Derivation and Internal Validation of the Ebola Prediction Score for Risk Stratification of Patients With Suspected Ebola Virus Disease
Derivation and Internal Validation of the Ebola Prediction Score for Risk Stratification of Patients With Suspected Ebola Virus Disease
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DOI：
10.1016/j.annemergmed.2015.03.011
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发表时间：
2015-09-01
影响因子：
6.2
通讯作者：
Kesselly, J. Kota T.
中科院分区：
文献类型：
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作者：
Levine, Adam C.;Shetty, Pranav Prathap;Kesselly, J. Kota T.
研究目的：目前在西非爆发的埃博拉病毒病是有记录以来最大的一次，使当地卫生系统和国际社会无法为所有疑似病例提供足够的隔离和治疗。本研究的目的是开发一种临床预测模型，帮助临床医生对疑似埃博拉病毒疾病患者进行风险分层。方法：对利比里亚邦县埃博拉治疗单元在运营前16周的常规临床护理期间收集的患者数据进行回顾分析。14个临床和流行病学变量的预测能力与实验室确认的埃博拉病毒疾病的主要结果进行了比较，使用Logistic回归建立了最终的预测模型。用Bootstrap抽样评估模型的内部有效性，并在模拟验证队列中评估其性能。结果：在研究期间，395名埃博拉治疗单位的患者中有382名(97%)获得了埃博拉病毒疾病检测结果。共有160名患者(42%)的埃博拉病毒检测呈阳性。Logistic回归分析确定了6个独立预测实验室确认的埃博拉病毒疾病的变量，包括患病接触、腹泻、食欲不振、肌肉疼痛、吞咽困难和无腹痛。用这6个变量构建的埃博拉预测分数，在接收者操作员特征曲线下的面积为0.75(95%可信区间为0.70至0.80)，用于预测实验室确认的埃博拉病毒疾病。埃博拉预测评分越高的患者患实验室确认的埃博拉病毒疾病的可能性越高。结论：临床医生可以使用埃博拉预测评分作为当前埃博拉病毒疾病病例定义的补充，对疑似埃博拉病毒疾病的患者进行风险分层。临床医生可以将这一新工具用于在埃博拉治疗单位或社区隔离中心的疑似疾病病房内对患者进行分组，以防止医院感染，或者在患者数量超出可用容量时作为分诊工具。然而，考虑到临床预测模型的内在局限性，能够快速、明确地排除患者中埃博拉病毒疾病的低成本、护理点检测应该是研究的优先事项。
Study objective: The current outbreak of Ebola virus disease in West Africa is the largest on record and has overwhelmed the capacity of local health systems and the international community to provide sufficient isolation and treatment of all suspected cases. The goal of this study is to develop a clinical prediction model that can help clinicians risk-stratify patients with suspected Ebola virus disease in the context of such an epidemic.Methods: A retrospective analysis was performed of patient data collected during routine clinical care at the Bong County Ebola Treatment Unit in Liberia during its first 16 weeks of operation. The predictive power of 14 clinical and epidemiologic variables was measured against the primary outcome of laboratory-confirmed Ebola virus disease, using logistic regression to develop a final prediction model. Bootstrap sampling was used to assess the internal validity of the model and estimate its performance in a simulated validation cohort.Results: Ebola virus disease testing results were available for 382 (97%) of 395 patients admitted to the Ebola treatment unit during the study period. A total of 160 patients (42%) tested positive for Ebola virus disease. Logistic regression analysis identified 6 variables independently predictive of laboratory-confirmed Ebola virus disease, including sick contact, diarrhea, loss of appetite, muscle pains, difficulty swallowing, and absence of abdominal pain. The Ebola Prediction Score, constructed with these 6 variables, had an area under the receiver operator characteristic curve of 0.75 (95% confidence interval 0.70 to 0.80) for the prediction of laboratory-confirmed Ebola virus disease. Patients with higher Ebola Prediction Scores had higher likelihoods of laboratory-confirmed Ebola virus disease.Conclusion: The Ebola Prediction Score can be used by clinicians as an adjunct to current Ebola virus disease case definitions to risk-stratify patients with suspected Ebola virus disease. Clinicians can use this new tool for the purpose of cohorting patients within the suspected-disease ward of an Ebola treatment unit or community-based isolation center to prevent nosocomial infection or as a triage tool when patient numbers overwhelm available capacity. Given the inherent limitations of clinical prediction models, however, a low-cost, point-of-care test that can rapidly and definitively exclude Ebola virus disease in patients should be a research priority.