ABSTRACT
Purpose: The post-pandemic era calls for appropriate literature on chest X-ray score cut-offs, enabling swift categorization and faster radiological reporting of patients with acute respiratory distress syndrome (ARDS)-like illnesses, hence prompting healthcare equity in low-resource centres where extortionate modalities of imaging such as computed tomography (CT) are unavailable. In this study, we aim to bridge the literature gap using the versatile zonal scoring system. Material and methods: This retrospective cohort study uses data from 751 COVID-19 RT-PCR+ patients. Concordant chest radiograph (CXR) scores were reported, and inter-rater reliability was measured using kappa indices. receiver operating characteristic curves were used to establish cut-off scores for the outcomes of interest: mild or severe disease, admission to an intensive care unit (ICU), and intubation. Categorical data were expressed using means and percentages, and c2 or t-tests were used for comparison at an a level of 0.05. Unadjusted odds ratios for each outcome of interest vs. CXR score and comorbidity were then calculated using binary logistic regression. Results: CXR findings included infiltrates (46.07%), pleural effusions (7.05%), consolidation and fibrosis (4.43%), pneumothoraces (2.71%), and cardiomegaly (2.26%). Most patients had an index CXR score of 0 (54.19%). The index cut-off score of ≤ 1 (82.95, 81.68) was established for mild disease, ≥ 4 for severe disease (85.71, 83.99), ≥ 3 for ICU admission (86.90, 71.91), and ≥ 4 for intubation (87.61, 72.90). Hypertension, type 2 diabetes mellitus, hypothyroidism, history of ischaemic heart disease, and history of tuberculosis were independent risk factors for a high CXR index score, intubation, and ICU admission. Conclusions: CXR scores can be effectively used in low-resource settings for triaging patients, maintaining records, and disease prognostication.
ABSTRACT
PURPOSE: We aimed to characterize novel coronavirus infections based on imaging [chest X-ray and chest computed tomography (CT)] at the time of admission. MATERIALS AND METHODS: We extracted data from 396 patients with laboratory-confirmed COVID-19 who were managed at 68 hospitals in Japan from January 25 to September 2, 2020. Case patients were categorized as severe (death or treatment with invasive ventilation during hospitalization) and non-severe groups. The imaging findings of the groups were compared by calculating odds ratios (ORs) and 95% confidence intervals (95% CIs), adjusted for sex, age, and hospital size (and radiographic patient positioning for cardiomegaly). Chest X-ray and CT scores ranged from 0 to 72 and 0 to 20, respectively. Optimal cut-off values for these scores were determined by a receiver-operating characteristic (ROC) curve analysis. RESULTS: The median age of the 396 patients was 48 years (interquartile range 28-65) and 211 (53.3%) patients were male. Thirty-two severe cases were compared to 364 non-severe cases. At the time of admission, abnormal lesions on chest X-ray and CT were mainly observed in the lower zone/lobe. Among severe cases, abnormal lesions were also seen in the upper zone/lobe. After adjustment, the total chest X-ray and CT score values showed a dose-dependent association with severe disease. For chest X-ray scores, the area under the ROC curve (AUC) was 0.91 (95% CI = 0.86-0.97) and an optimal cut-off value of 9 points predicted severe disease with 83.3% sensitivity and 84.7% specificity. For chest CT scores, the AUC was 0.94 (95% CI = 0.89-0.98) and an optimal cut-off value of 11 points predicted severe disease with 90.9% sensitivity and 82.2% specificity. Cardiomegaly was strongly associated with severe disease [adjusted OR = 24.6 (95% CI = 3.7-166.0)]. CONCLUSION: Chest CT and X-ray scores and the identification of cardiomegaly could be useful for classifying severe COVID-19 on admission.