Triple positivity for nonstructural antigen 1, immunoglobulin M and immunoglobulin G is predictive of severe thrombocytopaenia related to dengue infection

Risk and predictive factors for severe dengue infection: A systematic review and meta-analysis

Background

Dengue is a major public health issue worldwide and severe dengue (SD) is life threatening. It is critical to triage patients with dengue infection in the early stage. However, there is limited knowledge on early indicators of SD. The objective of this study is to identify risk factors for the prognosis of SD and try to find out some potential predictive factors for SD from dengue fever (DF) in the early of infection.

Methods

The PubMed, Cochrane Library and Web of Science databases were searched for relevant studies from June 1999 to December 2020. The pooled odds ratio (OR) or standardized mean difference (SMD) with 95% confidence intervals (CI) of identified factors was calculated using a fixed or random effect model in the meta-analysis. Tests for heterogeneity, publication bias, subgroup analyses, meta-regression, and a sensitivity analysis were further performed.

Findings

A total of 6,848 candidate articles were retrieved, 87 studies with 35,184 DF and 8,173 SD cases met the eligibility criteria. A total of 64 factors were identified, including population and virus characteristics, clinical symptoms and signs, laboratory biomarkers, cytokines, and chemokines; of these factors, 34 were found to be significantly different between DF and SD, while the other 30 factors were not significantly different between the two groups after pooling the data from the relevant studies. Additionally, 9 factors were positive associated with SD within 7 days after illness when the timing subgroup analysis were performed.

Conclusions

Practical factors and biomarkers for the identification of SD were established, which will be helpful for a prompt diagnosis and early effective treatment for those at greatest risk. These outcomes also enhance our knowledge of the clinical manifestations and pathogenesis of SD.

Assessment of the Risk of Severe Dengue Using Intrahost Viral Population in Dengue Virus Serotype 2 Patients via Machine Learning

Dengue virus, a positive-sense single-stranded RNA virus, continuously threatens human health. Although several criteria for evaluation of severe dengue have been recently established, the ability to prognose the risk of severe outcomes for dengue patients remains limited. Mutant spectra of RNA viruses, including single nucleotide variants (SNVs) and defective virus genomes (DVGs), contribute to viral virulence and growth. Here, we determine the potency of intrahost viral population in dengue patients with primary infection that progresses into severe dengue. A total of 65 dengue virus serotype 2 infected patients in primary infection including 17 severe cases were enrolled. We utilized deep sequencing to directly define the frequency of SNVs and detection times of DVGs in sera of dengue patients and analyzed their associations with severe dengue. Among the detected SNVs and DVGs, the frequencies of 9 SNVs and the detection time of 1 DVG exhibited statistically significant differences between patients with dengue fever and those with severe dengue. By utilizing the detected frequencies/times of the selected SNVs/DVG as features, the machine learning model showed high average with a value of area under the receiver operating characteristic curve (AUROC, 0.966 ± 0.064). The elevation of the frequency of SNVs at E (nucleotide position 995 and 2216), NS2A (nucleotide position 4105), NS3 (nucleotide position 4536, 4606), and NS5 protein (nucleotide position 7643 and 10067) and the detection times of the selected DVG that had a deletion junction in the E protein region (nucleotide positions of the junction: between 969 and 1022) increased the possibility of dengue patients for severe dengue. In summary, we demonstrated the detected frequencies/times of SNVs/DVG in dengue patients associated with severe disease and successfully utilized them to discriminate severe patients using machine learning algorithm. The identified SNVs and DVGs that are associated with severe dengue will expand our understanding of intrahost viral population in dengue pathogenesis.

Serological Diagnosis of Dengue

A reliable and specific diagnosis is imperative in viral diagnosis, both for clinical management and surveillance, and to ensure that early treatment and control measures are carried out. The number of days of illness is important to choose the most appropriate method to be used and for the correct interpretation of the results obtained. Specific IgM is elicited after that period, indicating an active infection and usually lasts up to 3 months. However, in DENV secondary infections, IgM levels may be significantly lower or undetectable. After 10-12 days, a lifetime specific IgG is produced. Routinely, the laboratory diagnosis of DENV infections can be performed by viral isolation and/or detection of viral nucleic acid, serological assays for the detection of specific antibodies (IgM/IgG), antigen (NS1) and the detection of viral antigens in tissues, which are suitable during certain phases of the disease. For serological diagnosis, serum, plasma, or cerebrospinal fluid (CSF) samples may be investigated. If the test is carried out a few days after collection, the specimens can be stored at 4 °C, since the immunoglobulins are stable in serum or plasma. If the storage period is extended, the material must be kept at -20 °C or -70 °C. In serology, several methods can be used to detect specific viral antigens and/or antibodies, produced by the host in response to DENV infection. Routinely, serological tests include the hemagglutination inhibition (HI) assay, the plaque reduction neutralizing test (PRNT), the gold standard assay for dengue immune response characterization, and ELISAs to detect IgM (MAC-ELISA) and IgG (IgG-ELISA).

The immature platelet fraction, a predictive tool for early recovery from dengue-related thrombocytopenia: a prospective study

BACKGROUND

There is a paucity of predictive factors for early recovery from thrombocytopenia related to dengue. The immature platelet fraction (IPF%) is reflective of megakaryopoiesis and may correlate with recovery from dengue-related thrombocytopenia. Our objective was to assess the predictive value of IPF% on days 2 and 3 of illness for recovery from dengue-related thrombocytopenia.

METHODS

A prospective study was conducted among patients with dengue admitted to our institution (Nawaloka Hospital PLC) from December 2019 to October 2020. Dengue was diagnosed based on positive non-structural antigen 1 or IgM. IPF% data were extracted from the Sysmex-XN-1000 automated hematology analyzer. Clinical data were obtained from electronic medical records. Statistical analyses were performed using SPSS version 20.

RESULTS

We included 240 patients. An IPF% on day 2 of illness of >7.15% had a sensitivity of 80.0% and specificity of 70.4% for prediction of platelet recovery (defined as platelet count ≥60×109/L) on day 7 of illness. An IPF% of >7.25% on day 3 of illness had a sensitivity of 88.9% and specificity of 47.1% for predicting platelet recovery >60×109/L on day 8 of illness. The IPF% was significantly lower in patients with severe dengue. Platelet recovery was observed within 48 h after the peak IPF% was reached, regardless of severity.

CONCLUSION

We propose that IPF% values on days 2 and 3 of illness are a promising predictive tool for early recovery from dengue-related thrombocytopenia.