Medtronic. Dr. Nead has received personal fees from Medtronic. Dr. Bowling has received personal fees from Medtronic. Dr. Murgu has received personal fees from Medtronic, Boston Scientific, Pinnacle Biologics, Olympus, Cook, Auris Robotics, and Elsevier; and has stock ownership in Concordia, Boston Scientific, and Merck. Dr. Krimsky has received personal fees from Medtronic, Innovital Systems, Gala Therapeutic, SOC, and Peytant; has stock ownership with Innovital Systems and CSA Medical; and has patents pending with Medtronic and Merit. Dr. Murillo has received support from Medtronic. Dr. LeMense has received personal fees from Medtronic. Dr. Minnich has received personal fees from Medtronic. Dr. Bansal has received personal fees from Medtronic, Pinnacle Biologics, Sunovion, and Veran Medical. Dr. Ellis has received support from Medtronic. Dr. Mahajan has received personal fees from Medtronic. Dr. Gildea has received personal fees from Medtronic. Dr. Bechara has received support from Medtronic. Dr. Sztejman has received support from Medtronic. Dr. Flandes has received grants from BTG-PneumRx and Ambu; and personal fees from Medtronic, BTG-PneumRx, Olympus, Ambu, PulmonX, and Boston Scientific. Dr. Rickman has received personal fees from Medtronic, Veran Medical, BD, Olympus, and Abbvie. Dr. Benzaquen has received support from Medtronic. Dr. Hogarth has received personal fees from Medtronic, Auris Surgical Robotics, Boston Scientific, Grifols, Shire, and CSL; and has stock ownership with Auris Surgical Robotics. Dr. Linden has received support from Medtronic. Dr. Wahidi has received personal fees from Medtronic and Veran Medical. Dr. Mattingley has received personal fees from Medtronic and is current employee of Medtronic (employment began after completion of enrollment). Dr. Hood is an employee with stock ownership at Medtronic; and has stock ownership with Boston Scientific. Ms. Lin and Ms. Wolvers are employees with stock ownership at Medtronic. Dr. Khandar has received personal fees from Medtronic.

BackgroundThe outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality.ObjectiveTo develop and validate machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission.Method725 patients were used to train and validate the model including a retrospective cohort of 299 hospitalised COVID-19 patients at Wuhan, China, from December 23, 2019, to February 13, 2020, and five cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 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.ResultsThe median age was 50.0 years and 137 (45.8%) were men in the retrospective cohort. The median age was 62.0 years and 236 (55.4%) were men in five cohorts. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.89, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 57.5% to 88.0%, all 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 at www.covid19risk.ai.ConclusionThe 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.

Introduction Coronavirus disease 2019 (COVID-19) can lead to respiratory failure due to severe immune response. Treatment targeting this immune response might be beneficial but there is limited evidence on its efficacy. The aim of this study was to determine if early treatment of patients with COVID-19 pneumonia with tocilizumab and/or steroids was associated with better outcome. Methods This observational single-center study included patients with COVID-19 pneumonia who were not intubated and received either standard of care (SOC, controls) or SOC plus early (within 3 days from hospital admission) anti-inflammatory treatment. SOC consisted of hydroxychloroquine 400mg bid plus, in those admitted before March 24 th , also darunavir/ ritonavir. Anti-inflammatory treatment consisted of either tocilizumab (8mg/kg intravenously or 162mg subcutaneously) or methylprednisolone 1 mg/kg for 5 days or both. Failure was defined as intubation or death, and the endpoints were failure-free survival

Objectives To describe clinical characteristics, management and outcome of individuals with coronavirus disease 2019 (COVID-19); and to evaluate risk factors for all-cause in-hospital mortality. Methods This retrospective study from a University tertiary care hospital in northern Italy, included hospitalized adult patients with a diagnosis of COVID-19 between 25 February 2020 and 25 March 2020. Results Overall, 317 individuals were enrolled. Their median age was 71 years and 67.2% were male (213/317). The most common underlying diseases were hypertension (149/317; 47.0%), cardiovascular disease (63/317; 19.9%) and diabetes (49/317; 15.5%). Common symptoms at the time of COVID-19 diagnosis included fever (285/317; 89.9%), shortness of breath (167/317; 52.7%) and dry cough (156/317; 49.2%). An ‘atypical’ presentation including at least one among mental confusion, diarrhoea or nausea and vomiting was observed in 53/317 patients (16.7%). Hypokalaemia occurred in 25.8% (78/302) and 18.5% (56/303) had acute kidney injury. During hospitalization, 111/317 patients (35.0%) received non-invasive respiratory support, 65/317 (20.5%) were admitted to the intensive care unit (ICU) and 60/317 (18.5%) required invasive mechanical ventilation. All-cause in-hospital mortality, assessed in 275 patients, was 43.6% (120/275). On multivariable analysis, age (per-year increase OR 1.07; 95% CI 1.04–1.10; p < 0.001), cardiovascular disease (OR 2.58; 95% CI 1.07–6.25; p 0.03), and C-reactive protein levels (per-point increase OR 1.009; 95% CI 1.004–1.014; p 0.001) were independent risk factors for all-cause in-hospital mortality. Conclusions COVID-19 mainly affected elderly patients with predisposing conditions and caused severe illness, frequently requiring non-invasive respiratory support or ICU admission. Despite supportive care, COVID-19 remains associated with a substantial risk of all-cause in-hospital mortality.

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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.

Background The prevalence of kidney involvement during SARS-CoV-2 infection has been reported to be high. Nevertheless, data are lacking about the determinants of acute kidney injury (AKI) and the combined effect of chronic kidney disease (CKD) and AKI in COVID-19 patients. Methods We collected data on patient demographics, comorbidities, chronic medications, vital signs, baseline laboratory test results and in-hospital treatment in patients with COVID-19 consecutively admitted to our Institution. Chronic kidney disease was defined as eGFR < 60 mL/min per 1.73 m2 or proteinuria at urinalysis within 180 days prior to hospital admission. AKI was defined according to KDIGO criteria. The primary and secondary outcomes were the development of AKI and death. Results Of 777 patients eligible for the study, acute kidney injury developed in 176 (22.6%). Of these, 79 (45%) showed an acute worsening of a preexisting CKD, and 21 (12%) required kidney replacement therapy. Independent associates of AKI were chronic kidney disease, C-reactive protein (CRP) and ventilation support. Among patients with acute kidney injury, 111 died (63%) and its occurrence increased the risk of death by 60% (HR 1.60 [95% IC 1.21–2.49] p = 0.002) independently of potential confounding factors including hypertension, preexisting kidney damage, and comorbidities. Patients with AKI showed a significantly higher rate of deaths attributed to bleeding compared to CKD and the whole population (7.5 vs 1.5 vs 3.5%, respectively). Conclusion Awareness of kidney function, both preexisting CKD and development of acute kidney injury, may help to identify those patients at increased risk of death.

RationalePulse glucocorticoid therapy is used in hyperinflammation related to coronavirus 2019 (COVID-19). We evaluated the efficacy and safety of pulse intravenous methylprednisolone in addition to standard treatment in COVID-19 pneumonia.MethodsIn this multicenter, randomised, double-blind, placebo-controlled trial, 304 hospitalised patients with Covid-19 pneumonia were randomised to receive 1 g of methylprednisolone intravenously for 3 consecutive days or placebo in addition to standard dexamethasone. The primary outcome was the duration of the patient hospitalisation, calculated as the time interval between randomisation and hospital discharge without the need of supplementary oxygen. The key secondary outcomes were survival free from invasive ventilation with orotracheal intubation and overall survival.ResultsOverall, 112 of 151 (75.4%) patients in the pulse methylprednisolone arm and 111 of 150 (75.2%) in the placebo arm were discharged from hospital without oxygen within 30 days from randomisation. Median time to discharge was similar in both groups [15 days (95% confidence interval (CI), 13.0 to 17.0) and 16 days (95%CI, 13.8 to 18.2); hazard ratio (HR), 0.92; 95% CI 0.71–1.20; p=0.528]. No significant differences between pulse methylprednisolone and placebo arms were observed in terms of admission to Intensive Care Unit with orotracheal intubation or death (20.0% versus 16.1%; HR, 1.26; 95%CI, 0.74–2.16; p=0.176), or overall mortality (10.0% versus 12.2%; HR, 0.83; 95%CI, 0.42–1.64; p=0.584). Serious adverse events occurred with similar frequency in the two groups.ConclusionsMethylprenisolone pulse therapy added to dexamethasone was not of benefit in patients with COVID-19 pneumonia.Message of the studyPulse glucocorticoid therapy is used for severe and/or life threatening immuno-inflammatory diseases. The addition of pulse glucocorticoid therapy to the standard low dose of dexamethasone scheme was not of benefit in patients with COVID-19 pneumonia.