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Prediction of mortality in severe dengue cases

Overview of attention for article published in BMC Infectious Diseases, May 2018
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Title
Prediction of mortality in severe dengue cases
Published in
BMC Infectious Diseases, May 2018
DOI 10.1186/s12879-018-3141-6
Pubmed ID
Authors

Saiful Safuan Md-Sani, Julina Md-Noor, Winn-Hui Han, Syang-Pyang Gan, Nor-Salina Rani, Hui-Loo Tan, Kanimoli Rathakrishnan, Mohd Azizuddin A-Shariffuddin, Marzilawati Abd-Rahman

Abstract

Increasing incidence of dengue cases in Malaysia over the last few years has been paralleled by increased deaths. Mortality prediction models will therefore be useful in clinical management. The aim of this study is to identify factors at diagnosis of severe dengue that predicts mortality and assess predictive models based on these identified factors. 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. 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). 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 17%
Researcher 9 13%
Student > Bachelor 9 13%
Student > Postgraduate 7 10%
Student > Doctoral Student 4 6%
Other 11 16%
Unknown 18 26%
Readers by discipline Count As %
Medicine and Dentistry 32 46%
Biochemistry, Genetics and Molecular Biology 4 6%
Engineering 2 3%
Computer Science 2 3%
Materials Science 2 3%
Other 6 9%
Unknown 22 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 May 2018.
All research outputs
#10,374,169
of 13,004,658 outputs
Outputs from BMC Infectious Diseases
#3,439
of 4,841 outputs
Outputs of similar age
#203,031
of 271,090 outputs
Outputs of similar age from BMC Infectious Diseases
#1
of 1 outputs
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