A n outbreak of coronavirus disease 2019 (COVID-19) began in Wuhan, China, in December 2019 and has rapidly spread around the world to become a pandemic (1). Italy was the second epicenter of the spread of the disease, and at the time of writing has a total of 222 104 cases and 31 106 deaths (1). Several studies have described the spectrum of chest imaging features of COVID-19 (2). However, to date, only a few case reports have described COVID-19-associated neurologic imaging findings (3-8). The purpose of our study was to systematically characterize neurologic symptoms and neuroimaging features in hospitalized patients with COVID-19 from multiple institutions in Italy.
Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk.
Highlights
In our experience chest CT had a significantly higher specificity and accuracy in detecting COVID-19 pneumonia than previously reported.
Chest CT and RT-PCR positive rates were 485/773 (62.7 %) and 462/773 (59.7 %), respectively.
CT sensitivity and specificity for COVID 19 with RT-PCR as reference were 90.7 % and 78.8 % respectively.
CT PPV, NPV and accuracy were 86.4 %, 85.1 % and 85.9 % respectively.
Highlights
Holistic information in COVID-19 patients with imaging and non-imaging data can help predict patient outcome in terms of the need for ICU admission.
Validation of model over multiple sites is important to establish its generalizablity.
Both volume and radiomic features of pulmonary opacities are key to quantifying the extent of lung involvement.
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