Introduction 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]. Dengue illness is often confused with other viral febrile states, confounding both clinical management [4]–[6] and disease surveillance for viral transmission prevention [7]. This difficulty is especially striking during the early phase of illness, where non-specific clinical symptoms and signs accompany the febrile illness [4]. More definitive symptoms, such as retro-orbital pain, and clinical signs, such as petechiae, do not appear until the later stages of illness, if at all. Definitive early dengue diagnosis thus requires laboratory tests and those suitable for use at this stage of illness are either costly, such as RT-PCR for dengue; not sufficiently rapid, such as virus isolation; or undergoing field trials, such as ELISA for NS1 protein of dengue virus [8],[9]. Furthermore, many dengue endemic places lack the necessary laboratory infrastructure or support [7] and thus a scheme for reliable clinical diagnosis, using data that can be obtained routinely, would be useful for early recognition of dengue fever, not only for case management but also for dengue surveillance. The current World Health Organization (WHO) scheme for classifying dengue infection (Table S1) makes use of symptoms and signs that are often not present in the first few days of illness, and thus not a guide for early diagnosis, but are instead designed for monitoring disease progression for clinical management of the severe DHF/DSS. Other attempts at identifying clinical features for the diagnosis of dengue disease have made use of univariate or multivariate analysis of clinical symptoms and signs, haematological or biochemical parameters [10],[11]. Although such studies provide a list of symptoms and signs that could be associated with dengue disease, how these should be applied for clinical diagnosis is not apparent. Evidence-based triage strategies that identify individuals likely to have dengue infection in the early stages of illness are needed to direct patient stratification in clinical investigations, management and healthcare resource planning. To address this goal, we show here that a decision tree approach can be useful to develop an intuitive diagnostic algorithm, using clinical and haematological parameters, that is able to distinguish dengue from non-dengue disease in the first 72 hours of illness. We also demonstrate a proof-of-concept that such an approach can be useful for early dengue disease prognostication. Materials and Methods Patients and clinical methods Ethical considerations The study protocol was approved by each organization's institutional review board. Patient enrolment, clinical and epidemiological data collection within the National Healthcare Group, Singapore was approved by the NHG IRB (DSRB B/05/013). Patient enrolment, clinical and epidemiological data collection in Dong Thap Hospital was approved by the hospital scientific and ethical committee as well as the Oxfordshire Tropical Research Ethical Committee, UK. Enrolment of study participants was conditional on appropriate informed consent administered by a study research nurse. All biological materials collected were anonymized after completion of demographic and clinical data collection. Screening and recruitment The protocol for patient recruitment in Singapore (the early dengue infection and outcome (EDEN) study) was described previously [12]. Adult patients (age >18 years) presenting at selected primary care polyclinics within 72 hours of onset of acute febrile illness and without rhinitis or clinically obvious alternative diagnoses for fever were eligible for study inclusion. Upon consent, anonymized demographic, clinical and epidemiological information were collected on a standardized data entry form on 3 occasions: 1–3 days post-onset of fever (1st visit), 4–7 days post-onset of fever (2nd visit) and 3–4 weeks post-onset of fever (3rd visit). Venous blood was also collected for haematological, virological and serological analyses at every visit. Remaining serum and blood were anonymized and stored at −80°C until use. The list of parameters monitored in this study is shown in the supplementary Table S2. Children or adults in whom there was a clinical suspicion of dengue were recruited within 72 hours of illness onset in Dong Thap Hospital, Vietnam. Blood samples were collected for diagnostic investigations at study enrolment and again at hospital discharge. Clinical data were collected daily on standard case record forms. Laboratory Methods Haematology A full blood count was performed on anticoagulated whole blood collected at all time points. A bench-top, FDA-approved haematocytometer was used for this application (iPoch-100, Sysmex, Japan). Calibration by internal and external QC controls was also performed on a regular basis. Serology and antigen detection IgM and IgG antibodies against dengue virus were detected using commercially available ELISAs (PanBio, Brisbane, Australia) according to manufacturer's instructions. Reverse-transcription polymerase chain reaction (RT-PCR) RNAs were extracted from the first serum portion or virus culture supernatant using QIAamp Viral RNA mini kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. RT-PCR to detect dengue viral RNA was carried out using a set of generic pan-dengue primers that targeted the 3′ non-coding region of dengue viruses as previously described [13]. Results were analysed with LightCycler software version 3.5 (Roche Diagnostics, Mannheim, Germany). Reactions with high crossover threshold (Ct) value or ambiguous melting curve results were analysed by electrophoresis on a 2% agarose gel, to confirm presence of product of the correct size. RNA extracted from previously obtained clinical isolates, namely dengue-1 (S144), dengue-2 (ST), dengue-3 (SGH) and dengue-4 (S006), propagated in C6/36 cell cultures were included as external control in every RT-PCR run. Decision tree analyses for disease modelling Classifier modelling The C4.5 decision tree classifier [14] software Inforsense (InforSense Ltd., London, UK) was used. A standard pruning confidence of 25% was used to remove branches where the algorithm was 25% or more confident so as to avoid having specific branches that would not be representative for generalisation. This prevents over-fitting of the data. The parameter ‘minimal cases’ represents a stopping criterion for further partition of the data at specific decision nodes. Tree growing at a specific decision node was stopped when at least one class had equal or less cases than the ‘minimal cases’. This prevents the tree from sub-dividing into overly specific nodes which have little supporting data. Choosing an appropriate value for ‘missing cases’ was done using k-fold cross validation (see below). Briefly, various decision trees with different ‘minimal cases’ were calculated and the value resulting in the tree with the best performance was chosen. The calculated algorithms were validated using the k-fold cross-validation approach. This is considered to be a powerful methodology to overcome data over-fitting [15]. Briefly, the original sample was divided into k sub-samples. Each sub-sample was put aside as evaluation data for testing a model, and the remaining k-1 sub-samples were used for training the model. The cross-validation process was repeated k times (folds) and each of the k sub-samples was used once as the validation data. The k results obtained from the k-folds could then be averaged to produce a single estimation of model performance [15]. The fold value was set to k = 10. To analyse the sensitivity and specificity of the decision algorithm, an averaged receiver-operating characteristic (ROC) curve was constructed. The area under the curve (AUC) serves as an indicator of the overall performance of the algorithm. The algorithms with the highest sensitivity along with a high AUC were selected. Statistical analysis All results have been summarized in terms of means and standard deviation for continuous variables using independent sample T-test. Shapiro-Wilk normality test was used to check for non-normally distributed parameters whereby a p value 1000/transfusion. * p 1000), both without documented pleural effusion, ascites or rise in serial hematocrit, or received platelet/blood transfusion. These clinical parameters have been previously observed in severe dengue [15],[16] and we have taken these cases collectively as clinically severe outcomes. Of these 23 cases, 19 (82.6%) were predicted by our tree as either probable severe dengue or likely severe dengue with data obtained in the first three days of illness. Conversely, 91.8% and 100% of the patients in the groups predicted by our tree as either likely non-severe dengue or probable non-severe dengue, respectively, did not show severe clinical outcomes (Table 1). The use of such a prognostic algorithm could prove useful in segregating patients according to likely clinical outcomes to guide clinical management and follow-up visits. Although our EDEN cohort in Singapore has focused on dengue in the adult population, our findings demonstrate a proof-of-concept that the use of simple haematological and virological parameters is predictive of disease outcome, and can be built upon to develop prognosis-based protocols for dengue case management that begins at the primary healthcare setting. Our study represents the first to demonstrate that decision algorithms for dengue diagnosis and prognosis can be developed for clinical use. While a large multi-centre prospective study will be needed for these algorithms to be applied globally, our analysis indicates that a decision tree approach can differentiate dengue from non-dengue febrile illness and predict outcome of disease. Supporting Information Table S1 Criteria for the classification of DF/DHF and the recommended approach to diagnosis, according to the WHO Guidelines. (0.03 MB DOC) Click here for additional data file. Table S2 Parameters and the respective units of measure used in the EDEN study to monitor the recruited cases in all three visits. (0.06 MB DOC) Click here for additional data file.
