CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study

Abstract

Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients.Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis.Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = −0.535, p < 0.001).Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

The role of artificial intelligence technology analysis of HRCT images in predicting the severity of COVID-19 pneumonia

“…The median duration of hospitalization was 20 (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31) days, admission to the intensive care unit (ICU) was needed in 144 patients with a median stay in the ICU of 16 (8-30) days.…”

“…Work on the development of this technology has already been carried out in several centers, but the published results concern a much smaller group of patients than in our study [11,12,13,31]. Pang, also using YITU CT software, performed an automatic HRCT image analysis in 140 patients with COVID-19, including the assessment of the percentage volume of inflammation, the percentage volume of ground glass and the percentage volume of consolidation [31].…”

CT of Post-Acute Lung Complications of COVID-19

“…A variety of quantitative techniques have been used to quantify COVID-19 infection in the early phase, ranging from simple densitometry to deep learning methods (95,96). Quantitative CT (QCT) assessment of severity of COVID-19 infection in the early phase of infection is an independent predictor of ICU admission and of mortality (97)(98)(99). QCT may also be used to assess sequential change in lung volumes and pulmonary opacity (99).…”

“…Quantitative CT (QCT) assessment of severity of COVID-19 infection in the early phase of infection is an independent predictor of ICU admission and of mortality (97)(98)(99). QCT may also be used to assess sequential change in lung volumes and pulmonary opacity (99). Evaluation of QCT in the post-acute stage of COVID has been more limited.…”

Chest CT in covid-19 pneumonia's follow-up: A 30 patients case series

“…To appraise COVID-19 infection in the initial phase, different techniques are used, including densitometry and deep learning methods [ 30 , 31 ]. Early phase quantitative CT assessment of severity is an autonomous forecaster of ICU hospitalization and mortality [ [32] , [33] , [34] ]. It could as well be used to appraise serial variations in lung volumes and pulmonary opacity [ 34 ].…”