Ram-Mohan Nikhil, Rogers Angela J, Blish Catherine A, Nadeau Kari C, Zudock Elizabeth J, Kim David, Quinn James V, Sun Lixian, Liesenfeld Oliver, Yang Samuel
Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Department of Medicine-Pulmonary, Allergy & Critical Care Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
medRxiv. 2022 Mar 17:2022.03.14.22272394. doi: 10.1101/2022.03.14.22272394.
Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial co-infection, and determining illness severity since current practices require separate workflows. Here we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting SARS-CoV-2 infection, bacterial co-infections, and predicting clinical severity of COVID-19.
161 patients with PCR-confirmed COVID-19 (52.2% female, median age 50.0 years, 51% hospitalized, 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene Blood RNA) and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter.
The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrolment and the remaining oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial co-infection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e. colitis (n=1), urinary tract infection (n=1), and clinically diagnosed bacterial infections (n=3) for a specificity of 99.4%. 2/101 (2.8%) patients in the IMX-SEV-3 Low and 7/60 (11.7%) in the Moderate severity classifications died within thirty days of enrollment.
IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19, bacterial co-infections, and predicted patients’ risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management including more accurate treatment decisions and optimized resource utilization.
急诊科临床医生在同时评估疑似新型冠状病毒肺炎（COVID-19）感染患者、检测细菌合并感染以及确定疾病严重程度方面面临挑战，因为目前的做法需要单独的工作流程。在此，我们探讨IMX-BVN-3/IMX-SEV-3 29种mRNA宿主反应分类器在同时检测严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染、细菌合并感染以及预测COVID-19临床严重程度方面的准确性。
161例经聚合酶链反应（PCR）确诊的COVID-19患者（52.2%为女性，中位年龄50.0岁，51%住院，5.6%死亡）在斯坦福医院急诊科入组。提取RNA（PAXgene Blood RNA中的2.5 mL全血），并使用Nanostring nCounter对29种响应感染的宿主mRNA进行定量。
IMX-BVN-3分类器在151例患者中识别出SARS-CoV-2感染，灵敏度为93.8%。分类器未检测出的10例患者中有6例在入组前9天以上COVID检测呈阳性，其余患者在后续检测中结果在阳性和阴性之间波动。该分类器还预测6例（3.7%）患者存在细菌合并感染。临床判定证实5/6（83.3%）的患者存在细菌感染，即结肠炎（n = 1）、尿路感染（n = 1）和临床诊断的细菌感染（n = 3），特异性为99.4%。IMX-SEV-3低严重度分类中的2/101（2.8%）患者和中度严重度分类中的7/60（11.7%）患者在入组后30天内死亡。
IMX-BVN-3/IMX-SEV-3分类器准确识别了COVID-19患者、细菌合并感染，并预测了患者的死亡风险。正在开发的这些分类器的即时检测版本可以改善急诊科患者管理，包括做出更准确的治疗决策和优化资源利用。
