key: cord-1031858-vj1zc1e7 authors: Ghafuri, Lida; Hamzehzadeh Alamdari, Arezou; Roustaei, Shahram; Golshani Beheshti, Arefeh; Nayerpour, Ali title: Predicting Severity of Novel Coronavirus (COVID-19) Pneumonia based upon Admission Clinical, Laboratory, and Imaging Findings date: 2021-03-03 journal: Tanaffos DOI: nan sha: d73907a58acf3b8f2f87085acc348e8101cea186 doc_id: 1031858 cord_uid: vj1zc1e7 BACKGROUND: The purpose of this study was to investigate the prognostic factors in hospitalized COVID-19 pneumonia patients according to the baseline clinical, laboratory, and imaging manifestations. MATERIALS AND METHODS: In this retrospective study on the SARS-CoV-2 laboratory-confirmed cases, clinical and laboratory data were collected from 156 hospitalized patients during August to October, 2020. Baseline chest CT was assessed, and the CT severity score was then calculated. Data were compared between the two groups of patients with moderate and severe/critical conditions. RESULTS: Of the 156 participants with the age range of 25–95 years (56.87±16.88), 70 and 86 patients were in the moderate and severe/critical groups, respectively. Most patients had typical imaging features on chest CT. Compared to the moderate group, the severe/critical group were older and were mainly suffering from underlying comorbidities. The rate of confusion on admission (P=0.008) and pulse rate≥100 (p=0.04) were significantly higher in the severe/critical group. According to the CT manifestations, consolidation, central and diffuse peripheral and central distribution, patchy/segmental morphology, crazy paving pattern, pleural effusion, aorta, and coronary artery calcification were more likely to emerge in the severe/critical group (p<0.05). In contrast, round/nodular morphology mainly appeared in the moderate group (p= 0.002). The chest CT severity scores were 10.24±7.91 and 6.13±4.42 in the severe/critical and moderate groups, respectively, indicating statistically significant values. CONCLUSION: The clinical, laboratory, and chest CT findings can be used for the prognosis of COVID-19 pneumonia. Predicting the outcomes for the patients on admission can play a critical role in decision making. (2) and spreads extremely rapidly among individuals across the globe. On March 11, 2020 , the WHO announced the COVID-19 outbreak a pandemic (3) . The leading cause of death among the patients infected by the virus is respiratory failure, septic shock, multiple organ failure, and cardiac arrest (4) . Many studies have revealed that older age, lymphocytopenia, aggressive pulmonary radiographic infiltration, and the presence of comorbidities are associated with poor outcomes in patients (5, 6) . Furthermore, older age, High D-Dimer level, high lactate dehydrogenase (LDH) level, high Sequential Organ Failure Assessment (SOFA) score, cardiac injury, and hyperglycemia may increase the likelihood of in-hospital deaths (7, 8) . Although the reverse transcriptionpolymerase chain reaction (RT-PCR) test is the most common diagnostic test to detect the infected patients, many studies have documented that the sensitivity of the computerized chest tomography (CT) is higher than RT-PCR, and the patients may exhibit lung abnormalities on their chest CT regardless of the RT-PCR results. Accordingly, a chest CT is highly recommended in screening patients with typical clinical features (5, 9, 10) . This study was approved by the Ethics Committee of the Tabriz University of Medical Sciences. Informed consent was waived regarding the retrospective nature of this study since the study would bring no risk to the patients or have no effect on the subjects' rights or safety. This retrospective single-center study on the SARS-CoV-2 laboratory-confirmed cases was conducted in the Radiology Research Center of the Tabriz University of Medical Sciences. The cases were defined as confirmed positive by RT-PCR assay of nasal and pharyngeal swab specimens if the initial test results or the repeated tests were performed on the patients with an initially negative test; however, highly suspected cases were clinically and epidemiologically positive. All admitted patients who were confirmed cases with lung involvement on the chest CT (n=156) were included in this study from August to October 2020 ( Figure 1 ). Figure 2 ). and coronary artery calcification were more likely to be observed in the severe/critical group (p < 0.05) ( Table 2 ). In contrast, round/nodular opacity mainly manifested in the moderate group (p=0.002). The vascular enlargement was mainly found in the severe/critical group, but it was not statistically significant. Another point about CT imaging was that pulmonary artery diameter had a significant difference between the two groups (P = 0.035). The findings of this study were consistent with those of the previous studies, indicating that the patients in the severe/critical group as non-survivors mostly had underlying medical conditions and were older than the moderate group cases or survivors. One of the probable causes might be reduced immunity (8, 16) . Confusion on admission was also associated with poor prognosis. In the severe cases, central nervous system involvement and neurologic manifestations, mainly nonspecific symptoms such as confusion, were noticed (17) . Other findings of this study generally correspond with the early radiology research efforts (11, 16, 18) . Bilateral involvement, peripheral distribution, GGO, and consolidation were the typical chest CT hallmarks in these patients. Furthermore, the CT involvement pattern was found to be efficient in stratifying disease severity, predicting the prognosis, and assessing the mortality risk. The presentation of lung consolidation was significantly higher in the severe/critical group. This finding was in line with those of the previous studies (19) . (20) . Furthermore, the aorta and coronary artery calcifications were significantly higher in the severe/critical group and the non-survivors. This seems to be logical by considering the association between these findings and the positive medical history of cardiovascular disease, hypertension, and diabetes. Finally, the low serum magnesium level was noticed to be more likely in the severe/critical group. This can be related to kidney injury or drug-induced magnesium loss caused by broad-spectrum antibiotic administration in some cases (21) . WHO. Naming the coronavirus disease (COVID-19) and the virus that causes it WHO. Coronavirus disease 2019 (COVID-19) Clinical Features of 85 Fatal Cases of COVID-19 from Wuhan. A Retrospective Observational Study Essentials for Radiologists on COVID-19: An Update-Radiology Scientific Expert Panel Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study Have the symptoms of patients with COVID-19 changed over time during hospitalization? Fleischner Society: glossary of terms for thoracic imaging Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19 Chinese Clinical Guidance For COVID-19 Pneumonia Diagnosis and Treatment The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia Central nervous system manifestations of COVID-19: A systematic review Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review CT Features and Short-term Prognosis of COVID-19 Pneumonia: A Single-Center Study from Kashan Complement associated microvascular injury and thrombosis in the pathogenesis of severe COVID-19 infection: A report of five cases Magnesium and Drugs The authors express their gratitude to the Tabriz Razzaghi Asl and Hossein Etemadi Bonabi for helping in data collection.