Chest Computed Tomography Severity Score to Predict Adverse Outcomes of Patients with COVID-19

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.

CT severity score in COVID-19 patients, assessment of performance in triage and outcome prediction: a comparative study of different methods

“…Recent reports show results compatible with our study as Hajiahmadi and colleagues reported ROC curve AUC 0.764 for CTSS1 for predicting severe/critical disease in a cohort including 24% severe/critical disease patients [ 27 ], while our calculated figure was 0.79. In addition, Aminzadeh and co-workers used a CTSS method similar to our CTSS7 and reported ROC curve AUC of 0.65 for triage of severe/critical patients and 0.76 for predicting critical disease at peak disease severity [ 28 ], while our corresponding calculated values for CTSS7 were 0.69 and 0.77, respectively.…”

Prognostic significance of COVID-19 MSCT chest findings on short-term disease progression

“…The range of age in this study sample was 20–90 years with mean equals 52.51+/−13.56. Haji Ahmadi et al found significant increase in the ICU admission and mortality with increasing age, decreasing O2 saturation in patients with COVID-19 pneumonia [ 7 ].…”

“…Zheng Ye et al [ 9 ] and Haji Ahmadi et al [ 7 ] stated that most of the cases showed lower lobar predominance. This was in contrast to what was stated in the current work that absent cranio-caudal predominance was the most common pattern.…”

Influence of threshold selection strategy on the prognostic accuracy of chest CT severity score for mortality prediction of COVID-19 patients

“…Similar results were also observed in several countries. 10 , 11 , 12 , 13 , 14 These studies suggest that the patients with higher CT-SSs might have more severe clinical outcomes and are more susceptible to mortality. Determination of the appropriate classification cut-off for this prognostic factor could have a considerable role in the early diagnosis and management of patients with poor prognoses.…”