Classifiers Could Identify Gastric Co-Infection Risks in Patients with COVID-19

Nikhil Ram Mohan, PhD

A 29-host-mRNA response classifier showed ability to detect bacterial co-infections, as well as superinfections, among patients with confirmed COVID-19.

In new abstract presented at the Society of Advanced Emergency Medicine (SAEM) 2022 Meeting in New Orleans this week, a team of academic and industry investigators reported that the dynamic host response platform may help emergency and acute care setting clinicians address various infection risks at once, particularly gastric diseases.

Led by Nikhil Ram Mohan, PhD, of the Stanford University School of Medicine’s emergency medicine department, investigators sought to assess the accuracy of the IMX-BVN-3 and IMX-SEV-3 host response classifiers in detecting SARS-CoV-2 infection, bacterial co-infections and superinfections, and predicting severity of COVID-19 disease in confirmed patients.

The team, supported by diagnostic company Inflammatix Inc., emphasized the potential significance of platforms being capable of providing such accuracy for coinciding risks during the ongoing pandemic. The 2 classifiers included in the study have been reported, they wrote, to help accurately determine both the likelihood of bacterial and viral infection—from very unlikely to very likely—and severity of illness—from low to high—in patients without COVID-19.

“The major clinical challenges currently faced by emergency department (ED) clinicians assessing patients with suspected infection include accurate identification of COVID-19, detection of bacterial co- or superinfection, and determination of severity of illness,” Mohan and colleagues wrote. “Currently no single biomarker or clinical algorithm can perform all 3 critical roles.”

Their assessment included 161 patients with PCR-confirmed COVID-19 infection who were enrolled through the Stanford University Medical Center. Median patient age was 50.0 years old; 52.2% were female. Approximately half (51.0%) were hospitalized during the assessment, and 9 had died.

Investigators extracted RNA via 2.5 mL whole blood samples assayed through PAXgene Blood RNA, and 29 host mRNAs were quantified via Nanostring nCounter. COVID-19 and bacterial co-infection or superinfection risks were defined as very unlikely, unlikely, likely, or very likely. Disease severity risks were defined as low, moderate, or high.

Nearly all patients (n = 151 [93.8%]) were allocated into the Very Likely or Likely viral bands; 5 patients each were placed into the Unlikely and Very Unlikely viral bands. Of the latter 10 patients, 6 had positive COVID-19 tests >9 days prior to admission; the remaining 4 had results oscillating between positive or negative, or were asymptomatic.

Regarding bacterial infection risk, 155 (96.3%) were classified in the Unlikely or Very Unlikely groups; 6 were classified as Likely. Of the latter 6, clinical adjudication confirmed that 4 (66.7%) had either bacterial coinfection Clostridium difficile colitis, fecal pathogens in urine, or clinically suspected bacterial superinfections. All 9 patients to die during hospitalization came from either the Low (n = 2 [2.8%]) or Moderate (n = 7 [11.7%]) severity disease classifications.

“In conclusion, IMX-BVN-3/SEV-3 accurately identified patients with COVID-19, bacterial co/ superinfections, and stratified clinical outcomes,” investigators wrote. “Such a multifaceted host response-based platform could improve ED patient management, accurate diagnoses, and optimized resource utilization.”

The study, “29-mRNA Host Response Classifier Detects Bacterial Co-infection and Superinfection and Predicts Outcomes in COVID-19 Patients,” was presented at SAEM 2022.