Denis-Luc Ardiet, Justus Nsio, Gaston Komanda, Rebecca M. Coulborn, Emmanuel Grellety, Francesco Grandesso, Richard Kitenge, Dolla L. Ngwanga, Bibiche Matady, Guyguy Manangama, Mathias Mossoko, John K. Ngwama, Placide Mbala, Francisco Luquero, Klaudia Porten, Steve Ahuka-Mundeke
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引用次数: 0
Abstract
The low specificity of Ebola virus disease clinical signs increases the risk for nosocomial transmission to patients and healthcare workers during outbreaks. Reducing this risk requires identifying patients with a high likelihood of Ebola virus infection. Analyses of retrospective data from patients suspected of having Ebola virus infection identified 13 strong predictors and time from disease onset as constituents of a prediction score for Ebola virus disease. We also noted 4 highly predictive variables that could distinguish patients at high risk for infection, independent of their scores. External validation of this algorithm on retrospective data revealed the probability of infection continuously increased with the score.
期刊介绍：
Emerging Infectious Diseases is a monthly open access journal published by the Centers for Disease Control and Prevention. The primary goal of this peer-reviewed journal is to advance the global recognition of both new and reemerging infectious diseases, while also enhancing our understanding of the underlying factors that contribute to disease emergence, prevention, and elimination.
Targeted towards professionals in the field of infectious diseases and related sciences, the journal encourages diverse contributions from experts in academic research, industry, clinical practice, public health, as well as specialists in economics, social sciences, and other relevant disciplines. By fostering a collaborative approach, Emerging Infectious Diseases aims to facilitate interdisciplinary dialogue and address the multifaceted challenges posed by infectious diseases.
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