ABSTRACT
Purpose: This study aimed to assess the correlation between lung ultrasound (LUS) and computed tomography (CT) findings and the predictability of LUS scores to anticipate disease characteristics, lab data, clinical severity, and mortality in patients with COVID-19. Material and methods: Fifty consecutive hospitalized PCR-confirmed COVID-19 patients who underwent chest CT scan and LUS on the first day of admission were enrolled. The LUS score was calculated based on the presence, severity, and distribution of parenchymal abnormalities in 14 regions. Results: The participants' mean age was 54.60 ± 19.93 years, and 26 (52%) were female. All patients had CT and LUS findings typical of COVID-19. The mean value of CT and LUS severity scores were 11.80 ± 3.89 (ranging from 2 to 20) and 13.74 ± 6.43 (ranging from 1 to 29), respectively. The LUS score was significantly higher in females (p = 0.016), and patients with dyspnoea (p = 0.048), HTN (p = 0.034), immunodeficiency (p = 0.034), room air SpO2 ≤ 93 (p = 0.02), and pleural effusion (p = 0.036). LUS findings were strongly correlated with CT scan results regarding lesion type, distribution, and severity in a region-by-region fashion (92-100% agreement). An LUS score of 14 or higher was predictive of room air SpO2 ≤ 93 and ICU admission, while an LUS score ≥ 12 was predictive of death (p = 0.011, 0.023, and 0.003, respectively). Conclusions: Our results suggested that LUS can be used as a valuable tool for detecting COVID-19 pneumonia and determining high-risk hospitalized patients, helping to triage and stratify high-risk patients, which waives the need to undertake irradiating chest CT and reduces the burden of overworked CT department staff.
ABSTRACT
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) continues to wreak havoc worldwide. This study assessed the ability of chest computed tomography (CT) severity score (CSS) to predict intensive care unit (ICU) admission and mortality in patients with COVID-19 pneumonia. MATERIALS AND METHODS: A total of 192 consecutive patients with COVID-19 pneumonia aged more than 20 years and typical CT findings and reverse-transcription polymerase chain reaction positive admitted in a tertiary hospital were included. Clinical symptoms at admission and short-term outcome were obtained. A semi-quantitative scoring system was used to evaluate the parenchymal involvement. The association between CSS, disease severity, and outcomes were evaluated. Prediction of CSS was assessed with the area under the receiver-operating characteristic (ROC) curves. RESULTS: The incidence of admission to ICU was 22.8% in men and 14.1% in women. CSS was related to ICU admission and mortality. Areas under the ROC curves were 0.764 for total CSS. Using a stepwise binary logistic regression model, gender, age, oxygen saturation, and CSS had a significant independent relationship with ICU admission and death. Patients with CSS ≥12.5 had about four-time risk of ICU admission and death (odds ratio 1.66, 95% confidence interval 1.66 - 9.25). The multivariate regression analysis showed the superiority of CSS over other clinical information and co-morbidities. CONCLUSION: CSS was a strong predictor of progression to ICU admission and death and there was a substantial role of non-contrast chest CT imaging in the presence of typical features for COVID-19 pneumonia as a reliable predictor of clinical severity and patient's outcome.