Predicting Ebola infection: A malaria-sensitive triage score for Ebola virus disease

Background: the non-specific symptoms of ebola virus disease (evd) pose a major problem to triage and isolation efforts at ebola treatment centres (etcs). under the current triage protocol,half the patients allocated to high-risk “probable” wards were evd(-): a misclassification speculated to predispose nosocomial evd infection. a better understanding of the statistical relevance of individual triage symptoms is essential in resource-poor settings where rapid,laboratory-confirmed diagnostics are often unavailable. methods/principal findings: this retrospective cohort study analyses the clinical characteristics of 566 patients admitted to the goal-mathaska etc in sierra leone. the diagnostic potential of each characteristic was assessed by multivariate analysis and incorporated into a statistically weighted predictive score,designed to detect evd as well as discriminate malaria. of the 566 patients,28% were evd(+) and 35% were malaria(+). malaria was 2-fold more common in evd(-) patients (p<0.05),and thus an important differential diagnosis. univariate analyses comparing evd(+) vs. evd(-) and evd(+)/malaria(-) vs. evd(-)/malaria(+) cohorts revealed 7 characteristics with the highest odds for evd infection,namely: reported sick-contact,conjunctivitis,diarrhoea,referral-time of 4–9 days,pyrexia,dysphagia and haemorrhage. oppositely,myalgia was more predictive of evd(-) or evd(-)/malaria(+). including these 8 characteristics in a triage score,we obtained an 89% ability to discriminate evd(+) from either evd(-) or evd(-)/malaria(+). conclusions/significance: this study proposes a highly predictive and easy-to-use triage tool,which stratifies the risk of evd infection with 89% discriminative power for both evd(-) and evd(-)/malaria(+) differential diagnoses. improved triage could preserve resources by identifying those in need of more specific differential diagnostics as well as bolster infection prevention/control measures by better compartmentalizing the risk of nosocomial infection. © 2017 hartley et al.