Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department

ABSTRACT 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-BV...

Full description

Bibliographic Details
Main Authors: Nikhil Ram-Mohan, Angela J. Rogers, Catherine A. Blish, Kari C. Nadeau, Elizabeth J. Zudock, David Kim, James V. Quinn, Lixian Sun, Oliver Liesenfeld, Samuel Yang
Format: Article
Language:English
Published: American Society for Microbiology 2022-12-01
Series:Microbiology Spectrum
Subjects:
Online Access:https://journals.asm.org/doi/10.1128/spectrum.02305-22
_version_ 1828113773826670592
author Nikhil Ram-Mohan
Angela J. Rogers
Catherine A. Blish
Kari C. Nadeau
Elizabeth J. Zudock
David Kim
James V. Quinn
Lixian Sun
Oliver Liesenfeld
Samuel Yang
author_facet Nikhil Ram-Mohan
Angela J. Rogers
Catherine A. Blish
Kari C. Nadeau
Elizabeth J. Zudock
David Kim
James V. Quinn
Lixian Sun
Oliver Liesenfeld
Samuel Yang
author_sort Nikhil Ram-Mohan
collection DOAJ
description ABSTRACT 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 classifiers that require just 2.5 mL of patient blood in concurrently detecting viral and bacterial infections as well as predicting the severity and 30-day outcome from the infection. A point-of-care device, in development, will circumvent the need for blood culturing and drastically reduce the time needed to detect an infection. This will negate the need for empirical use of broad-spectrum antibiotics and allow for antibiotic use stewardship. Additionally, accurate classification of the severity of infection and the prediction of 30-day severe outcomes will allow for appropriate allocation of hospital resources.
first_indexed 2024-04-11T12:14:09Z
format Article
id doaj.art-b51a9dc66cb34209894e53442bcf6e28
institution Directory Open Access Journal
issn 2165-0497
language English
last_indexed 2024-04-11T12:14:09Z
publishDate 2022-12-01
publisher American Society for Microbiology
record_format Article
series Microbiology Spectrum
spelling doaj.art-b51a9dc66cb34209894e53442bcf6e282022-12-22T04:24:26ZengAmerican Society for MicrobiologyMicrobiology Spectrum2165-04972022-12-0110610.1128/spectrum.02305-22Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency DepartmentNikhil Ram-Mohan0Angela J. Rogers1Catherine A. Blish2Kari C. Nadeau3Elizabeth J. Zudock4David Kim5James V. Quinn6Lixian Sun7Oliver Liesenfeld8Samuel Yang9Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USADepartment of Medicine—Pulmonary, Allergy & Critical Care Medicine, Stanford University School of Medicine, Stanford, California, USADepartment of Medicine/Infectious Diseases, Stanford University School of Medicine, Stanford, California, USADepartment of Medicine—Pulmonary, Allergy & Critical Care Medicine, Stanford University School of Medicine, Stanford, California, USADepartment of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USADepartment of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USADepartment of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USAInflammatix, Inc., Burlingame, California, USAInflammatix, Inc., Burlingame, California, USADepartment of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USAABSTRACT 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 classifiers that require just 2.5 mL of patient blood in concurrently detecting viral and bacterial infections as well as predicting the severity and 30-day outcome from the infection. A point-of-care device, in development, will circumvent the need for blood culturing and drastically reduce the time needed to detect an infection. This will negate the need for empirical use of broad-spectrum antibiotics and allow for antibiotic use stewardship. Additionally, accurate classification of the severity of infection and the prediction of 30-day severe outcomes will allow for appropriate allocation of hospital resources.https://journals.asm.org/doi/10.1128/spectrum.02305-22diagnosisCOVID-19bacterial superinfectioncoinfectionprognosismortality prediction
spellingShingle Nikhil Ram-Mohan
Angela J. Rogers
Catherine A. Blish
Kari C. Nadeau
Elizabeth J. Zudock
David Kim
James V. Quinn
Lixian Sun
Oliver Liesenfeld
Samuel Yang
Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department
Microbiology Spectrum
diagnosis
COVID-19
bacterial superinfection
coinfection
prognosis
mortality prediction
title Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department
title_full Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department
title_fullStr Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department
title_full_unstemmed Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department
title_short Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department
title_sort using a 29 mrna host response classifier to detect bacterial coinfections and predict outcomes in covid 19 patients presenting to the emergency department
topic diagnosis
COVID-19
bacterial superinfection
coinfection
prognosis
mortality prediction
url https://journals.asm.org/doi/10.1128/spectrum.02305-22
work_keys_str_mv AT nikhilrammohan usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment
AT angelajrogers usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment
AT catherineablish usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment
AT karicnadeau usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment
AT elizabethjzudock usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment
AT davidkim usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment
AT jamesvquinn usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment
AT lixiansun usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment
AT oliverliesenfeld usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment
AT samuelyang usinga29mrnahostresponseclassifiertodetectbacterialcoinfectionsandpredictoutcomesincovid19patientspresentingtotheemergencydepartment