• Angeletti, Chiara
• Pasquini, Luca
• Ranieri, Sofia Chiatamone
• Franchi, Paola
• Bernardini, Antonio
• Stefanini, Teseo
• Ortis, Piermaria
• Pietrantonio, Filomena
• Boellis, Alessandro
• Curti, Simona
• Tagliente, Emanuela
• Capotondi, Carlo
• Napoli, Alberto Di
• Voicu, Ioan Paul
• Cipriano, Enrica
• Napolitano, Antonio

Abstract

<jats:sec><jats:title>Abstract</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Key Points</jats:title><jats:p>• 3D CT-based deep learning model predicted the internal validation set with high accuracy, sensibility and specificity (&gt; 90%) mortality, ICU admittance, and intubation in COVID-19 patients.</jats:p><jats:p>• 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.</jats:p></jats:sec>

Topics

• impedance spectroscopy
• size-exclusion chromatography