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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