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Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

Rationale: Current guidelines recommend patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia receive empirical antibiotics for suspected bacterial superinfection on the basis of weak evidence. Rates of ventilator-associated pneumonia (VAP) in clinical trials of patients with SARS-CoV-2 pneumonia are unexpectedly low. Objectives: We conducted an observational single-center study to determine the prevalence and etiology of bacterial superinfection at the time of initial intubation and the incidence and etiology of subsequent bacterial VAP in patients with severe SARS-CoV-2 pneumonia. Methods: Bronchoscopic BAL fluid samples from all patients with SARS-CoV-2 pneumonia requiring mechanical ventilation were analyzed using quantitative cultures and a multiplex PCR panel. Actual antibiotic use was compared with guideline-recommended therapy. Measurements and Main Results: We analyzed 386 BAL samples from 179 patients with SARS-CoV-2 pneumonia requiring mechanical ventilation. Bacterial superinfection within 48 hours of intubation was detected in 21% of patients. Seventy-two patients (44.4%) developed at least one VAP episode (VAP incidence rate = 45.2/1,000 ventilator days); 15 (20.8%) initial VAPs were caused by difficult-to-treat pathogens. The clinical criteria did not distinguish between patients with or without bacterial superinfection. BAL-based management was associated with significantly reduced antibiotic use compared with guideline recommendations. Conclusions: In patients with SARS-CoV-2 pneumonia requiring mechanical ventilation, bacterial superinfection at the time of intubation occurs in

Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such asSchema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based onSchema.organd catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

BackgroundPatients with severe SARS-CoV-2 pneumonia experience longer durations of critical illness yet similar mortality rates compared to patients with severe pneumonia secondary to other etiologies. As secondary bacterial infection is common in SARS-CoV-2 pneumonia, we hypothesized that unresolving ventilator-associated pneumonia (VAP) drives the apparent disconnect between length-of-stay and mortality rate among these patients.MethodsWe analyzed VAP in a prospective single-center observational study of 585 mechanically ventilated patients with suspected pneumonia, including 190 patients with severe SARS-CoV-2 pneumonia. We developed CarpeDiem, a novel machine learning approach based on the practice of daily ICU team rounds to identify clinical states for each of the 12,495 ICU patient-days in the cohort. We used the CarpeDiem approach to evaluate the effect of VAP and its resolution on clinical trajectories.FindingsPatients underwent a median [IQR] of 4 [2,7] transitions between 14 clinical states during their ICU stays. Clinical states were associated with differential hospital mortality. The long length-of-stay among patients with severe SARS-CoV-2 pneumonia was associated with prolonged stays in clinical states defined by severe respiratory failure and with a lower frequency of transitions between clinical states. In all patients, including those with COVID-19, unresolving VAP episodes were associated with transitions to unfavorable states and hospital mortality.InterpretationCarpeDiem offers a machine learning approach to examine the effect of VAP on clinical outcomes. Our findings suggest an underappreciated contribution of unresolving secondary bacterial pneumonia to outcomes in mechanically ventilated patients with pneumonia, including due to SARS-CoV-2.Abstract FigureGraphical abstractDisentangling the contributions of ICU complications and interventions to ICU outcomes. (A) Traditional approaches evaluate the ICU stay as a black box with severity of illness measured on presentation and dichotomized survival at an arbitrary time point (e.g., day 28) or on ICU or hospital discharge. Hence, the effect of intercurrent complications and interventions cannot be easily measured, a problem that is compounded when ICU stays are long or significantly differ between groups. (B) Defining the ICU course by clinical features during each day in the ICU permits the association of a complication or intervention with transitions toward clinical states associated with favorable or unfavorable outcomes.

Some patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) develop severe pneumonia and acute respiratory distress syndrome1 (ARDS). Distinct clinical features in these patients have led to speculation that the immune response to virus in the SARS-CoV-2-infected alveolus differs from that in other types of pneumonia2. Here we investigate SARS-CoV-2 pathobiology by characterizing the immune response in the alveoli of patients infected with the virus. We collected bronchoalveolar lavage fluid samples from 88 patients with SARS-CoV-2-induced respiratory failure and 211 patients with known or suspected pneumonia from other pathogens, and analysed them using flow cytometry and bulk transcriptomic profiling. We performed single-cell RNA sequencing on 10 bronchoalveolar lavage fluid samples collected from patients with severe coronavirus disease 2019 (COVID-19) within 48 h of intubation. In the majority of patients with SARS-CoV-2 infection, the alveolar space was persistently enriched in T cells and monocytes. Bulk and single-cell transcriptomic profiling suggested that SARS-CoV-2 infects alveolar macrophages, which in turn respond by producing T cell chemoattractants. These T cells produce interferon-γ to induce inflammatory cytokine release from alveolar macrophages and further promote T cell activation. Collectively, our results suggest that SARS-CoV-2 causes a slowly unfolding, spatially limited alveolitis in which alveolar macrophages containing SARS-CoV-2 and T cells form a positive feedback loop that drives persistent alveolar inflammation. Analysis of bronchoalveolar lavage fluid samples from patients with SARS-CoV-2-induced respiratory failure suggests that SARS-CoV-2 infects alveolar macrophages to cause release of T cell chemoattractants, thereby inducing local inflammatory cytokine release and further T cell activation, ultimately resulting in a positive feedback loop that drives alveolar inflammation.

Rationale: The frequency of superinfection with Aspergillus among patients with severe COVID-19-related respiratory failure is unknown. The association of bronchoalveolar lavage (BAL) fluid markers, such as galactomannan, with the presence of Aspergillus infections is also unclear. Methods: Our cohort included all patients with COVID-19 admitted to the Northwestern Memorial Hospital ICU from March to November 2020 who were intubated and underwent a BAL;patients were identified as having COVID-19 by the presence of a flag in the electronic medical record. We evaluated BAL fluid data for fungal organisms and markers. Data analysis was performed in Excel and Prism, with non-parametric values compared by two-tailed Mann Whitney tests. Results: We identified 274 patients meeting study entry criteria. The median (interquartile range) age was 62 years (47-69), and 93 (34%) were female. 714 BALs were performed on these patients, and 457 galactomannan tests were sent from these BALs. Only six (2.2%) patients grew Aspergillus on BAL fluid culture;one patient never grew Aspergillus but given his significantly elevated BAL galactomannan of 7.81, was empirically treated. The median (IQR) optical density index of galactomannan for the BAL samples that grew Aspergillus was 6.69 (3.37-8.87) compared with 0.08 (0.06-0.13) for those that did not, p 0.5 as positive, with patients who grew Aspergillus on culture as gold standard, the test sensitivity was 91.7% and specificity was 94.6%;using the ATS recommended cutoff of >1.0 as positive, the sensitivity was 75.0% and specificity was 97.5%. The median (IQR) age of patients who grew Aspergillus was 71.5 years (64-80.5) (p=0.02 compared with the overall cohort). Only two were immunocompromised (diffuse large B-cell lymphoma and another was status post liver transplant). Of note, two patients who grew Aspergillus had visible mold or plaque seen during bronchoscopy;three had cavitary findings on imaging;three died. Conclusions: Aspergillus superinfection is uncommon among ventilated patients with COVID-19 but can occur in patients without typical immunocompromising risk factors. BAL fluid galactomannan levels are lower in patients without Aspergillus, and had reasonable specificity especially if cutoff of >1.0 was used. Future analysis should focus on additional factors that differentiate between these two groups.

BackgroundSevere community-acquired pneumonia secondary to SARS-CoV-2 is a leading cause of death. Current guidelines recommend patients with SARS-CoV-2 pneumonia receive empirical antibiotic therapy for suspected bacterial superinfection, but little evidence supports these recommendations.MethodsWe obtained bronchoscopic bronchoalveolar lavage (BAL) samples from patients with SARS-CoV-2 pneumonia requiring mechanical ventilation. We analyzed BAL samples with multiplex PCR and quantitative culture to determine the prevalence of superinfecting pathogens at the time of intubation and identify episodes of ventilator-associated pneumonia (VAP) over the course of mechanical ventilation. We compared antibiotic use with guideline-recommended care.ResultsThe 179 ventilated patients with severe SARS-CoV-2 pneumonia discharged from our hospital by June 30, 2020 were analyzed. 162 (90.5%) patients had at least one BAL procedure; 133 (74.3%) within 48 hours after intubation and 112 (62.6%) had at least one subsequent BAL during their hospitalization. A superinfecting pathogen was identified within 48 hours of intubation in 28/133 (21%) patients, most commonly methicillin-sensitive Staphylococcus aureus or Streptococcus species (21/28, 75%). BAL-based treatment reduced antibiotic use compared with guideline-recommended care. 72 patients (44.4%) developed at least one VAP episode. Only 15/72 (20.8%) of initial VAPs were attributable to multidrug-resistant pathogens. The incidence rate of VAP was 45.2/1000 ventilator days.ConclusionsWith use of sensitive diagnostic tools, bacterial superinfection at the time of intubation is infrequent in patients with severe SARS-CoV-2 pneumonia. Treatment based on current guidelines would result in substantial antibiotic overuse. The incidence rate of VAP in ventilated patients with SARS-CoV-2 pneumonia are higher than historically reported.

Some patients infected with Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) develop severe pneumonia and the acute respiratory distress syndrome (ARDS) [1]. Distinct clinical features in these patients have led to speculation that the immune response to virus in the SARS-CoV-2-infected alveolus differs from other types of pneumonia [2]. We collected bronchoalveolar lavage fluid samples from 86 patients with SARS-CoV-2-induced respiratory failure and 252 patients with known or suspected pneumonia from other pathogens and subjected them to flow cytometry and bulk transcriptomic profiling. We performed single cell RNA-Seq in 5 bronchoalveolar lavage fluid samples collected from patients with severe COVID-19 within 48 hours of intubation. In the majority of patients with SARS-CoV-2 infection at the onset of mechanical ventilation, the alveolar space is persistently enriched in alveolar macrophages and T cells without neutrophilia. Bulk and single cell transcriptomic profiling suggest SARS-CoV-2 infects alveolar macrophages that respond by recruiting T cells. These T cells release interferon-gamma to induce inflammatory cytokine release from alveolar macrophages and further promote T cell recruitment. Our results suggest SARS-CoV-2 causes a slowly unfolding, spatially-limited alveolitis in which alveolar macrophages harboring SARS-CoV-2 transcripts and T cells form a positive feedback loop that drives progressive alveolar inflammation.This manuscript is accompanied by an online resource: https://www.nupulmonary.org/covid-19/One sentence summarySARS-CoV-2-infected alveolar macrophages form positive feedback loops with T cells in patients with severe COVID-19.