CT quantification of COVID-19 pneumonia extent to predict individualized outcome

Abstract

OBJECTIVES: This study aimed to predict individual COVID-19 patient prognosis at hospital admission using artificial intelligence (AI)-based quantification of computed tomography (CT) pulmonary involvement. BACKGROUND: Assessing patient prognosis in COVID-19 pneumonia is crucial for patient management and hospital and ICU organization. METHODS: We retrospectively analyzed 559 patients with PCR-verified COVID-19 pneumonia referred to the hospital for a severe disease course. We correlated the CT extent of pulmonary involvement with patient outcome. We also attempted to define cut-off values of pulmonary involvement for predicting different outcomes. RESULTS: CT-based disease extent quantification is an independent predictor of patient morbidity and mortality, with the prognosis being impacted also by age and cardiovascular comorbidities. With the use of explored cut-off values, we divided patients into three groups based on their extent of disease: (1) less than 28 % (sensitivity 65.4 %; specificity 89.1 %), (2) ranging from 28 % (31 %) to 47 % (sensitivity 87.1 %; specificity 62.7 %), and (3) above 47 % (sensitivity 87.1 %; specificity, 62.7 %), representing low risk, risk for oxygen therapy and invasive pulmonary ventilation, and risk of death, respectively. CONCLUSION: CT quantification of pulmonary involvement using AI-based software helps predict COVID-19 patient outcomes (Tab. 4, Fig. 4, Ref. 38).