Metabolomics for the Diagnosis of Secondary Infections in Critically Ill Patients With COVID-19

Secondary infections are a common occurrence in critically ill COVID-19 patients. These are difficult to identify, and antibiotic usage is high in this population. Identification of biomarkers for secondary infections would help to ensure antibiotics are being utilized only for patients who require them. This study sought to identify a panel of biomarkers capable of distinguishing critically ill COVID-19 patients with and without secondary infections.

A multicenter retrospective cohort study.

Three critical care units in Scotland, United Kingdom.

One hundred five patients admitted to critical care with COVID-19, and 49 healthy volunteer controls.

None.

Serial blood samples were obtained from critically ill COVID-19 patients with and without confirmed secondary infections, and a single sample was collected from healthy volunteers to provide baseline metabolic profiles. Metabolomic analysis was performed using liquid chromatography-mass spectrometry, and metabolites that were significantly different between patients with and without secondary infections were identified. Additionally, metabolites capable of distinguishing Gram-positive from Gram-negative organisms were also investigated. Forty patients developed a secondary infection during the study period. A significant increase in metabolites creatine and 2-hydroxyisovalerylcarnitine, and a significant reduction in S-methyl-L-cysteine were detected in patients with secondary infections. This metabolite panel could identify patients with secondary infections with an area under the curve (AUC) of 0.83 (95% CI, 0.68-0.97). Metabolites differentiating Gram-positive and Gram-negative infections included betaine, N(6)-methyllysine, and phosphatidylcholines (PCs; 38:6), PC(38:4), PC(40:6), and PC(36:4) with an AUC of 0.88 (95% CI, 0.68-1.0).

Metabolomic profiling of critically ill COVID-19 shows promise for identification of novel biomarkers for secondary infections. Larger validation studies will help to confirm these findings.