Association of Initial Symptoms or Comorbidities With Pneumonia Lesions in COVID-19 Patients: Based on Artificial Intelligence-Enabled CT Quantitation

Abstract

Background: Coronavirus disease 2019 (COVID-19) patients with a larger ratio of pneumonia lesions are more likely to progress to acute respiratory distress syndrome and death. This study aimed to investigate the relationship of baseline parameters with pneumonia lesions on admission, as quantified by an artificial intelligence (AI) algorithm using computed tomography (CT) images. Methods: This retrospective study quantitatively assessed lung lesions on CT using an AI algorithm in 1630 consecutive patients confirmed with COVID-19 on admission and classified the patients into none (0%), mild (>0–25%), intermediate (>25–50%), and severe (>50%) groups, according to the lesion ratio of the whole lung. A multivariate linear regression model was established to explore the relationship between the lesion ratio and laboratory parameters. The baseline parameters associated with lung lesions, including demographics, initial symptoms, and comorbidities, were determined using a multivariate ordinal regression model. Results: The 1630 patients confirmed with COVID-19 had a median whole lung lesion ratio of 4.1%, and the right lower lung lobe had the most lesions among the five lung lobes based on the evaluation of CT using AI algorithm. The whole lung lesion ratio was associated with the levels of plasma fibrinogen (r=0.280, p<0.001), plasma D-dimer (r=0.248, p<0.001), serum α-hydroxybutyrate dehydrogenase (r=0.363, p<0.001), serum albumin (r=-0.300, p<0.001), and peripheral blood leukocyte count (r=0.194, p<0.001). Among the four patients groups categorised by whole lung lesion ratio, the highest frequency of cough (p<0.001) and shortness of breath (p<0.001) were found in the severe group, and the highest frequency of hypertension (p<0.001), diabetes (p<0.001) and anemia (p=0.039) were observed in the intermediate group. Based on baseline ordinal regression analysis, cough (p=0.009), shortness of breath (p<0.001), hypertension (p=0.002), diabetes (p=0.005), and anemia (p=0.006) were independent risk factors for more severe lung lesions. Conclusions: Based on AI-enabled CT quantitation, patients with initial symptoms of cough/shortness of breath, or with comorbidities of hypertension, diabetes, or anemia, had a higher risk for more severe lung lesions on admission in COVID-19 patients.