Meta-analysis of predictive symptoms for Ebola virus disease.
Introduction One of the leading challenges in the 2013-2016 West African Ebola virus disease (EVD) outbreak was how best to quickly identify patients with EVD, separating them from those without the disease, in order to maximise limited isolation bed capacity and keep health systems functioning. Met...
| Published in: | PLOS Neglected Tropical Diseases |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020
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| Subjects: | |
| Online Access: | https://doi.org/10.1371/journal.pntd.0008799 https://doaj.org/article/4e78995457884ebbaa15a4a25881b8df |
| Summary: | Introduction One of the leading challenges in the 2013-2016 West African Ebola virus disease (EVD) outbreak was how best to quickly identify patients with EVD, separating them from those without the disease, in order to maximise limited isolation bed capacity and keep health systems functioning. Methodology We performed a systematic literature review to identify all published data on EVD clinical symptoms in adult patients. Data was dual extracted, and random effects meta-analysis performed for each symptom to identify symptoms with the greatest risk for EVD infection. Results Symptoms usually presenting late in illness that were more than twice as likely to predict a diagnosis of Ebola, were confusion (pOR 3.04, 95% CI 2.18-4.23), conjunctivitis (2.90, 1.92-4.38), dysphagia (1.95, 1.13-3.35) and jaundice (1.86, 1.20-2.88). Early non-specific symptoms of diarrhoea (2.99, 2.00-4.48), fatigue (2.77, 1.59-4.81), vomiting (2.69, 1.76-4.10), fever (1.97, 1.10-4.52), muscle pain (1.65, 1.04-2.61), and cough (1.63, 1.24-2.14), were also strongly associated with EVD diagnosis. Conclusions The existing literature fails to provide a unified position on the symptoms most predictive of EVD, but highlights some early and late stage symptoms that in combination will be useful for future risk stratification. Confirmation of these findings across datasets (or ideally an aggregation of all individual patient data) will aid effective future clinical assessment, risk stratification tools and emergency epidemic response planning. |
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