3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.

Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external validation groups included 608, 153, and 290 patients, respectively. Images, clinical, and laboratory data were fed into different customizations of a dense neural network to choose the best performing architecture for the prediction of mortality, intubation, and intensive care unit (ICU) admission. The AI model tested on CT and clinical features displayed accuracy, sensitivity, specificity, and ROC-AUC, respectively, of 91.7%, 90.5%, 92.4%, and 95% for the prediction of patient's mortality; 91.3%, 91.5%, 89.8%, and 95% for intubation; and 89.6%, 90.2%, 86.5%, and 94% for ICU admission (internal validation) in the testing cohort. The performance was lower in the validation cohort for mortality (71.7%, 55.6%, 74.8%, 72%), intubation (72.6%, 74.7%, 45.7%, 64%), and ICU admission (74.7%, 77%, 46%, 70%) prediction. The addition of the available laboratory data led to an increase in sensitivity for patient's mortality (66%) and specificity for intubation and ICU admission (50%, 52%, respectively), while the other metrics maintained similar performance results. We present a deep-learning model to predict mortality, ICU admittance, and intubation in COVID-19 patients. KEY POINTS: • 3D CT-based deep learning model predicted the internal validation set with high accuracy, sensibility and specificity (> 90%) mortality, ICU admittance, and intubation in COVID-19 patients. • The model slightly increased prediction results when laboratory data were added to the analysis, despite data imbalance. However, the model accuracy dropped when CT images were not considered in the analysis, implying an important role of CT in predicting outcomes.

Anti-AT1R autoantibodies and prediction of the severity of Covid-19.

The stimulation of AT1R (Angiotensin II Receptor Type 1) by Angiotensin II has, in addition to the effects on the renin-angiotensin system, also pro-inflammatory effects through stimulation of ADAM17 and subsequent production of INF-gamma and Interleukin-6. This pro-inflammatory action stimulate the cytokine storm that characterizes the most severe forms of SARS-CoV-2 infection. We studied the effect of AT1Rab on the AT1R on 74 subjects with SARS-CoV-2 infection with respiratory symptoms requiring hospitalization. We divided the patients into 2 groups: 34 with moderate and 40 with severe symptoms that required ICU admission. Hospitalized subjects showed a 50% reduction in the frequency of AT1Rab compared to healthy reference population. Of the ICU patients, 33/40 (82.5%) were AT1Rab negative and 16/33 of them (48.5%) died. All 7 patients positive for AT1Rab survived. These preliminary data seem to indicate a protective role played by AT1R autoantibodies on inflammatory activation in SARS-CoV-2 infection pathology.

Pleural empyema associated with alveolar-pleural fistulas in severe acute respiratory syndrome coronavirus 2.

Surgery in COVID-19 disease complicated by APF represents the last life-saving treatment option. The choice of the therapeutic period to indicate this approach is fundamental. In fact, the clinical stability of patient is necessary in order to allow single-lung ventilation and to minimize postoperative sequelae.