Research Article: Decision Tree Algorithms Predict the Diagnosis and Outcome of Dengue Fever in the Early Phase of Illness

Date Published: March 12, 2008

Publisher: Public Library of Science

Author(s): Lukas Tanner, Mark Schreiber, Jenny G. H. Low, Adrian Ong, Thomas Tolfvenstam, Yee Ling Lai, Lee Ching Ng, Yee Sin Leo, Le Thi Puong, Subhash G. Vasudevan, Cameron P. Simmons, Martin L. Hibberd, Eng Eong Ooi, Jeremy Farrar

Abstract: BackgroundDengue is re-emerging throughout the tropical world, causing frequent recurrent epidemics. The initial clinical manifestation of dengue often is confused with other febrile states confounding both clinical management and disease surveillance. Evidence-based triage strategies that identify individuals likely to be in the early stages of dengue illness can direct patient stratification for clinical investigations, management, and virological surveillance. Here we report the identification of algorithms that differentiate dengue from other febrile illnesses in the primary care setting and predict severe disease in adults.Methods and FindingsA total of 1,200 patients presenting in the first 72 hours of acute febrile illness were recruited and followed up for up to a 4-week period prospectively; 1,012 of these were recruited from Singapore and 188 from Vietnam. Of these, 364 were dengue RT-PCR positive; 173 had dengue fever, 171 had dengue hemorrhagic fever, and 20 had dengue shock syndrome as final diagnosis. Using a C4.5 decision tree classifier for analysis of all clinical, haematological, and virological data, we obtained a diagnostic algorithm that differentiates dengue from non-dengue febrile illness with an accuracy of 84.7%. The algorithm can be used differently in different disease prevalence to yield clinically useful positive and negative predictive values. Furthermore, an algorithm using platelet count, crossover threshold value of a real-time RT-PCR for dengue viral RNA, and presence of pre-existing anti-dengue IgG antibodies in sequential order identified cases with sensitivity and specificity of 78.2% and 80.2%, respectively, that eventually developed thrombocytopenia of 50,000 platelet/mm3 or less, a level previously shown to be associated with haemorrhage and shock in adults with dengue fever.ConclusionThis study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance.

Partial Text: Dengue fever/dengue haemorrhagic fever (DF/DHF) is a re-emerging disease throughout the tropical world. The disease is caused by four closely related dengue viruses, which are transmitted by the Aedes mosquitoes, principally Aedes aegypti[1]. DHF and dengue shock syndrome (DSS) represent the severe end of the disease spectrum, which if not properly managed, would result in significant mortality. The pathophysiology of severe DHF and DSS is characterized by plasma leakage as a result of alteration in microvascular permeability [2]. There is as yet no vaccine or specific antiviral therapy for DF/DHF and management of cases remains largely supportive [3].

We constructed a decision tree for dengue diagnosis with 1,200 patients with acute febrile illness. Of these, 1,012 were recruited from the EDEN study and 188 from Vietnam. The EDEN cohort consisted of 173 DF, 3 DHF and 836 non-dengue cases while the Vietnam cohort consisted of 168 DHF and 20 DSS cases, resulting in a total of 364 dengue and 836 non-dengue cases used for our diagnostic tree construction.

The lack of evidence-based diagnostic algorithm for early dengue diagnosis as well as prognostic triage strategies limits effective patient management, use of healthcare resources and disease surveillance efforts. For instance, over 80% of the total dengue cases in Singapore are admitted for hospitalised care, mostly to monitor for signs of clinical deterioration. Prognostication in the early stages of dengue illness could significantly influence clinical management and the use of healthcare resources, particularly during dengue outbreaks, such as occurred in Singapore in 2005 where up to 8% of all acute hospitals beds available were occupied by dengue cases [19].

Source:

http://doi.org/10.1371/journal.pntd.0000196

 

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