In this study, we focused on the analysis of basic clinical information, comorbidity, symptoms, laboratory results, clinical outcomes and CT findings in 60 critically ill COVID-19 pneumonia patients. This investigation is different from the previous ones which had included different categories of COVID-19 patients and compared mild with moderate or severe, severe with non-severe, and intensive care unit (ICU) with non-ICU patients [2, 18-21]. Of the 60 patients in our study, 10 (16.67%) died; thus, the mortality rate is similar to the 15% that was reported by Huang et al. [4] but much higher than the 1.4% for all the patients and 8.1% for the severe patients as documented by Guan et al. [3]. However, most of the patients (93.6%) remained in the hospital in the study by Guan et al.; therefore, the clinical outcomes were unknown at the time of publication despite the analysis of a large sample size. The mortality rate in our cohort is lower than the 38.5 and 49% that was reported in China [2, 4, 5, 22]. The low mortality recorded in these investigations could have been due to enrolment with the exclusion of critically ill COVID-19 patients. It could also have been due to the fact that not all the patients received CT scans within 24 h, considering the emergency of the situation.
Since the outbreak of COVID-19, several studies are available in literature and some of them have focused on the description of chest imaging features [6-15, 18-21, 23]. Typical chest CT findings include GGO and consolidation, which are seen in most of the patients, while other findings include crazy-paving pattern, interlobular thickening and linear opacities. These reported findings are based on patients with mild and moderate disease. However, there is a lack of information about analyzing the imaging features in severely ill patients. In such a context, our study has uniquely presented the imaging findings regarding the involvement and distribution patterns in critically ill or severe COVID-19 pneumonia patients. In the mild and moderate types of patients, only few pulmonary lobes were involved according to the previous studies (1-3 lobes involvement: 29-44.4%; 4 lobes involvement: 11.1-19%; 5 lobes involvement: 38-44.4%) [6, 7]. However, in this study of the critically ill patients, involvement of all 5 lobes was observed in 98% of the patients. A larger proportion of intermediate (87%) and medial (62%) areas was involved in our recruited critically ill patients, which was in contrast with the predominant peripheral involvement in the mild and moderate patients [7, 11, 13, 24]. Among the critically ill patients in this study, the mean degree of lung involvement was 2.2±0.9 with a range of 0-4, which represents nearly 50% of the lung field being involved. In the death group, the mean score was 3.3±0.5, indicating a higher degree of lung involvement. In the research by Chung et al. [7], a total of the lung severity scores of mild and moderate patients were calculated and a summation of each lobe score (with similar 0-4 scales) was performed to determine the degree of involvement of the lung field. Their results showed that the mean total lung severity score was 9.9 in a range of 0-20. Our results aid in predicting the extent of disease in these critically ill patients by analyzing the degree of lung involvement based on chest CT images.
According to the MuLBSTA scoring system [25], multilobular infiltrates, lymphocyte ≤ 0.8×109/L, bacterial coinfection, acute-smoker, quit-smoker, hypertension and age ≥60 years are the mortality risks for viral pneumonia. In this work, although 5 lobes were infiltrated in most patients, medial or parahilar area involvement and the degree of lung involvement were significantly higher in the death group. Involvement range and degree might be the potential risk predictors on CT images in the COVID-19 pneumonia patients. Our patient sample is different from those in other studies as we evaluated the clinical and imaging features in the critically ill patients with pneumonia. Since emphysema was more common in the death group, it could be hypothesized that an underlying lung disease may also affect the clinical outcome. Furthermore, patients with low lymphocyte count, hypertension and old age appeared more frequently in the death group.
The NLR was identified as the independent risk factor for predicting critical illness in the COVID-19 pneumonia patients, with 3.13 serving as a good cut-off value [26]. In this research, the average NLR of the 60 patients was 9.7±9.5, which is significantly higher than the recommended value of 3.13. Moreover, the NLR was significantly higher in the death patients (nearly double the value of the recovery group), indicating its potential to predict not only critical illness but also death in the severely ill patients.
Despite numerous reports on COVID-19 being available in literature, to our knowledge, only the clinical factors or imaging features associated with disease severity have been documented [27, 28]. For instance, Liu et al. analyzed 78 COVID-19 pneumonia patients, focusing only on the clinical factors with regard to their effects on disease progression [27]. In their group, 11 patients demonstrated deterioration of the situation, while 67 exhibited improvement or stabilization. Akin to our findings, patient age, CRP, pre-existing conditions such as smoking history and respiratory failure, albumin, and maximum body temperature on admission were identified as the factors responsible for COVID-19 progression. Li et al. compared 25 critically ill COVID-19 pneumonia patients with 55 ordinary patients in terms of their clinical and CT imaging features [28]. In addition to the common CT imaging findings associated with COVID-19 patients, they used the CT scores to determine the diagnostic value of this technique in differentiating these two situations. Older age, comorbidities, increased CRP and neutrophil ratio, decreased lymphocytes and higher CT scores were directly related to severe or critical illness. These findings, along with ours, further confirm the value of these combined factors in assisting clinical-decision making while evaluating the disease severity for prediction of outcomes.
Some limitations exist in this study. First, this was a preliminary description of the CT findings in critically ill COVID-19 pneumonia patients. The sample size was relatively small owing to the strict selection criteria applied to these patients. Thus, further studies with the inclusion of more cases should be conducted so that robust conclusions could be drawn. Second, as the CT follow-up scans were done at different clinical sites, only the first scan taken within 24 hours of critical illness onset was described in this work. Change of chest CT findings in critically ill patients, including the recovery and death process, can provide additional information for clinical outcome prediction and management assessment. Besides, we did not follow-up on the recovered patients. A recent research reported that some recovered patients may still be carriers of the virus [29]. Therefore, long-term follow-up of these patients is necessary, which should be the focus of future research. Lastly, no autopsy was performed in the death patient group. Upon comparison with the pathological results, additional and a more precise interpretation of the CT image signs will be available in the future work.
In conclusion, this study involving critically ill COVID-19 patients has revealed that ground-glass opacities, crazy-paving pattern and air bronchogram represent the most common findings, with more of the pulmonary lobes involved in the patients. Medial and intermediate area involvements in the lungs were more often seen in the death group than in the recovery group. Additionally, some clinical and laboratory factors such as patient age, co-existing emphysema, CRP and NLR could be used to predict the disease outcomes as they were significantly higher in the patients who succumbed to the disease than in those who recovered from it. Follow-up of the recovery patients is necessary to confirm whether they continue to harbor the virus despite the absence of clinical symptoms.