Application of clinical and CT imaging features in the evaluation of disease progression in patients with COVID-19

• Published in: BMC pulmonary medicine Vol. 23; no. 1; pp. 1 - 10
• Main Authors: Wu, Guobin, Zhu, Yunya, Qiu, Xingting, Yuan, Xiaoliang, Mi, Xiaojing, Zhou, Rong
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.09.2023; BioMed Central Ltd; BMC
• Subjects: Analysis; Blood; Blood oxygenation, Extracorporeal; Cardiovascular disease; Care and treatment; Cell number; Clinical features; Comorbidity; Computed tomography; COVID-19; Critical Care Medicine; CT imaging; CT imaging features; Diabetes mellitus; Diagnosis; Diagnostic imaging; Health aspects; Hospitals; Illnesses; Influenza; Intensive; Intensive care; Internal Medicine; Laboratories; Lungs; Lymphocytes; Medical colleges; Medical imaging; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Mortality; Oxygen saturation; Patient outcomes; Patients; Pneumology/Respiratory System; Pneumonia; Progression; Pulmonary arteries; Pulmonology; Regression analysis; Respiratory failure; Risk factors; Severe acute respiratory syndrome coronavirus 2; Ventilators; China; Progression; COVID-19; Clinical features; CT imaging features
• ISSN: 1471-2466; 1471-2466
• DOI: 10.1186/s12890-023-02613-2

Background The Corona Virus Disease 2019(COVID-19) pandemic has strained healthcare systems worldwide, necessitating the early prediction of patients requiring critical care. This study aimed to analyze the laboratory examination indicators, CT features, and prognostic risk factors in COVID-19 patients. Methods A retrospective study was conducted on 90 COVID-19 patients at the First Affiliated Hospital of Gannan Medical University between December 17, 2022, and March 17, 2023. Clinical data, laboratory examination results, and computed tomography (CT) imaging data were collected. Logistic multivariate regression analysis was performed to identify independent risk factors, and the predictive ability of each risk factor was assessed using the area under the receiver operating characteristic (ROC) curve. Results Multivariate logistic regression analysis revealed that comorbid diabetes (odds ratio [OR] = 526.875, 95%CI = 1.384-1960.84, P = 0.053), lymphocyte count reduction (OR = 8.773, 95%CI = 1.432–53.584, P = 0.064), elevated D-dimer level (OR = 362.426, 95%CI = 1.228-984.995, P = 0.023), and involvement of five lung lobes (OR = 0.926, 95%CI = 0.026–0.686, P = 0.025) were risk factors for progression to severe COVID-19. ROC curve analysis showed the highest predictive value for 5 lung lobes (AUC = 0.782). Oxygen saturation was positively correlated with normally aerated lung volume and the proportion of normally aerated lung volume (P < 0.05). Conclusions The study demonstrated that comorbid diabetes, lymphocyte count reduction, elevated D-dimer levels, and involvement of the five lung lobes are significant risk factors for severe COVID-19. In CT lung volume quantification, normal aerated lung volume and the proportion of normal aerated lung volume correlated with blood oxygen saturation.