Ghazi Lama, Simonov Michael, Mansour Sherry, Moledina Dennis, Greenberg Jason, Yamamoto Yu, Biswas Aditya, Wilson F Perry
Clinical and Translational Research Accelerator, Yale University, New Haven, Connecticut.
medRxiv. 2020 Dec 2:2020.11.30.20241414. doi: 10.1101/2020.11.30.20241414.
False negative SARS-CoV-2 tests can lead to spread of infection in the inpatient setting to other patients and healthcare workers. However, the population of patients with COVID who are admitted with false negative testing is unstudied.
To characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR.
Retrospective cohort study.
Five hospitals within the Yale New Haven Health System between 3/10/2020 and 9/1/2020.
Adult patients who received diagnostic testing for SARS-CoV-2 virus within the first 96 hours of hospitalization.
We developed a logistic regression model from readily available electronic health record data to predict SARS-CoV-2 positivity in patients who were positive for COVID and those who were negative and never retested.
This model was applied to patients testing negative for SARS-CoV-2 who were retested within the first 96 hours of hospitalization. We evaluated the ability of the model to discriminate between patients who would subsequently retest negative and those who would subsequently retest positive.
We included 31,459 hospitalized adult patients; 2,666 of these patients tested positive for COVID and 3,511 initially tested negative for COVID and were retested. Of the patients who were retested, 61 (1.7%) had a subsequent positive COVID test. The model showed that higher age, vital sign abnormalities, and lower white blood cell count served as strong predictors for COVID positivity in these patients. The model had moderate performance to predict which patients would retest positive with a test set area under the receiver-operator characteristic (ROC) of 0.76 (95% CI 0.70 - 0.83). Using a cutpoint for our risk prediction model at the 90 percentile for probability, we were able to capture 35/61 (57%) of the patients who would retest positive. This cutpoint amounts to a number-needed-to-retest range between 15 and 77 patients.
We show that a pragmatic model can predict which patients should be retested for COVID. Further research is required to determine if this risk model can be applied prospectively in hospitalized patients to prevent the spread of SARS-CoV-2 infections.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）检测假阴性可能导致住院环境中的感染传播给其他患者和医护人员。然而，新冠病毒检测呈假阴性而入院的患者群体尚未得到研究。
对最初通过聚合酶链反应（PCR）检测新冠病毒呈阴性的患者中真正的SARS-CoV-2感染进行特征描述并建立预测模型。
回顾性队列研究。
2020年3月10日至2020年9月1日期间耶鲁纽黑文医疗系统内的五家医院。
在住院的前96小时内接受SARS-CoV-2病毒诊断检测的成年患者。
我们根据易于获取的电子健康记录数据建立了一个逻辑回归模型，以预测新冠病毒检测呈阳性和呈阴性且未再次检测的患者中SARS-CoV-2的阳性情况。
该模型应用于在住院的前96小时内再次接受检测的SARS-CoV-2检测呈阴性的患者。我们评估了该模型区分随后再次检测呈阴性和呈阳性患者的能力。
我们纳入了31459名住院成年患者；其中2666名患者新冠病毒检测呈阳性，3511名患者最初新冠病毒检测呈阴性并接受了再次检测。在接受再次检测的患者中，61名（1.7%）随后新冠病毒检测呈阳性。该模型显示，年龄较大、生命体征异常和白细胞计数较低是这些患者新冠病毒呈阳性的有力预测因素。该模型在预测哪些患者再次检测呈阳性方面表现中等，受试者工作特征曲线（ROC）下的检验集面积为0.76（95%可信区间0.70 - 0.83）。使用我们风险预测模型概率的第90百分位数作为切点，我们能够捕捉到61名中35名（57%）再次检测呈阳性的患者。该切点相当于需要再次检测的患者数量范围在15至77名之间。
我们表明一个实用的模型可以预测哪些患者应该接受新冠病毒的再次检测。需要进一步研究以确定该风险模型是否可以前瞻性地应用于住院患者，以预防SARS-CoV-2感染的传播。
