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...
| Κύριοι συγγραφείς: | , , , , , , , , , |
|---|---|
| Μορφή: | Άρθρο |
| Γλώσσα: | English |
| Έκδοση: |
American Society for Microbiology
2022-12-01
|
| Σειρά: | Microbiology Spectrum |
| Θέματα: | |
| Διαθέσιμο Online: | 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 |