Predicting Ebola infection: A malaria-sensitive triage score for Ebola virus disease.
Predicting Ebola infection: A malaria-sensitive triage score for Ebola virus disease.
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DOI：
10.1371/journal.pntd.0005356
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发表时间：
2017-02
影响因子：
3.8
通讯作者：
Faouzi M
中科院分区：
文献类型：
--
作者：
Hartley MA;Young A;Tran AM;Okoni-Williams HH;Suma M;Mancuso B;Al-Dikhari A;Faouzi M
埃博拉病毒病（EVD）的非特异性症状对埃博拉治疗中心（ETC）的分诊和隔离工作构成了重大问题。根据目前的分诊方案，分配到高风险"可能"病房的患者中有一半是EVD（-）：推测这种错误分类易导致院内EVD感染。更好地了解个体分类症状的统计相关性在资源匮乏的环境中至关重要，因为在这些环境中，通常无法获得快速的实验室确认的诊断。这项回顾性队列研究分析了塞拉利昂GOAL-Mathaska ETC收治的566例患者的临床特征。通过多变量分析评估每个特征的诊断潜力，并将其纳入统计加权预测评分，旨在检测EVD以及区分疟疾。在566名患者中，28%为EVD（+），35%为疟疾（+）。疟疾在EVD（-）患者中的发病率是正常人的2倍（p <0.05），因此是一个重要的鉴别诊断。比较EVD（+）与EVD（-）和EVD（+）/疟疾（-）与EVD（-）/疟疾（+）队列的单变量分析显示，EVD感染几率最高的7个特征，即：报告的疾病接触、结膜炎、腹泻、4 - 9天的分娩时间、发热、吞咽困难和出血。结论：肌痛更能预测EVD（-）或EVD（-）/疟疾（+）。将这8个特征包括在分类评分中，我们获得了89%的区分EVD（+）与EVD（-）或EVD（-）/疟疾（+）的能力。这项研究提出了一种高度预测性和易于使用的分类工具，该工具对EVD感染的风险进行了分层，对于EVD（-）和EVD（-）/疟疾（+）鉴别诊断具有89%的区分能力。改进的分诊可以通过识别需要更具体的鉴别诊断来保护资源，并通过更好地划分医院感染风险来加强感染预防/控制措施。在发现埃博拉病毒病（EVD）40年后，感染的来源、宿主和动态在很大程度上仍然未知，因此再次出现的威胁仍然存在。由于EVD在发展中国家脆弱的医疗系统中蓬勃发展，因此分流工具必须成本低且易于使用，以便最好地分配有限的资源并确保EVD监测的可持续性。从公共卫生的角度来看，在筛查埃博拉等高度传染性和致命性疾病时，敏感性至关重要。然而，一旦这些可疑患者到达治疗中心，特异性就变得更加重要，以便准确地将他们分配到风险适当的病房，并更好地分配有限的资源。目前，识别"疑似"埃博拉患者的预测试分类包括对更为流行的疾病疟疾所共有的非特异性症状进行二元评估。使用这些指南，超过70%的被选择进入ETC潜在传染性环境的患者没有感染埃博拉病毒。在ETC中，患者可能会根据被称为"埃博拉外观"的临床主观评估被进一步分类到风险更高的"可能"病房：因为事实证明，这种评估的准确性与抛硬币相当。虽然通过分层划分风险是感染预防和控制措施的重要组成部分，但患者分流应足够准确，以证明其益处。这项研究构建了一个易于使用和高度准确（90%）的分类评分系统，以疟疾敏感的方式区分EVD感染风险：这不仅显着提高了EVD的预测准确性，而且还可以识别（更致命的）疟疾感染。
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. 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(+). 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. Four decades after the discovery of Ebola virus disease (EVD), the sources, reservoirs and dynamics of infection are still largely unknown and thus the threat of re-emergence remains ever present. As EVD thrives on fragile healthcare systems in the developing world, it is essential that triage tools are low-cost and easy-to-use in order to best allocate limited resources and ensure sustainability of EVD surveillance. From a public health perspective, sensitivity is paramount when screening for highly contagious and fatal diseases such as Ebola. However, once these suspect patients arrive at the treatment centres, specificity becomes far more important in order to accurately allocate them to risk-appropriate wards and better distribute limited resources. Currently, pre-test triage to identify “suspect” Ebola patients consists of a binary evaluation of non-specific symptoms that are shared by the much more prevalent disease: Malaria. Using these guidelines, over 70% of patients selected for admission to the potentially contagious environment of an ETC did not have Ebola. Within the ETC, patients may be further triaged into a higher risk “probable” ward on the basis of a clinically subjective assessment known as the “Ebola look”: since proven to have comparable accuracy to flipping a coin. While compartmentalising risk by stratification is an essential component to infection prevention and control measures, patient triage should be sufficiently accurate to justify to its benefit. This study constructs an easy-to-use and highly accurate (90%) triage scoring system that discriminates EVD infection risk in a malaria-sensitive manner: a strategy, which not only significantly improves the predictive accuracy for EVD but may also identify the (more deadly) infection of malaria.