NETosis induction reflects COVID-19 severity and long COVID: insights from a 2-center patient cohort study in Israel.

BACKGROUND: COVID-19 severity and its late complications continue to be poorly understood. Neutrophil extracellular traps (NETs) form in acute COVID-19, likely contributing to morbidity and mortality. OBJECTIVES: This study evaluated immunothrombosis markers in a comprehensive cohort of acute and recovered COVID-19 patients, including the association of NETs with long COVID. METHODS: One-hundred-seventy-seven patients were recruited from clinical cohorts at 2 Israeli centers: acute COVID-19 (mild/moderate, severe/critical), convalescent COVID-19 (recovered and long COVID), along with 54 non-COVID controls. Plasma was examined for markers of platelet activation, coagulation, and NETs. Ex vivo NETosis induction capability was evaluated after neutrophil incubation with patient plasma. RESULTS: Soluble P-selectin, factor VIII, von Willebrand factor, and platelet factor 4 were significantly elevated in patients with COVID-19 versus controls. Myeloperoxidase (MPO)-DNA complex levels were increased only in severe COVID-19 and did not differentiate between COVID-19 severities or correlate with thrombotic markers. NETosis induction levels strongly correlated with illness severity/duration, platelet activation markers, and coagulation factors, and were significantly reduced upon dexamethasone treatment and recovery. Patients with long COVID maintained higher NETosis induction, but not NET fragments, compared to recovered convalescent patients. CONCLUSIONS: Increased NETosis induction can be detected in patients with long COVID. NETosis induction appears to be a more sensitive NET measurement than MPO-DNA levels in COVID-19, differentiating between disease severity and patients with long COVID. Ongoing NETosis induction capability in long COVID may provide insights into pathogenesis and serve as a surrogate marker for persistent pathology. This study emphasizes the need to explore neutrophil-targeted therapies in acute and chronic COVID-19.

The diagnostic value of nasal microbiota and clinical parameters in a multi-parametric prediction model to differentiate bacterial versus viral infections in lower respiratory tract infections.

BACKGROUND: The ability to accurately distinguish bacterial from viral infection would help clinicians better target antimicrobial therapy during suspected lower respiratory tract infections (LRTI). Although technological developments make it feasible to rapidly generate patient-specific microbiota profiles, evidence is required to show the clinical value of using microbiota data for infection diagnosis. In this study, we investigated whether adding nasal cavity microbiota profiles to readily available clinical information could improve machine learning classifiers to distinguish bacterial from viral infection in patients with LRTI. RESULTS: Various multi-parametric Random Forests classifiers were evaluated on the clinical and microbiota data of 293 LRTI patients for their prediction accuracies to differentiate bacterial from viral infection. The most predictive variable was C-reactive protein (CRP). We observed a marginal prediction improvement when 7 most prevalent nasal microbiota genera were added to the CRP model. In contrast, adding three clinical variables, absolute neutrophil count, consolidation on X-ray, and age group to the CRP model significantly improved the prediction. The best model correctly predicted 85% of the 'bacterial' patients and 82% of the 'viral' patients using 13 clinical and 3 nasal cavity microbiota genera (Staphylococcus, Moraxella, and Streptococcus). CONCLUSIONS: We developed high-accuracy multi-parametric machine learning classifiers to differentiate bacterial from viral infections in LRTI patients of various ages. We demonstrated the predictive value of four easy-to-collect clinical variables which facilitate personalized and accurate clinical decision-making. We observed that nasal cavity microbiota correlate with the clinical variables and thus may not add significant value to diagnostic algorithms that aim to differentiate bacterial from viral infections.

Antibiotic misuse in respiratory tract infections in children and adults-a prospective, multicentre study (TAILORED Treatment).

Respiratory tract infections (RTI) are more commonly caused by viral pathogens in children than in adults. Surprisingly, little is known about antibiotic use in children as compared to adults with RTI. This prospective study aimed to determine antibiotic misuse in children and adults with RTI, using an expert panel reference standard, in order to prioritise the target age population for antibiotic stewardship interventions. We recruited children and adults who presented at the emergency department or were hospitalised with clinical presentation of RTI in The Netherlands and Israel. A panel of three experienced physicians adjudicated a reference standard diagnosis (i.e. bacterial or viral infection) for all the patients using all available clinical and laboratory information, including a 28-day follow-up assessment. The cohort included 284 children and 232 adults with RTI (median age, 1.3 years and 64.5 years, respectively). The proportion of viral infections was larger in children than in adults (209(74%) versus 89(38%), p < 0.001). In case of viral RTI, antibiotics were prescribed (i.e. overuse) less frequently in children than in adults (77/209 (37%) versus 74/89 (83%), p < 0.001). One (1%) child and three (2%) adults with bacterial infection were not treated with antibiotics (i.e. underuse); all were mild cases. This international, prospective study confirms major antibiotic overuse in patients with RTI. Viral infection is more common in children, but antibiotic overuse is more frequent in adults with viral RTI. Together, these findings support the need for effective interventions to decrease antibiotic overuse in RTI patients of all ages.

Automating a new host-protein assay for differentiating bacterial from viral infection to reduce operator hands-on time.

Distinguishing bacterial from viral infections is often challenging, leading to antibiotic misuse, and detrimental ramifications for the patient, the healthcare system and society. A novel ELISA-based assay that integrates the circulating levels of three host-response proteins (TRAIL, IP-10 and CRP) was developed to assist in differentiation between bacterial and viral etiologies. We developed a new protocol for measuring the host-based assay biomarkers using an automated ELISA workstation. The automated protocol was validated and was able to reduce technician hands-on time by 76%, while maintaining high analytical performance. Following automation, the assay has been incorporated into the routine workflow at a pediatric department, and is performed daily on admitted and emergency department patients. The automation protocol reduces the overall burden on the hospital laboratory performing the assay. This benefit has potential to promote adoption of the host-based assay, facilitating timely triage of febrile patients and prudent use of antibiotics.

A novel host-protein assay outperforms routine parameters for distinguishing between bacterial and viral lower respiratory tract infections.

Bacterial and viral lower respiratory tract infections (LRTIs) are often clinically indistinguishable, leading to antibiotic overuse. We compared the diagnostic accuracy of a new assay that combines 3 host-biomarkers (TRAIL, IP-10, CRP) with parameters in routine use to distinguish bacterial from viral LRTIs. Study cohort included 184 potentially eligible pediatric and adult patients. Reference standard diagnosis was based on adjudication by an expert panel following comprehensive clinical and laboratory investigation (including respiratory PCRs). Experts were blinded to assay results and assay performers were blinded to reference standard outcomes. Evaluated cohort included 88 bacterial and 36 viral patients (23 did not fulfill inclusion criteria; 37 had indeterminate reference standard outcome). Assay distinguished bacterial from viral LRTI patients with sensitivity of 0.93±0.06 and specificity of 0.91±0.09, outperforming routine parameters, including WBC, CRP and chest x-ray signs. These findings support the assay's potential to help clinicians avoid missing bacterial LRTIs or overusing antibiotics.