Dengue virus infection during pregnancy increased the risk of adverse fetal outcomes? An updated meta-analysis

Xiong, Yiquan; Mo, Yun; Shi, Ting-li; Zhu, Lin; Chen, Qing

doi:10.1016/j.jcv.2017.07.008

Cited by 26 publications

(26 citation statements)

References 36 publications

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“…The spreading of DENV has been expanding in recent years [4,6,7]. Global epidemiology of dengue shows that the time interval between regional epidemics after World War II have become shorter particularly in urban centers of Southeast Asian countries where dengue is endemic [35,36].…”

Section: Discussionmentioning

confidence: 99%

“…Dengue is the most influential arbovirus disease in the world, according to global morbidities and mortalities [4,5]. To reduce the magnitude of dengue epidemics and to decrease fatalities, early detection of dengue cases through surveillance to target high risk areas and populations has become one of the most important public health strategies in many countries [6,7]. However, the infection of dengue virus (DENV) results in a wide clinical spectrum of symptoms, ranging from subclinical infection, to mild dengue fever (DF), to severe dengue [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Comparing machine learning with case-control models to identify confirmed dengue cases

Weng

Wang

et al. 2020

PLoS Negl Trop Dis

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In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (<35 weeks) were 92.6%, 92.9%, and 93.1% in DT, DNN, and LR respectively. Adjusted odds ratios examined with LR for low WBCs [≤ 3.2 (x103/μL)], fever (≥38°C), low platelet counts [< 100 (x103/μL)], and elderly (≥ 65 years) were 5.17 [95% confidence interval (CI): 3.96–6.76], 3.17 [95%CI: 2.74–3.66], 3.10 [95%CI: 2.44–3.94], and 1.77 [95%CI: 1.50–2.10], respectively. Our prediction models can readily be used in resource-poor countries where viral/serologic tests are inconvenient and can also be applied for real-time syndromic surveillance to monitor trends of dengue cases and even be integrated with mosquito/environment surveillance for early warning and immediate prevention/control measures. In other words, a local community hospital/clinic with an instrument of complete blood counts (including platelets) can provide a sentinel screening during outbreaks. In conclusion, the machine learning approach can facilitate medical and public health efforts to minimize the health threat of dengue epidemics. However, laboratory confirmation remains the primary goal of surveillance and outbreak investigation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparing machine learning with case-control models to identify confirmed dengue cases

Weng

Wang

et al. 2020

PLoS Negl Trop Dis

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“…A 2018 study added to this by investigating correlates of severe maternal disease in French Guiana: they showed an 8.6-fold increase in the risk of postpartum hemorrhage in the presence of severe dengue (228). Whether or not dengue increases the risk of adverse fetal and perinatal outcomes (specifically miscarriage, stillbirth, preterm birth, and low birthweight) remains controversial, with two meta-analyses from 2016 and 2017 reporting conflicting results (229,230).…”

Section: Dengue Virusmentioning

confidence: 99%

Innate Immune Responses to Acute Viral Infection During Pregnancy

et al. 2020

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“…found a statistically significant relationship between dengue fever in pregnant women and the following adverse outcomes: preterm birth, abortion and fetal mortality [20,31,32]. This discrepancy could be related to the fact that our main outcome is combination of several adverse pregnancy outcomes.…”

Section: Plos Onementioning

confidence: 81%

Dengue virus infection and pregnancy outcomes during the 2017 outbreak in Ouagadougou, Burkina Faso: A retrospective cohort study

Tougma

Zoungrana²,

Yaméogo

et al. 2020

PLoS ONE

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Introduction Dengue fever is a re-emerging pathology in Burkina Faso. It affects everyone and pregnant women are not left out. The objective of this study was to estimate the burden of dengue fever and to assess its effects on pregnancy outcomes in hospitalized pregnant women during the 2017 outbreak in Ouagadougou, Burkina Faso. Method This was a retrospective cohort study including febrile pregnant women from five health facilities in Ouagadougou. The study was carried out from July 1st to December 31st, 2017. A logistic stepwise regression was performed to identify the pregnancy adverse outcomes risk factors. Results Our study included 424 pregnant women at a mean age of 27.1 years old (Standard deviation: 6.23 years). Overall 28.54% (121/424) were infected with dengue virus. During followup, 29.01% (123/424) presented an adverse pregnancy outcome. Adjusted for gestational age and clinical symptoms, the risk of adverse pregnancy outcome was twice as high among dengue infected women as compared to uninfected women with an adjusted Odds Ratio (aOR) = 2.09 (1.08-4.05). The risk of the adverse pregnancy outcome was higher in the third trimester of pregnancy with aOR = 1.66 (1.02-2.72) in dengue fever infected women.

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Dengue virus infection during pregnancy increased the risk of adverse fetal outcomes? An updated meta-analysis

Cited by 26 publications

References 36 publications

Comparing machine learning with case-control models to identify confirmed dengue cases

Comparing machine learning with case-control models to identify confirmed dengue cases

Innate Immune Responses to Acute Viral Infection During Pregnancy

Dengue virus infection and pregnancy outcomes during the 2017 outbreak in Ouagadougou, Burkina Faso: A retrospective cohort study

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