Background Italy is one of the most affected countries by the coronavirus disease 2019 (COVID-19). The responsible pathogen is named severe acute respiratory syndrome coronavirus (SARS-CoV-2). The clinical spectrum ranges from asymptomatic infection to severe pneumonia, leading to intensive care unit admission. Evidence of cerebrovascular complications associated with SARS-CoV-2 is limited. We herein report six patients who developed acute stroke during COVID-19 infection. Methods A retrospective case series of patients diagnosed with COVID-19 using reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs, who developed clinical and neuroimaging evidence of acute stroke during SARS-CoV-2 infection. Results Six patients were identified (5 men); median age was 69 years (range 57-82). Stroke subtypes were ischemic (4, 67%) and hemorrhagic (2, 33%). All patients but one had pre-existing vascular risk factors. One patient developed encephalopathy prior to stroke, characterized by focal seizures and behavioral abnormalities. COVID-19-related pneumonia was severe (i.e., requiring critical care support) in 5/6 cases (83%). Liver enzyme alteration and lactate dehydrogenase (LDH) elevation were registered in all cases. Four patients (67%) manifested acute kidney failure prior to stroke. Four patients (67%) had abnormal coagulation tests. The outcome was poor in the majority of the patients: five died (83%) and the remaining one (17%) remained severely neurologically affected (mRS: 4). Conclusions Both ischemic and hemorrhagic stroke can complicate the course of COVI-19 infection. In our series, stroke developed mostly in patients with severe pneumonia and multiorgan failure, liver enzymes and LDH were markedly increased in all cases, and the outcome was poor.

BackgroundIn mechanically ventilated Acute Respiratory Distress Syndrome (ARDS) patients with novel coronavirus disease (COVID-19), we frequently recognised the development of pneumomediastinum and/or subcutaneous emphysema despite employing a protective mechanical ventilation strategy. The purpose of this study was to determine if the incidence of pneumomediastinum/subcutaneous emphysema in COVID-19 patients was higher than in ARDS patients without COVID-19 and if this difference could be attributed to barotrauma or to lung frailty.MethodsWe identified the cohort of patients with ARDS and COVID-19 (“CoV-ARDS”), and the cohort of patients with ARDS from other causes (“noCoV-ARDS”).Patients with CoV-ARDS were admitted to ICU during the COVID-19 pandemic and had microbiologically confirmed SARS-CoV-2 infection. NoCoV-ARDS was identified by an ARDS diagnosis in the 5 years before the COVID-19 pandemic period.ResultsPneumomediastinum/subcutaneous emphysema occurred in 23 out of 169 (13.6%) patients with CoV-ARDS and in 3 out of 163 (1.9%) patients with noCoV-ARDS (p<0.001). Mortality was 56.5% in CoV-ARDS patients with pneumomediastinum/subcutaneous emphysema and 50% in patients without pneumomediastinum (p=0.46).CoV-ARDS patients had a high incidence of pneumomediastinum/subcutaneous emphysema despite the use of low tidal volume (5.9∓0.8 mL·kg−1 ideal body weight) and low airway pressure (plateau pressure 23∓4 cmH2O).ConclusionsWe observed a seven-fold increase in pneumomediastinum/subcutaneous emphysema in CoV-ARDS. An increased lung frailty in CoV-ARDS could explain this finding more than barotrauma, which, according to its etymology, refers to high transpulmonary pressure.

Highlights Stroke is an infrequent, but potentially life-threatening, complication of COVID-19. Typical features include large vessel occlusion and multi-territory stroke. Atypical presentations include PRES, vasculitis, and arterial dissection. Sedation interruption may be required for neurologic evaluation of ICU patients. Older age, elevated D-dimer, LDH, and creatinine are associated with poor outcome.

Background Lower muscle mass is a known predictor of unfavorable outcome, but its prognostic impact on COVID-19 patients is unknown. Purpose To investigate the contribution of CT-derived muscle status in predicting clinical outcomes in COVID-19 patients. Materials and Methods Clinical/laboratory data and outcomes (intensive care unit [ICU] admission and death) were retrospectively retrieved for patients with reverse transcriptase polymerase chain reaction-confirmed COVID-19, who underwent chest CT on admission in four hospitals in Northern Italy from February 21 to April 30, 2020. Extent and type of pulmonary involvement, mediastinal lymphadenopathy, and pleural effusion were assessed. Cross-sectional areas and attenuation of paravertebral muscles were measured on axial CT images at T5 and T12 vertebral level. Multivariable linear and binary logistic regression, including calculation odds ratios (OR) with 95% confidence intervals (CIs), were used to build four models to predict ICU admission and death, tested and compared using receiver operating characteristic curve (ROC) analysis. Results A total 552 patients (364 men; median age 65 years, interquartile range 54–75) were included. In a CT-based model, lower-than-median T5 paravertebral muscle area showed the highest ORs for ICU admission (OR 4.8, 95% CI 2.7–8.5; P <.001) and death (OR 2.3, 95% CI 1.0–2.9; P =.027). When clinical variables were included in the model, lower-than-median T5 paravertebral muscle area still showed the highest ORs both for ICU admission (OR 4.3; 95% CI 2.5–7.7; P <.001) and death (OR 2.3, 95% CI 1.3–3.7; P =.001). At ROC analysis, the CT-based model and the model including clinical variables showed the same area under the curve (AUC) for ICU admission prediction (AUC 0.83, P =.380) and were not different in predicting death (AUC 0.86 versus AUC 0.87, respectively, P =.282). Conclusion In hospitalized patients with COVID-19, lower muscle mass on CT was independently associated with ICU admission and hospital mortality.

for the CoViD-19 Lombardia Team OBJECTIVES: The aims of this study are to report the prevalence of delirium on admission to the unit in patients hospitalized with SARS-CoV-2 infection, to identify the factors associated with delirium, and to evaluate the association between delirium and in-hospital mortality. DESIGN: Multicenter observational cohort study. SETTINGS: Acute medical units in four Italian hospitals. PARTICIPANTS: A total of 516 patients (median age 78 years) admitted to the participating centers with SARS-CoV-2 infection from February 22 to May 17, 2020. MEASUREMENTS: Comprehensive medical assessment with detailed history, physical examinations, functional status, laboratory and imaging procedures. On admission, delirium was determined by the Diagnostic and Statistical Manual of Mental Disorders (5th edition) criteria, 4AT, m-Richmond Agitation Sedation Scale, or clinical impression depending on the site. The primary outcomes were delirium rates and in-hospital mortality. RESULTS: Overall, 73 (14.1%, 95% confidence interval (CI) = 11.0-17.3%) patients presented delirium on admission. Factors significantly associated with delirium were dementia (odds ratio, OR = 4.66, 95% CI = 2.03-10.69), the number of chronic diseases (OR = 1.20, 95% CI = 1.03; 1.40), and chest X-ray or CT opacity (OR = 3.29, 95% CI = 1.12-9.64 and 3.35, 95% CI = 1.07-10.47, for multiple or bilateral opacities and single opacity vs no opacity, respectively). There were 148 (33.4%) in-hospital deaths in the no-delirium group and 43 (58.9%) in the delirium group (P-value assessed using the Gray test <.001). As assessed by a multivariable Cox model, patients with delirium on admission showed an almost twofold increased hazard ratio for in-hospital mortality with respect to patients without delirium (hazard ratio = 1.88, 95% CI = 1.25-2.83). CONCLUSION: Delirium is prevalent and associated with in-hospital mortality among older patients hospitalized with SARS-CoV-2 infection.

COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.

Plain chest radiography remains the first diagnostic approach to diffuse infiltrative lung disease but has limited diagnostic sensitivity and specificity. Many diseases remain occult or are not correctly assessed using chest X-ray, appearing as a nonspecific ‘reticulonodular pattern’. High-resolution CT (HRCT) is actually the recommended imaging technique in the diagnosis, assessment, and follow-up of these diseases, allowing also the evaluation of the effectiveness of the medical therapy and the selection of the type and the location of the biopsy when required. Appropriate techniques must be used to acquire high-quality HRCT scans, with the thin collimation and high spatial reconstruction algorithm being the most important factors. A nodular pattern, linear and reticular opacities, cystic lesions, ground-glass opacities and consolidations are the most common HRCT patterns of diffuse infiltrative lung disease. This article reviews the role of chest radiography and HRCT in the diagnosis and assessment of these diseases, the technical aspects of HRCT, its clinical indications and the radiological pattern of the most common types of chronic diffuse infiltrative lung disease.

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