on NS1 rapid test improved the specificity, but reduced the sensitivity, both in model and physician diagnoses. The Bayesian network model developed in this study could be useful to assist physicians in diagnosing dengue, particularly in regions where experienced physicians and laboratory confirmation tests are limited.

Highlights

Dengue is considered a global public health threat due to its potential to rapidly spread across countries [1]
In many parts of the world, dengue diagnosis still relies on clinical manifestation and basic laboratory tests
Comparison between Bayesian network and human on dengue clinical diagnosis funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Summary

Introduction

Dengue is considered a global public health threat due to its potential to rapidly spread across countries [1]. Dengue infections lead to a wide range of clinical outcomes. Several laboratory confirmation tests for dengue infection are currently available, including serological test, virus detection, antigen detection, and genome detection. The use of these tests is still limited since they require sophisticated equipment and well-trained staff [9,10,11]. Since the serological test requires a pair of serum samples taken two weeks apart to confirm a rise in IgM/IgG dengue antibody, this test may not be practical for initial diagnosis of dengue [12]. Virus and genome detection can directly identify dengue viral infection. Virus detection must be performed during the viremic stage, within 1–5 days of onset of fever [1]

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Conclusion