METHODS: Over six months in 2018, we recruited 368 adults who met the WHO 2009 criteria for probable dengue infection. They underwent the following blood tests: full blood count, dengue virus (DENV) rapid diagnostic test (RDT), ELISA (dengue IgM and IgG), nested RT-PCR for dengue, multiplex qRT-PCR for Zika, Chikungunya and dengue as well as PCR tests for Leptopspira spp., Japanese encephalitis and West Nile virus.
RESULTS: Laboratory-confirmed dengue infections (defined by positive tests in NS1, IgM, high-titre IgG or nested RT-PCR) were found in 167 (45.4%) patients. Of these 167 dengue patients, only 104 (62.3%) were positive on rapid diagnostic testing. Dengue infection was significantly associated with the following features: family or neighbours with dengue in the past week (AOR: 3.59, 95% CI:2.14-6.00, p<0.001), cutaneous rash (AOR: 3.58, 95% CI:1.77-7.23, p<0.001), increased temperature (AOR: 1.33, 95% CI:1.04-1.70, p = 0.021), leucopenia (white cell count < 4,000/μL) (AOR: 3.44, 95% CI:1.72-6.89, p<0.001) and thrombocytopenia (platelet count <150,000/μL)(AOR: 4.63, 95% CI:2.33-9.21, p<0.001). Dengue infection was negatively associated with runny nose (AOR: 0.47, 95% CI:0.29-0.78, p = 0.003) and arthralgia (AOR: 0.42, 95% CI:0.24-0.75, p = 0.004). Serotyping by nested RT-PCR revealed mostly mono-infections with DENV-2 (n = 64), DENV-1 (n = 32) and DENV-3 (n = 17); 14 co-infections occurred with DENV-1/DENV-2 (n = 13) and DENV-1/DENV-4 (n = 1). Besides dengue, none of the pathogens above were found in patients' serum.
CONCLUSIONS: Acute undifferentiated febrile infections are a diagnostic challenge for community-based clinicians. Rapid diagnostic tests are increasingly used to diagnose dengue infection but negative tests should be interpreted with caution as they fail to detect a considerable proportion of dengue infection. Certain clinical features and haematological parameters are important in the clinical diagnosis of dengue infection.
METHOD: This is a retrospective cohort study of confirmed severe dengue patients that were admitted in 2014 to Hospital Kuala Lumpur. Data on baseline characteristics, clinical parameters, and laboratory findings at diagnosis of severe dengue were collected. The outcome of interest is death among patients diagnosed with severe dengue.
RESULTS: There were 199 patients with severe dengue included in the study. Multivariate analysis found lethargy, OR 3.84 (95% CI 1.23-12.03); bleeding, OR 8.88 (95% CI 2.91-27.15); pulse rate, OR 1.04 (95% CI 1.01-1.07); serum bicarbonate, OR 0.79 (95% CI 0.70-0.89) and serum lactate OR 1.27 (95% CI 1.09-1.47), to be statistically significant predictors of death. The regression equation to our model with the highest AUROC, 83.5 (95% CI 72.4-94.6), is: Log odds of death amongst severe dengue cases = - 1.021 - 0.220(Serum bicarbonate) + 0.001(ALT) + 0.067(Age) - 0.190(Gender).
CONCLUSION: This study showed that a large proportion of severe dengue occurred early, whilst patients were still febrile. The best prediction model to predict death at recognition of severe dengue is a model that incorporates serum bicarbonate and ALT levels.
CONCLUDING REMARKS: DengueTools was able to make significant advances in methods for understanding and controlling dengue transmission in a range of settings. These will have implications for public health agendas to counteract dengue, including vaccination programmes.
OUTLOOK: Towards the end of the DengueTools project, Zika virus emerged as an unexpected epidemic in the central and southern America. Given the similarities between the dengue and Zika viruses, with vectors in common, some of the DengueTools thinking translated readily into the Zika situation.