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Development and Evaluation of a Rapid Decision Algorithm for the Triage of Patients During Ebola Outbreaks
37 Pages Posted: 28 Nov 2022
More...Abstract
Background: During Ebola outbreaks, given the low specificity of clinical signs of disease, public health measures often use a broad suspect case definition, and test many symptomatic patients to achieve control. While theoretically allowing for good sensitivity, this approach also captures many Ebola-negative patients and has limitations regarding time to diagnosis, cost, nosocomial transmission risks, response efficiency, and community acceptance.
Methods: Based on previous findings describing Ebola symptomatology during early (Days 0-2 after disease onset) and later (Day 3 or later) phases, we aimed to develop a straightforward tool to help healthcare workers classify suspected cases according to likely risk (low/intermediate/high) of EVD. An EVD prediction score was developed using four priority variables most associated with infection (prioritization rule), and the time from disease onset. It was externally validated on retrospective data (14,346 patients) and diagnostic performance was analyzed to define two thresholds distinguishing these risk categories.
Findings: The probability of infection increased with the Ebola prediction score , and sensitivities of the lower and upper thresholds were 88·1% (84·0 to 91·4%) and 13·8% (10·2 to 18·1%), respectively. The prioritization rule further improved the above sensitivities to 91·2% (88·1 to 94·3%) and 56·7% (51·3 to 62·2%), respectively. When tested prospectively during two Ebola outbreaks (2,652 patients), the tool classified 38·9% of patients as low risk, with no false negative cases (interim results). Easy-to-use and understand, supervision was nevertheless needed to ensure proper anamnesis and EVD exposure assessment with the tool.
Interpretation: These findings suggest that this new tool allows healthcare workers to reliably estimate the probability of infection. The efficiency of Ebola outbreak responses could be improved through isolation measures adapted to Ebola risk.
Funding Information: This study was funded entirely through programmatic funds from Médecins Sans Frontières and its research affiliate Epicentre.
Declaration of Interests: We declare that we have no conflicts of interest.
Ethics Approval Statement: The study protocol was approved by the authorities of the Ebola response in DRC, and approved by the National Ethics Committee of the School of Public Health, Kinshasa, DRC (ESP/CE/40/2020) and \ the Médecins Sans Frontières Ethical Review Board (ID: 2035).
Keywords: Ebola, symptomatology, clinical algorithm, predictors, triage, epidemics, DRC
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