Trajectories of host-response biomarkers and inflammatory subphenotypes in COVID-19 patients across the spectrum of respiratory support.

PurposeEnhanced understanding of the dynamic changes in the dysregulated inflammatory response in COVID-19 may help improve patient selection and timing for immunomodulatory therapies. MethodsWe enrolled 323 COVID-19 inpatients on different levels of baseline respiratory support: i) Low Flow Oxygen (37%), ii) Non-Invasive Ventilation or High Flow Oxygen (NIV_HFO, 29%), iii) Invasive Mechanical Ventilation (IMV, 27%), and iv) Extracorporeal Membrane Oxygenation (ECMO, 7%). We collected plasma samples upon enrollment and days 5 and 10 to measure host-response biomarkers. We classified subjects into inflammatory subphenotypes using two validated predictive models. We examined clinical, biomarker and subphenotype trajectories and outcomes during hospitalization. ResultsIL-6, procalcitonin, and Angiopoietin-2 were persistently elevated in patients at higher levels of respiratory support, whereas sRAGE displayed the inverse pattern. Patients on NIV_HFO at baseline had the most dynamic clinical trajectory, with 26% eventually requiring intubation and exhibiting worse 60-day mortality than IMV patients at baseline (67% vs. 35%, p<0.0001). sRAGE levels predicted NIV failure and worse 60-day mortality for NIV_HFO patients, whereas IL-6 levels were predictive in IMV or ECMO patients. Hyper-inflammatory subjects at baseline (<10% by both models) had worse 60-day survival (p<0.0001) and 50% of them remained classified as hyper-inflammatory on follow-up sampling at 5 days post-enrollment. Receipt of combined immunomodulatory therapies (steroids and anti-IL6 agents) was associated with markedly increased IL-6 and lower Angiopoietin-2 levels (p<0.05). ConclusionsLongitudinal study of systemic host responses in COVID-19 revealed substantial and predictive inter-individual variability, influenced by baseline levels of respiratory support and concurrent immunomodulatory therapies.

Noninvasive diagnosis of secondary infections in COVID-19 by sequencing of plasma microbial cell-free DNA.

BackgroundSecondary infection (SI) diagnosis in COVID-19 is challenging, due to overlapping clinical presentations, practical limitations in obtaining samples from the lower respiratory tract (LRT), and low sensitivity of microbiologic cultures. Research QuestionCan metagenomic sequencing of plasma microbial cell-free DNA (mcfDNA-Seq) help diagnose SIs complicating COVID-19? Study Design and MethodsWe enrolled 42 inpatients with COVID-19 classified as microbiologically-confirmed SI (Micro-SI, n=8), clinically-diagnosed SI (Clinical-SI, n=13, i.e. empiric antimicrobials), or no clinical suspicion for SI (No-Suspected-SI, n=21) at time of enrollment. From baseline and follow-up plasma samples (days 5 and 10 post-enrollment), we quantified mcfDNA for all detected microbes by mcfDNA sequencing and measured nine host-response biomarkers. From LRT samples among intubated subjects, we quantified bacterial burden with 16S rRNA gene quantitative PCR. ResultsWe performed mcfDNA-Seq in 82 plasma samples. Sequencing was successful in 60/82 (73.2%) samples, which had significantly lower levels of human cfDNA than failed samples (p<0.0001). McfDNA detection was significantly higher in Micro-SI (15/16 [94%]) compared to Clinical-SI samples (8/14 [57%], p=0.03), and unexpectedly common in No-Suspected-SI samples (25/30 [83%]), similar to detection rate in Micro-SI. We detected culture-concordant mcfDNA species in 13/16 Micro-SI samples (81%) and mcfDNA levels tracked with SI outcome (resolution or persistence) under antibiotic therapy. McfDNA levels correlated significantly with LRT bacterial burden (r=0.74, p=0.02) as well as plasma biomarkers of host response (white blood cell count, IL-6, IL-8, and SPD, all p<0.05). Baseline mcfDNA levels were predictive of worse 90-day survival (hazard ratio 1.30 [1.02-1.64] for each log10 mcfDNA, p=0.03). InterpretationHigh circulating levels of mcfDNA in a substantial proportion of patients with COVID-19 without clinical suspicion for SI suggest that SIs may often remain undiagnosed. McfDNA-Seq, when clinically available, can offer a non-invasive diagnostic tool for pathogen identification, with prognostic value on host inflammatory response and clinical outcomes.