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.

埃博拉病毒病临床体征的低特异性增加了疫情暴发期间医院内传播给患者和医务工作者的风险。降低这种风险需要识别埃博拉病毒感染可能性高的患者。对疑似埃博拉病毒感染患者的回顾性数据分析确定了 13 个强预测因子和发病时间是埃博拉病毒病预测评分的组成部分。我们还注意到 4 个高度预测的变量，这些变量可以区分感染高风险患者，而与他们的评分无关。该算法在回顾性数据上的外部验证表明，感染的可能性随着评分的增加而持续增加。