| Title: |
Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department |
| Authors: |
Ram-Mohan, Nikhil; Rogers, Angela J; Blish, Catherine A; Nadeau, Kari C; Zudock, Elizabeth J; Kim, David; Quinn, James V; Sun, Lixian; Liesenfeld, Oliver; Group, The Stanford COVID-19 Biobank Study; Yang, Samuel; follows:, The additional author members of the Stanford COVID-19 Biobank Study Group are as; Hashemi, Marjan M; Tjandra, Kristel C; Newberry, Jennifer A; Blomkalns, Andra L; O’Hara, Ruth; Ashley, Euan; Mann, Rosen; Visweswaran, Anita; Ranganath, Thanmayi; Roque, Jonasel; Manohar, Monali; Din, Hena Naz; Kumar, Komal; Jee, Kathryn; Noon, Brigit; Anderson, Jill; Fay, Bethany; Schreiber, Donald; Zhao, Nancy; Vergara, Rosemary; McKechnie, Julia; Wilk, Aaron; de la Parte, Lauren; Dantzler, Kathleen Whittle; Ty, Maureen; Kathale, Nimish; Rustagi, Arjun; Martinez-Colon, Giovanny; Ivison, Geoff; Pi, Ruoxi; Lee, Maddie; Brewer, Rachel; Hollis, Taylor; Baird, Andrea; Ugur, Michele; Bogusch, Drina; Nahass, Georgie; Haider, Kazim; Tran, Kim Quyen Thi; Simpson, Laura; Tal, Michal; Chang, Iris; Do, Evan; Fernandes, Andrea; Lee, Allie; Ahuja, Neera; Snow, Theo; Krempski, James |
| Contributors: |
Unnikrishnan, Meera |
| Source: |
Microbiology Spectrum, vol 10, iss 6 |
| Publisher Information: |
eScholarship, University of California |
| Publication Year: |
2022 |
| Collection: |
University of California: eScholarship |
| Subject Terms: |
3107 Microbiology (for-2020); 31 Biological Sciences (for-2020); Infectious Diseases (rcdc); Genetics (rcdc); Biodefense (rcdc); Emergency Care (rcdc); Emerging Infectious Diseases (rcdc); Lung (rcdc); Coronaviruses (rcdc); 4.2 Evaluation of markers and technologies (hrcs-rac); 4.1 Discovery and preclinical testing of markers and technologies (hrcs-rac); Infection (hrcs-hc); 3 Good Health and Well Being (sdg); Humans (mesh); Female (mesh); Middle Aged (mesh); Male (mesh); COVID-19 (mesh); SARS-CoV-2 (mesh); Coinfection (mesh); RNA; Messenger (mesh); Bacteria (mesh); Bacterial Infections (mesh); diagnosis; COVID-19; bacterial superinfection; coinfection; prognosis; mortality prediction |
| Subject Geographic: |
e02305 - e02322 |
| Description: |
Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial coinfection, 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 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and bacterial coinfections and predicting clinical severity of COVID-19. A total of 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 enrollment, and the remaining patients oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial coinfection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e., Clostridioides difficile colitis (n = 1), urinary tract infection (n = 1), and clinically diagnosed bacterial infections (n = 3), for a specificity of 99.4%. Two of 101 (2.8%) patients in the IMX-SEV-3 "Low" severity classification and 7/60 (11.7%) in the "Moderate" severity classification died within 30 days of enrollment. IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19 and bacterial coinfections 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. IMPORTANCE We assay the utility of the single-test IMX-BVN-3/IMX-SEV-3 ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
qt7m54x568; https://escholarship.org/uc/item/7m54x568; https://escholarship.org/content/qt7m54x568/qt7m54x568.pdf |
| DOI: |
10.1128/spectrum.02305-22 |
| Availability: |
https://escholarship.org/uc/item/7m54x568; https://escholarship.org/content/qt7m54x568/qt7m54x568.pdf; https://doi.org/10.1128/spectrum.02305-22 |
| Rights: |
CC-BY-NC-ND |
| Accession Number: |
edsbas.6ABC1F19 |
| Database: |
BASE |