Evaluation of the value of chest CT severity score in assessment of COVID-19 severity and short-term prognosis.

Background: Evaluations have shown that the severity of pulmonary involvement is very important in the mortality rate of patients with coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the value of chest CT severity score in assessment of COVID-19 severity and short-term prognosis. Materials and Methods: This study was a cross-sectional study with a sample size of 197 patients, including all patients admitted to Rasoul Akram Hospital, with positive polymerase chain reaction, to investigate the relationship between computed tomography (CT) severity score and mortality. The demographic data and CT scan findings (including the pattern, side, and distribution of involvement), co-morbidities, and lab data were collected. Finally, gathered data were analyzed by SPSS-26. Results: 119 (60.4%) patients were male, and 78 (39.6%) were female. The mean age was 58.58 ± 17.3 years. Totally, 61 patients died; of those, 41 (67.2%) were admitted to the intensive care unit (ICU), so there was a significant relation between death and ICU admission (P value = 0.000). Diabetes was the most common co-morbidity, followed by hypertension and IHD. There was no significant relation between co-morbidities and death (P value = 0.13). The most common patterns of CTs were interlobular septal thickening and ground glass opacities, and a higher CT severity score was in the second week from the onset of symptoms, which was associated with more mortality (P value < 0.05). Conclusion: Our study showed that a patient with a higher CT severity score of the second week had a higher risk of mortality. Also, association of the CT severity score, laboratory data, and symptoms could be applicable in predicting the patient's condition.

Clinical severity and high-resolution CT severity score in COVID-19: Is there an association.

Objectives: To identify a correlation between the clinical parameters and CT chest severity score in COVID-19. Methods: A total of 205 RT-PCR positive patients were included in this descriptive cross-sectional study with convenience sampling from November 2020 to June 2021 in KRL Hospital. The study population was stratified in disease severity as per the WHO's guidelines. Clinical and radiological characteristics were compared in survivors and non survivors to draw conclusion. Results: The mean age was 57 years and the majority of the patients 57% were male. Overall mortality was 22% and the mean CT severity score was 18. Non survivors were more tachypneic, hypoxic, had a higher CT chest severity score, higher clinical severity, more comorbid condition and higher TLC, D-Dimers, LDH, CRP, NLR. Raised CT severity score showed a conclusive correlation with greater disease severity. One way ANOVA showed a significant difference between mean CT severity score amongst different disease categories. Conclusion: Higher CT severity score corresponds to a higher clinical severity and higher chances of mortality.

Age and chest computed tomography severity score are predictors of long-COVID.

INTRODUCTION: About one-third of acute coronavirus disease 2019 (COVID-19) survivors have suffered from persisting symptoms called long-COVID. Clinical factors such as age and intensity (moderate or acute) of COVID-19 have been found to be associated with long-COVID. Many tissues might be damaged functionally or structurally during acute COVID-19 which can be detected by blood assays and chest computed tomography (CT). We aimed to evaluate the relationship between long-COVID and the initial findings of blood assays and chest CT as possible predictors. METHODOLOGY: The study included patients with acute COVID-19. Laboratory tests and chest CT were obtained from each patient at the time of admission to the hospital. Chest CT was evaluated for pneumonic involvement and severity score. Multivariable regression model was created to find the factors that were independently associated with long-COVID. RESULTS: There were 60 (38.2%) patients with long-COVID and 97 (61.8%) without. Baseline demographic, laboratory and chest CT parameters were similar in both groups, except for age, chronic lung disease and chest CT severity score (46.9 ± 15.1 years vs 52.6 ± 15.9 years, p = 0.03; 11.7% vs 3.1%, p = 0.03 and 10.3 ± 9.6 vs 6.5 ± 7.6, p = 0.02, respectively). In multivariable model, chest CT severity score (OR: 1.059, 95% CI: 1.002-1.119, p = 0.04) and age (OR: 0.953, 95% CI: 0.928-0.979, p < 0.001) were independently associated with long-COVID. CONCLUSIONS: Chest CT severity score and age were independently associated with long-COVID and may be used to predict the future risk of long-COVID.

Correlation of Computed Tomography (CT) Severity Score With Laboratory and Clinical Parameters and Outcomes in Coronavirus Disease 2019 (COVID-19).

BACKGROUND:  Coronavirus disease 2019 (COVID-19) is a potentially lethal respiratory illness caused by a newly identified coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the novelty of the virus, high caseloads, and increasing turnaround time for reverse transcriptase-polymerase chain reaction (RT-PCR) results, accurate information about the clinical course and prognosis of individual patients was largely unknown. This has forced physicians all over the world to brainstorm attempts to come up with reliable indicators like chest high-resolution computed tomography (HRCT) for any changes suggestive of COVID-19; surrogate laboratory parameters such as C-reactive protein (CRP), ferritin, D-dimer, lactate dehydrogenase (LDH), or interleukin-6 (IL-6) for assessing the severity of the disease; and other organ-specific tests to identify the multiorgan involvement in severe-to-critical COVID-19. Chest computed tomography (CT) scans play a significant role in the management of COVID-19 disease and serve as an indicator of disease severity and its possible outcome, which might help in the early identification of patients who might need critical care and earlier prognostication. METHODS: A retrospective observational study was conducted at a single center in a level 3 critical care unit (CCU) of a 750-bed teaching hospital in Hyderabad, Telangana, India, over a period of six months. All RT-PCR-positive COVID-19 patients admitted to the CCU with CT chest performed within 24 hours of admission were screened for eligibility for this study. CT severity scoring was based on chest HRCT or CT. RESULTS: Of the 110 patients, a majority (36.36%) were aged between 61 and 70 years. The mean age of our study population was 59.65±11.88 years. Of the 110 patients, the majority were admitted to the hospital for 22-28 days (24.55%), followed by 8-14 days (22.72%), and 21.82% were admitted for one day. Of the 110 patients, a majority were admitted to the CCU for seven days (41.82%), followed by 15-21 days (24.55%); and 19.09% were admitted for 8-14 days. Most of the patients were discharged (65.45%), and we had a 34.55% mortality rate in our study. We found a significant association between chest CT severity score (CTSS) and the age of the patient, duration of hospital stay, and duration of CCU stay using multivariate regression analysis. CONCLUSION: CTSS could be greatly helpful for the screening and early identification of the disease, especially in those patients awaiting an RT-PCR report or with negative RT-PCR, which would lead to appropriate isolation and treatment measures. Early detection could also help assess the progression of the disease, alter the course of management at the earliest point possible, and improve the prognostication of COVID-19 patients.

Effectiveness of BBV152 vaccine and ChAdOx1-S vaccine in preventing severe disease among vaccinated patients admitted to a designated COVID-19 hospital in India.

Purpose: Coronavirus disease 2019 (COVID-19) is a highly formidable disease. Globally, multiple vaccines have been developed to prevent and manage this disease. However, the periodic mutations of severe acute respiratory syndrome coronavirus 2 variants cast doubt on the effectiveness of commonly used vaccines in mitigating severe disease in the Indian population. This study aimed to assess the effectiveness of the BBV152 vaccine and ChAdOx1-S vaccine in preventing severe forms of the disease. Materials and Methods: This retrospective study, based on hospital records, was conducted on 204 vaccinated COVID-19 patients using a consecutive sampling approach. Data on their vaccination status, comorbidities, and high-resolution computed tomography lung reports' computed tomography severity scores were extracted from their medical records. Fisher's exact test and binomial logistic regression analysis were employed to assess the independent associations of various factors with the dependent variables. Results: Of the 204 records, 57.9% represented males, with a mean age of 61.5±9.8 years. Both vaccines demonstrated effective protection against severe illness (90.2%), with BBV152 offering slightly better protection compared to ChAdOx1-S. Male gender, partial vaccination, comorbid conditions, and the type of vaccine were identified as independent predictors of severe lung involvement. Conclusion: This study indicates that both vaccines were highly effective (90%) in preventing severe forms of the disease in fully vaccinated individuals. When comparing the two vaccines, BBV152 was slightly more effective than ChAdOx1-S in preventing severe COVID-19.

Assessment of lung involvement using HRCT among vaccinated and non-vaccinated elderly COVID-19 patients admitted in a designated hospital, Tamil Nadu - A retrospective study.

Introduction: The COVID-19 pandemic is considered one of the most devastating situations globally, the worst affected were the senior citizens. A number of initiatives were carried out to control the COVID-19 pandemic; one such important measure is the development of COVID-19 vaccines to prevent the disease. But the continuous emergence of new SARS-COV2 variants (antigenic drift) and its demographic variation in virulence makes the vaccine's efficacy questionable. This study is intended to evaluate the association between the degree of lung involvement and the effectiveness of vaccination against the disease in cases admitted to a designated hospital in Tamil Nadu. Materials and Methods: A hospital records-based-retrospective research was conducted among COVID-19 patients admitted from the 1st of April 2021 to the 31st of May 2021, and information was gathered regarding their vaccination status, comorbid conditions, and CT severity score (CTSS) in the HRCT lung report. A consecutive sampling technique was used to choose the study participants; about 120 participants were included in the study. The Chi-square test and Fisher's extract test were used to evaluate the hypothesis. The relationship between a dependent variable and independent factors was estimated using multiple linear regression. Results: Among 120 participants, about 60.2% were males and 39.8% were females. Vaccination status and comorbid conditions had a significant association with severe lung involvement in COVID-19 patients. Conclusion: Non-vaccinated patients had severe lung involvement based on the HRCT lung scan findings than the vaccinated patients. To reduce mortality, it is essential to ensure universal coverage of COVID-19 vaccination.

Effects of Patient-Based Imaging Artifacts On CT Diagnosis of COVID-19 and Its Severity.

PURPOSE: To investigate the effects of patient-induced artifacts on the diagnostic performance of the COVID-19 Reporting and Data System (CO-RADS) and the computed tomography chest severity score (CT-SS). METHODS: A single-center retrospective analysis of patients aged 18 years and older who were admitted to the authors' hospital with laboratory-confirmed COVID-19 and underwent chest CT between July and November 2021 was conducted. Patients' chest CT scans were examined by 3 radiologists for CT-SS and CO-RADS classifications. Patient-based artifacts, including metal artifacts, incomplete projection artifacts, motion artifacts, and insufficient inspiration, were identified by 3 readers who were unaware of each other. For statistical analysis, interreader agreement was investigated using Fleiss kappa () agreement analysis. RESULTS: The study population included 549 patients with a median age of 66 years (IQR, 55-75 years), 321 (58.5%) of whom were men. According to the overall CO-RADS classification, the highest interreader agreement was in patients without CT artifacts ( = 0.924), while the lowest interreader agreement was in patients with motion artifacts ( = 0.613). For the CO-RADS 1 and 2 patient groups, insufficient inspiration decreased the interreader agreement most ( = 0.712 and = 0.250, respectively). For the CO-RADS 3, 4, and 5 patient groups, motion artifacts reduced the interreader agreement most ( = 0.464, = 0.453, and = 0.705, respectively). For total CT-SS, the highest kappa value was in patients without artifacts ( = 0.574), while the lowest kappa value was in patients with motion artifacts ( = 0.374). DISCUSSION: The CT technologist can avoid patient-induced artifacts by placing patients carefully on the CT table, giving patients necessary instructions before CT acquisition, and selecting optimal scanning parameters. The authors are not aware of another study in the literature investigating the effects of patient-based artifacts on interreader agreement of CO-RADS classification and CT-SS for COVID-19. CONCLUSION: CT artifacts degrade image quality and might lead to interreader disagreement of CO-RADS classification and CT-SS for patients with COVID-19.

The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study.

Introduction: Chest computed tomography (CT) is suitable to assess morphological changes in the lungs. Chest CT scoring systems (CCTS) have been developed and use in order to quantify the severity of pulmonary involvement in COVID-19. CCTS has also been correlated with clinical outcomes. Here we wished to use a validated, relatively simple CTSS to assess chest CT patterns and to correlate CTSS with clinical outcomes in COVID-19. Patients and methods: Altogether 227 COVID-19 cases underwent chest CT scanning using a 128 multi-detector CT scanner (SOMATOM Go Top, Siemens Healthineers, Germany). Specific pathological features, such as ground-glass opacity (GGO), crazy-paving pattern, consolidation, fibrosis, subpleural lines, pleural effusion, lymphadenopathy and pulmonary embolism were evaluated. CTSS developed by Pan et al. (CTSS-Pan) was applied. CTSS and specific pathologies were correlated with demographic, clinical and laboratory data, A-DROP scores, as well as outcome measures. We compared CTSS-Pan to two other CT scoring systems. Results: The mean CTSS-Pan in the 227 COVID-19 patients was 14.6 ± 6.7. The need for ICU admission (p < 0.001) and death (p < 0.001) were significantly associated with higher CTSS. With respect to chest CT patterns, crazy-paving pattern was significantly associated with ICU admission. Subpleural lines exerted significant inverse associations with ICU admission and ventilation. Lymphadenopathy was associated with all three outcome parameters. Pulmonary embolism led to ICU admission. In the ROC analysis, CTSS>18.5 significantly predicted admission to ICU (p = 0.026) and CTSS>19.5 was the cutoff for increased mortality (p < 0.001). CTSS-Pan and the two other CTSS systems exerted similar performance. With respect to clinical outcomes, CTSS-Pan might have the best performance. Conclusion: CTSS may be suitable to assess severity and prognosis of COVID-19-associated pneumonia. CTSS and specific chest CT patterns may predict the need for ventilation, as well as mortality in COVID-19. This can help the physician to guide treatment strategies in COVID-19, as well as other pulmonary infections.

Cardiopulmonary Outcomes in Covid-19 Patients Discharged From a Tertiary Care Center: A Prospective Study.

To determine the cardiopulmonary changes in the survivors of acute COVID-19 infection at 3-6 month and 6-12 month. We followed up 53 patients out of which 28 (52%) had mild COVID-19 and 25 (48%) had severe COVID-19. The first follow-up was between 3 month after diagnosis up to 6 month and second follow-up between 6 and 12 month from the date of diagnosis of acute COVID-19. They were monitored using vital parameters, pulmonary function tests, echocardiography and a chest computed tomography (CT) scan. We found improvement in diffusing capacity for carbon monoxide (DLCO) with a median of 52% of predicted and 80% of predicted at the first and second follow-up, respectively. There was improvement in the CTSS in severe group from 22 (18-24) to 12 (10-18; p-0.001). Multivariable logistic regression revealed increased odds of past severe disease with higher CTSS at follow-up (OR-1.7 [CI 1.14-2.77]; P = 0.01). Correlation was found between CTSS and DLCO at second follow-up (r2 = 0.36; p < 0.01). Most of patients recovered from COVID-19 but a subgroup of patients continued to have persistent radiological and pulmonary function abnormalities necessitating a structured follow-up.

Three-month follow-up after severe COVID-19 infection: are chest CT results associated with respiratory outcomes and respiratory recovery in COVID-19 patients?

BACKGROUND: CT Severity Score (CT-SS) can be used to assess the extent of severe coronavirus disease 19 (COVID-19) pneumonia. Follow-up CT-SS in patients surviving COVID-19-associated hyperinflammation and its correlation with respiratory parameters remains unknown. This study aims to assess the association between CT-SS and respiratory outcomes, both in hospital and at three months after hospitalization. METHODS: Patients from the COVID-19 High-intensity Immunosuppression in Cytokine storm Syndrome (CHIC) study surviving hospitalization due to COVID-19 associated hyperinflammation were invited for follow-up assessment at three months after hospitalization. Results of CT-SS three months after hospitalization were compared with CT-SS at hospital admission. CT-SS at admission and at 3-months were correlated with respiratory status during hospitalization and with patient reported outcomes as well as pulmonary- and exercise function tests at 3-months after hospitalization. RESULTS: A total of 113 patients were included. Mean CT-SS decreased by 40.4% (SD 27.6) in three months (P < 0.001). CT-SS during hospitalization was higher in patients requiring more oxygen (P < 0.001). CT-SS at 3-months was higher in patients with more dyspnoea (CT-SS 8.31 (3.98) in patients with modified Medical Council Dyspnoea scale (mMRC) 0-2 vs. 11.03 (4.47) in those with mMRC 3-4). CT-SS at 3-months was also higher in patients with a more impaired pulmonary function (7.4 (3.6) in patients with diffusing capacity for carbon monoxide (DLCO) > 80%pred vs. 14.3 (3.2) in those with DLCO < 40%pred, P = 0.002). CONCLUSION: Patients surviving hospitalization for COVID-19-associated hyperinflammation with higher CT-SS have worse respiratory outcome, both in-hospital and at 3-months after hospitalization. Strict monitoring of patients with high CT-SS is therefore warranted.

Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis.

BACKGROUND: Prediction of outcomes in severe COVID-19 patients using chest computed tomography severity score (CTSS) may enable more effective clinical management and early, timely ICU admission. We conducted a systematic review and meta-analysis to determine the predictive accuracy of the CTSS for disease severity and mortality in severe COVID-19 subjects. METHODS: The electronic databases PubMed, Google Scholar, Web of Science, and the Cochrane Library were searched to find eligible studies that investigated the impact of CTSS on disease severity and mortality in COVID-19 patients between 7 January 2020 and 15 June 2021. Two independent authors looked into the risk of bias using the Quality in Prognosis Studies (QUIPS) tool. RESULTS: Seventeen studies involving 2788 patients reported the predictive value of CTSS for disease severity. The pooled sensitivity, specificity, and summary area under the curve (sAUC) of CTSS were 0.85 (95% CI 0.78-0.90, I2 =83), 0.86 (95% CI 0.76-0.92, I2 =96) and 0.91 (95% CI 0.89-0.94), respectively. Six studies involving 1403 patients reported the predictive values of CTSS for COVID-19 mortality. The pooled sensitivity, specificity, and sAUC of CTSS were 0.77 (95% CI 0.69-0.83, I2 = 41), 0.79 (95% CI 0.72-0.85, I2 = 88), and 0.84 (95% CI 0.81-0.87), respectively. DISCUSSION: Early prediction of prognosis is needed to deliver the better care to patients and stratify them as soon as possible. Because different CTSS thresholds have been reported in various studies, clinicians are still determining whether CTSS thresholds should be used to define disease severity and predict prognosis. CONCLUSION: Early prediction of prognosis is needed to deliver optimal care and timely stratification of patients.  CTSS has strong discriminating power for the prediction of disease severity and mortality in patients with COVID-19.

Serum and Urinary Biomarkers in COVID-19 Patients with or without Baseline Chronic Kidney Disease.

In a prospective, observational, non-interventional, single-center study, we assessed various plasma and urinary biomarkers of kidney injury (neutrophil gelatinase-associated Lipocain [NGAL], kidney-injury molecule-1 [KIM-1], and interleukin-18 [IL-18]); inflammation (IL-6, C-reactive protein [CRP]); plus angiotensin converting enzyme 2 (ACE2) in 120 COVID-19 patients (of whom 70 had chronic kidney disease (CKD) at emergency-department (ED) admission). Our aim was to correlate the biomarkers with the outcomes (death, acute kidney injury [AKI]). All patients had received a chest-CT scan at admission to calculate the severity score (0-5). Biomarkers were also assessed in healthy volunteers and non-COVID-19-CKD patients. These biomarkers statistically differed across subgroups, i.e., they were significantly increased in COVID-19 patients, except for urinary (u)KIM1 and uIL-18. Amongst the biomarkers, only IL-6 was independently associated with mortality, along with AKI and not using remdesivir. Regarding the prediction of AKI, only IL-6 and uKIM1 were significantly elevated in patients presenting with AKI. However, AKI could not be predicted. Having high baseline IL-6 levels was associated with subsequent ventilation requirement and death. The mortality rate was almost 90% when the chest CT-scan severity score was 3 or 4 vs. 6.8% when the severity score was 0-2 (p < 0.0001).

Is a high chest CT severity score a risk factor for an increased incidence of long-term neuroimaging findings after COVID-19?

OBJECTIVES: We aimed to determine the incidences of neuroimaging findings (NIF) and investigate the relationship between the course of pneumonia severity and neuroimaging findings. MATERIALS AND METHODS: Our study was a retrospective analysis of 272 (>18 years) COVID-19 patients who were admitted between "March 11, 2021, and September 26, 2022". All patients underwent both chest CT and neuroimaging. The patient's chest CTs were evaluated for pneumonia severity using a severity score system (CT-SS). The incidence of NIF was calculated. NIF were categorized into two groups; neuroimaging positive (NIP) and neuroimaging negative (NIN). Consecutive CT-SS changes in positive and negative NIF patients were analyzed. RESULTS: The median age of total patients was 71; IQR, 57-80. Of all patients, 56/272 (20.6%) were NIP. There was no significant relationship between NIP and mortality (p = 0.815) and ICU admission (p = 0.187). The incidences of NIF in our patients were as follows: Acute-subacute ischemic stroke: 47/272 (17.3%); Acute spontaneous intracranial hemorrhage: 13/272 (4.8%); Cerebral microhemorrhages: 10/272 (3.7%) and Cerebral venous sinus thrombosis: 3/25 (10.7%). Temporal change of CT-SSs, there was a statistically significant increase in the second and third CT-SSs compared to the first CT-SS in both patients with NIP and NIN. CONCLUSION: Our results showed that since neurological damage can be seen in the late period and neurological damage may develop regardless of pneumonia severity.

Chest CT severity score as a predictor of mortality and short-term prognosis in COVID-19.

Background: As India was slowly coming out of shock from the second wave wrecked by the Delta strain, the world population is now struck once again with a new strain of coronavirus disease 2019 (COVID-19), designated as B.1.1.529, named as OMICRON. Though several international studies have evaluated the role of computed tomography (CT) in diagnosis, predicting prognosis, and monitoring the progression of disease, to our best knowledge, there are no Indian studies published in this context. Objective: (1) To determine the use of chest CT severity score as predictor of mortality in COVID-19 patients. (2) To determine the prognosis based on length of hospital stay. Materials and Methods: A observational cohort study was done at Travancore Medical College Hospital. A retrospective analysis of patients who presented to the Emergency Medicine Department with a positive COVID antigen or reverse transcriptase-polymerase chain reaction (RT-PCR) results and those who underwent a CT chest at the time of presentation was conducted. Data was analyzed by using Statistical Package for Social Sciences (SPSS) version 16. Descriptive statistics such as mean, frequency, and percentages were calculated. Chi-square test was used to find the statistical significance. The Kaplan-Meier method was used to evaluate the relationship between CT score and mortality, which was compared with the log-rank test. Results: A total of 252 patients with positive COVID antigen or RT-PCR who underwent CT chest were included in our study. Our study population was composed of 139 (55.2%) males and 113 (44.8%) females. Only one patient with mild CT severity score required >14 days of ICU stay, whereas two (2%) and five (9.6%) patients with moderate and severe CT severity score, respectively, required ICU stay for >14 days. The P value was 0.001, which again is statistically significant. In our study, out of 44 patients categorized under mild CT severity score, only two (4.5%) patients had expired. Out of 98 patients categorized under moderate CT severity score, 14 (14.3%) patients had expired, whereas out of 52 patients categorized under severe CT severity score at the time of admission, 25 (48.1%) patients had expired. The P value was 0.001, which is statistically significant. Conclusion: Our study could prove that patients with CT severity score ≥15 had high risk of mortality and required prolonged ICU stay of >5 days. CT severity score helps the primary care physicians to predict probable outcome and length of hospital stay at the time of admission itself and allocate the limited resources appropriately.

Interobserver Variability in CT Severity Scoring System in COVID-19 Positive Patients.

BACKGROUND:  Chest CT scans are done in cases of coronavirus disease 2019 (COVID-19)-positive patients to understand the severity of the disease and plan treatment accordingly. Severity is determined according to a 25-point scoring system, however, there could be interobserver variability in using this scoring system thus leading to the different categorization of patients. We tried to look for this interobserver variability and thus find out its reliability. METHODS:  The study was retrospective and was done in a designated COVID center. Some 100 patients were involved in the study who tested positive for COVID-19 disease. The research was conducted over six months (January 2021 to June 2021). Images were given to three radiologists with a minimum of 10 years of experience in thoracic imaging working in different setups at different places for interpretation and scoring further and their scores were compared. Before the study, the local ethics committee granted its approval. RESULTS:  There was no significant variability in the interobserver scoring system thus proving its reliability. The standard deviation between different observers was less than three. There was almost perfect agreement amongst all the observers (Fleiss' K=0.99 [95% confidence interval, CI: 0.995-0.998]). Maximum variations were observed in the moderate class.  Conclusion: There was minimum inter-observer variability in the 25-point scoring system thus proving its reliability in categorizing patients according to severity. There was no change in the class of the patient according to its severity. A 25-point scoring system hence can be used by clinicians to plan treatment and thus improve a patient's prognosis.

Relation between macrophage inflammatory protein-1 and intercellular adhesion molecule-1 and computed tomography findings in critically-ill saudi covid-19 patients.

BACKGROUND: Several, clinical and biochemical factors were suggested as risk factors for more severe forms of Covid-19. Macrophage inflammatory protein-1 alpha (MIP-1α, CCL3) is a chemokine mainly involved in cell adhesion and migration. Intracellular adhesion molecule 1 (ICAM-1) is an inducible cell adhesion molecule involved in multiple immune processes. The present study aimed to assess the relationship between baseline serum MIP-1α and ICAM-1 level in critically-ill Covid-19 patients and the severity of computed tomography (CT) findings. METHODS: The study included 100 consecutive critically-ill patients with Covid-19 infection. Diagnosis of infection was established on the basis of RT-PCR tests. Serum MIP-1α and ICAM-1 levels were assessed using commercially available ELISA kits. All patients were subjected to a high-resolution computed tomography assessment. RESULTS: According to the computed tomography severity score, patients were classified into those with moderate/severe (n=49) and mild (n = 51) pulmonary involvement. Severe involvement was associated with significantly higher MIP-1α and ICAM-1 level. Correlation analysis identified significant positive correlations between MIP-1α and age, D-dimer, IL6, in contrast, there was an inverse correlation with INF-alpha. Additionally, ICAM-1 showed significant positive correlations with age, D-Dimer,- TNF-α, IL6,while an inverse correlation with INF-alpha was observed. CONCLUSIONS: MIP-1α and ICAM-1 level are related to CT radiological severity in Covid-19 patients. Moreover, these markers are well-correlated with other inflammatory markers suggesting that they can be used as reliable prognostic markers in Covid-19 patients.

The RALE Score Versus the CT Severity Score in Invasively Ventilated COVID-19 Patients-A Retrospective Study Comparing Their Prognostic Capacities.

BACKGROUND: Quantitative radiological scores for the extent and severity of pulmonary infiltrates based on chest radiography (CXR) and computed tomography (CT) scan are increasingly used in critically ill invasively ventilated patients. This study aimed to determine and compare the prognostic capacity of the Radiographic Assessment of Lung Edema (RALE) score and the chest CT Severity Score (CTSS) in a cohort of invasively ventilated patients with acute respiratory distress syndrome (ARDS) due to COVID-19. METHODS: Two-center retrospective observational study, including consecutive invasively ventilated COVID-19 patients. Trained scorers calculated the RALE score of first available CXR and the CTSS of the first available CT scan. The primary outcome was ICU mortality; secondary outcomes were duration of ventilation in survivors, length of stay in ICU, and hospital-, 28-, and 90-day mortality. Prognostic accuracy for ICU death was expressed using odds ratios and Area Under the Receiver Operating Characteristic curves (AUROC). RESULTS: A total of 82 patients were enrolled. The median RALE score (22 [15-37] vs. 26 [20-39]; p = 0.34) and the median CTSS (18 [16-21] vs. 21 [18-23]; p = 0.022) were both lower in ICU survivors compared to ICU non-survivors, although only the difference in CTSS reached statistical significance. While no association was observed between ICU mortality and RALE score (OR 1.35 [95%CI 0.64-2.84]; p = 0.417; AUC 0.50 [0.44-0.56], this was noticed with the CTSS (OR, 2.31 [1.22-4.38]; p = 0.010) although with poor prognostic capacity (AUC 0.64 [0.57-0.69]). The correlation between the RALE score and CTSS was weak (r2 = 0.075; p = 0.012). CONCLUSIONS: Despite poor prognostic capacity, only CTSS was associated with ICU mortality in our cohort of COVID-19 patients.

Incidence and Predictors of Cardiac Arrhythmias in Patients With COVID-19.

Background: Coronavirus disease 2019 (COVID-19) is a systemic disease caused by severe acute respiratory syndrome coronavirus 2. Arrhythmias are frequently associated with COVID-19 and could be the result of inflammation or hypoxia. This study aimed to define the incidence of arrhythmias in patients with COVID-19 and to correlate arrhythmias with pulmonary damage assessed by computed tomography (CT). Methods: All consecutive patients with a COVID-19 diagnosis hospitalized at Universitair Ziekenhuis Brussel, Belgium, between March 2020 and May 2020, were screened. All included patients underwent a thorax CT scan and a CT severity score, a semiquantitative scoring system of pulmonary damage, was calculated. The primary endpoint was the arrhythmia occurrence during follow-up. Results: In this study, 100 patients were prospectively included. At a mean follow-up of 19.6 months, 25 patients with COVID-19 (25%) experienced 26 arrhythmic episodes, including atrial fibrillation in 17 patients, inappropriate sinus tachycardia in 7 patients, atrial flutter in 1 patient, and third-degree atrioventricular block in 1 patient. No ventricular arrhythmias were documented. Patients with COVID-19 with arrhythmias showed more often need for oxygen, higher oxygen maximum flow, longer QTc at admission, and worse damage at CT severity score. In univariate logistic regression analysis, significant predictors of the primary endpoint were: the need for oxygen therapy (odds ratio [OR] 4.59, 95% CI 1.44-14.67, p = 0.01) and CT severity score of pulmonary damage (OR per 1 point increase 1.25, 95% CI 1.11-1.4, p < 0.001). Conclusions: In a consecutive cohort of patients with COVID-19 the incidence of cardiac arrhythmias was 25%. The need for oxygen therapy and CT severity score were predictors of arrhythmia occurrence during follow-up.

CT-severity score in COVID-19 patients: for whom is it applicable best?

Background: lung involvement in COVID-19 can be quantified by chest CT scan. We evaluated the triage and prognostication performance of seven proposed CT-severity score (CTSS) systems in two age groups of ≥65 and <65 years old. Methods: Confirmed COVID-19 patients by reverse transcriptase polymerase chain reaction (RT-PCR) admitted from February 20th, 2020 to July 22nd were included in a retrospective single center study. Clinical disease severity at presentation and at peak disease severity were recorded. CT images were scored according to seven different scoring systems (CTSS1-CTSS7). The cohort was divided into two age groups of ≥65 and <65 years old. Receiver operator characteristic (ROC) curves for each age group for diagnosis of severe/critical disease on admission (for triage) were plotted. Such curves were also plotted for predicting severe/critical disease at peak disease severity (for prognostication), and critical disease at peak severity (for prognostication). Areas under the curve (AUCs), best thresholds, and corresponding sensitivities (Sens.) and specificities (Spec.) were calculated. Results: 96 patients were included with a mean age of 63.6±17.4 years. All CTSSs in 65-year-old or more group (N=55) showed excellent performance (AUC=0.80-0.83, Sens.+Spec.= 155-162%) in triage and excellent or outstanding performance (AUC=0.81-0.92, Sens.+Spec.= 153-177%) in prognostication. In the younger group (N=44), all CTSSs were unsatisfactory for triage (AUC=0.49-0.57) and predicting severe/critical disease (AUC=0.67-0.70), but were acceptable for predicting critical disease (AUC=0.70-0.73, Sens.+Spec.= 132-151%). Conclusion: CTSS is an excellent tool in triage and prognostication in patients with COVID-19 ≥65 years old, but is of limited value in younger patients.

Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis.

Purpose: Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis. Methods: Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes. Results: This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157-1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307-9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both P > 0.2). The shapes of Begg's funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg's test P = 0.945 and 0.356, respectively). Conclusions: The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement.