Development of Ebola virus disease prediction scores: Screening tools for Ebola suspects at the triage-point during an outbreak.
Development of Ebola virus disease prediction scores: Screening tools for Ebola suspects at the triage-point during an outbreak.
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DOI：
10.1371/journal.pone.0278678
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Mulangu, Sabue
中科院分区：
文献类型：
--
作者：
Tshomba, Antoine Oloma;Mukadi-Bamuleka, Daniel-Ricky;De Weggheleire, Anja;Tshiani, Olivier M.;Kitenge, Richard O.;Kayembe, Charles T.;Jacobs, Bart K. M.;Lynen, Lutgarde;Mbala-Kingebeni, Placide;Muyembe-Tamfum, Jean-Jacques;Ahuka-Mundeke, Steve;Mumba, Dieudonne N.;Tshala-Katumbay, Desire D.;Mulangu, Sabue
埃博拉病毒病（EVD）疫情的控制依赖于快速诊断和及时行动，这在资源有限的情况下是一项艰巨的任务。这项研究开发的预测分数可以帮助医护人员在埃博拉病毒病爆发期间改进埃博拉病毒病疑似病例的分类点决策。在刚果民主共和国东部埃博拉病毒病爆发期间，我们计算了埃博拉病毒病预测因子的准确度测量值，以与参考标准 GeneXpert® 结果进行比较来评估其诊断能力。我们使用 Spiegelhalter-Knill-Jones 方法开发了预测评分，并构建了临床预测评分 (CPS) 和扩展临床预测评分 (ECPS)。我们绘制了受试者工作特征曲线 (ROC)，估计 ROC 下面积 (AUROC) 以评估评分的表现，并计算净效益 (NB) 以评估评分在给定截止点的临床效用（决策能力）。我们进行了决策曲线分析（DCA），以在一定阈值概率范围内比较预测分数的决策能力，并量化不必要的隔离数量。该分析对 10432 名受试者的数据进行，其中包括 651 例埃博拉病毒病病例。整个数据集中的埃博拉病毒病患病率为 6.2%，符合世界卫生组织埃博拉病例定义的嫌疑人亚组的患病率为 14.8%，不符合该病例定义的嫌疑人的患病率为 3.2%。 WHO 临床定义的敏感性为 61.6%，特异性为 76.4%。疲劳、吞咽困难、眼睛发红、牙龈出血、吐血、精神错乱、咯血以及与埃博拉病毒病病例接触史是埃博拉病毒病的预测因素。 ECPS 的 AUROC 为 0.88 (95%CI: 0.86–0.89)，显着高于 CPS 的 0.71 (95%CI: 0.69–0.73) (p < 0.0001)。在-1分时，CPS的敏感性为85.4%，特异性为42.3%，ECPS的敏感性为78.8%，特异性为81.4%。评分的诊断性能在三种疾病背景下（整体、符合或不符合世界卫生组织病例定义数据集）有所不同。在 10% 的阈值概率下，例如在疾病不利的情况下，ECPS 的 NB 为 0.033，不必要的隔离净减少 67.1%。使用 ECPS 作为隔离埃博拉病毒病嫌疑人的联合方法可将不必要的隔离次数减少 65.7%。我们研究中开发的评分显示出作为埃博拉病毒病病例预测指标的良好性能，因为它们的使用提高了净效益，即临床效用。这些快速且低成本的工具可以帮助决策者在疫情暴发期间在分诊点隔离埃博拉病毒病可疑病例。然而，这些工具在大规模使用之前仍需要外部验证和成本效益评估。
The control of Ebola virus disease (EVD) outbreaks relies on rapid diagnosis and prompt action, a daunting task in limited-resource contexts. This study develops prediction scores that can help healthcare workers improve their decision-making at the triage-point of EVD suspect-cases during EVD outbreaks. We computed accuracy measurements of EVD predictors to assess their diagnosing ability compared with the reference standard GeneXpert® results, during the eastern DRC EVD outbreak. We developed predictive scores using the Spiegelhalter-Knill-Jones approach and constructed a clinical prediction score (CPS) and an extended clinical prediction score (ECPS). We plotted the receiver operating characteristic curves (ROCs), estimated the area under the ROC (AUROC) to assess the performance of scores, and computed net benefits (NB) to assess the clinical utility (decision-making ability) of the scores at a given cut-off. We performed decision curve analysis (DCA) to compare, at a range of threshold probabilities, prediction scores’ decision-making ability and to quantify the number of unnecessary isolation. The analysis was done on data from 10432 subjects, including 651 EVD cases. The EVD prevalence was 6.2% in the whole dataset, 14.8% in the subgroup of suspects who fitted the WHO Ebola case definition, and 3.2% for the set of suspects who did not fit this case definition. The WHO clinical definition yielded 61.6% sensitivity and 76.4% specificity. Fatigue, difficulty in swallowing, red eyes, gingival bleeding, hematemesis, confusion, hemoptysis, and a history of contact with an EVD case were predictors of EVD. The AUROC for ECPS was 0.88 (95%CI: 0.86–0.89), significantly greater than this for CPS, 0.71 (95%CI: 0.69–0.73) (p < 0.0001). At -1 point of score, the CPS yielded a sensitivity of 85.4% and specificity of 42.3%, and the ECPS yielded sensitivity of 78.8% and specificity of 81.4%. The diagnostic performance of the scores varied in the three disease contexts (the whole, fitting or not fitting the WHO case definition data sets). At 10% of threshold probability, e.g. in disease-adverse context, ECPS gave an NB of 0.033 and a net reduction of unnecessary isolation of 67.1%. Using ECPS as a joint approach to isolate EVD suspects reduces the number of unnecessary isolations by 65.7%. The scores developed in our study showed a good performance as EVD case predictors since their use improved the net benefit, i.e., their clinical utility. These rapid and low-cost tools can help in decision-making to isolate EVD-suspicious cases at the triage point during an outbreak. However, these tools still require external validation and cost-effectiveness evaluation before being used on a large scale.
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