Electromagnetic navigation bronchoscopy (ENB) is a minimally invasive technology that guides endoscopic tools to pulmonary lesions. ENB has been evaluated primarily in small, single-center studies; thus, the diagnostic yield in a generalizable setting is unknown. NAVIGATE is a prospective, multicenter, cohort study that evaluated ENB using the superDimension navigation system (Medtronic, Minneapolis, Minnesota). In this United States cohort analysis, 1215 consecutive subjects were enrolled at 29 academic and community sites from April 2015 to August 2016. The median lesion size was 20.0 mm. Fluoroscopy was used in 91% of cases (lesions visible in 60%) and radial endobronchial ultrasound in 57%. The median ENB planning time was 5 minutes; the ENB-specific procedure time was 25 minutes. Among 1157 subjects undergoing ENB-guided biopsy, 94% (1092 of 1157) had navigation completed and tissue obtained. Follow-up was completed in 99% of subjects at 1 month and 80% at 12 months. The 12-month diagnostic yield was 73%. Pathology results of the ENB-aided tissue samples showed malignancy in 44% (484 of 1092). Sensitivity, specificity, positive predictive value, and negative predictive value for malignancy were 69%, 100%, 100%, and 56%, respectively. ENB-related Common Terminology Criteria for Adverse Events grade 2 or higher pneumothoraces (requiring admission or chest tube placement) occurred in 2.9%. The ENB-related Common Terminology Criteria for Adverse Events grade 2 or higher bronchopulmonary hemorrhage and grade 4 or higher respiratory failure rates were 1.5% and 0.7%, respectively. NAVIGATE shows that an ENB-aided diagnosis can be obtained in approximately three-quarters of evaluable patients across a generalizable cohort based on prospective 12-month follow-up in a pragmatic setting with a low procedural complication rate.

Background: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality.Objective: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission.Method: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix.Results: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found atwww.covid19risk.ai.Conclusion: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.

Data on the pathology of COVID-19 are scarce; available studies show diffuse alveolar damage; however, there is scarce information on the chronologic evolution of COVID-19 lung lesions. The primary aim of the study is to describe the chronology of lung pathologic changes in COVID-19 by using a post-mortem transbronchial lung cryobiopsy approach. Our secondary aim is to correlate the histologic findings with computed tomography patterns. SARS-CoV-2-positive patients, who died while intubated and mechanically ventilated, were enrolled. The procedure was performed 30 min after death, and all lung lobes sampled. Histopathologic analysis was performed on thirty-nine adequate samples from eight patients: two patients (illness duration < 14 days) showed early/exudative phase diffuse alveolar damage, while the remaining 6 patients (median illness duration—32 days) showed progressive histologic patterns (3 with mid/proliferative phase; 3 with late/fibrotic phase diffuse alveolar damage, one of which with honeycombing). Immunohistochemistry for SARS-CoV-2 nucleocapsid protein was positive predominantly in early-phase lesions. Histologic patterns and tomography categories were correlated: early/exudative phase was associated with ground-glass opacity, mid/proliferative lesions with crazy paving, while late/fibrous phase correlated with the consolidation pattern, more frequently seen in the lower/middle lobes. This study uses an innovative cryobiopsy approach for the post-mortem sampling of lung tissues from COVID-19 patients demonstrating the progression of fibrosis in time and correlation with computed tomography features. These findings may prove to be useful in the correct staging of disease, and this could have implications for treatment and patient follow-up.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19), which has rapidly become epidemic in Italy and other European countries. The disease spectrum ranges from asymptomatic/mildly symptomatic presentations to acute respiratory failure. At the present time the absolute number of severe cases requiring ventilator support is reaching or even surpassing the intensive care unit bed capacity in the most affected regions and countries. To narratively summarize the available literature on the management of COVID-19 in order to combine current evidence and frontline opinions and to provide balanced answers to pressing clinical questions. Inductive PubMed search for publications relevant to the topic. The available literature and the authors' frontline-based opinion are summarized in brief narrative answers to selected clinical questions, with a conclusive statement provided for each answer. Many off-label antiviral and anti-inflammatory drugs are currently being administered to patients with COVID-19. Physicians must be aware that, as they are not supported by high-level evidence, these treatments may often be ethically justifiable only in those worsening patients unlikely to improve only with supportive care, and who cannot be enrolled onto randomized clinical trials. Access to well-designed randomized controlled trials should be expanded as much as possible because it is the most secure way to change for the better our approach to COVID-19 patients.