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.

Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study

Background: The high mortality rate of COVID-19 makes it necessary to seek early identification of high-risk patients with poor prognoses. Although the association between CT-SS and mortality of COVID-19 patients was reported, its prognosis significance in combination with other prognostic parameters was not evaluated yet. Method(s): This retrospective single-center study reviewed a total of 6854 suspected patients referred to Imam Khomeini hospital, Ilam city, west of Iran, from February 9, 2020 to December 20, 2020. The prognostic performances of k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and J48 decision tree algorithms were evaluated based on the most important and relevant predictors. The metrics derived from the confusion matrix were used to determine the performance of the ML models. Result(s): After applying exclusion criteria, 815 hospitalized cases were entered into the study. Of these, 447(54.85%) were male and the mean (+/- SD) age of participants was 57.22(+/- 16.76) years. The results showed that the performances of the ML algorithms were improved when they are fed by the dataset with CT-SS data. The kNN model with an accuracy of 94.1%, sensitivity of 100. 0%, precision of 89.5%, specificity of 88.3%, and AUC around 97.2% had the best performance among the other three ML techniques. Conclusion(s): The integration of CT-SS data with demographics, risk factors, clinical manifestations, and laboratory parameters improved the prognostic performances of the ML algorithms. An ML model with a comprehensive collection of predictors could identify high-risk patients more efficiently and lead to the optimal use of hospital resources.Copyright © 2023, The Author(s).

Correlation of Inflammatory Markers with Radiological Findings in Novel COVID-19 Diseased Patients in a Tertiary Care Centre in South India

Background: In December 2019, SARS-COV-2 infection emerged in Wuhan, China causing COVID-19 and subsequently spread throughout the globe. Systemic inflammation has been reported as a predictor for COVID-19 outcomes. Elevated levels of inflammatory markers are shown to be associated with endothelial dysfunction, cytokine storm and coagulopathy in COVID- 19. Raised inflammatory markers influences the mortality in severe Covid-19. Objective(s): The aim of the study is to correlate the inflammatory markers with CT severity score among COVID-19 patients. Material(s) and Method(s): Retrospective cross-sectional study conducted among 250 patients admitted in the COVID 19 isolation wards confirmed by RT-PCR. The study was conducted over a period of six months (April 2021 to September 2021) based on data's from the central laboratory registers in Biochemistry & CT Severity Score from the medical records. Result(s): Statistically significant elevation in Ferritin, CRP, LDH and D dimer among the severely affected group of patients is noted, all four markers are positively correlated with CT scores & increases with increase in the disease severity. It is observed that SpO2 decreases with increase in the severity of the disease. Conclusion(s): In this study we found significant correlation of the raised inflammatory markers and the CT severity score and the disease severity which highlights the prognostic significance of the inflammatory markers that would guide us in the diagnosis and management of critically ill patients at the earliest.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

Prognostic impact of CT severity score in childhood cancer with SARS-CoV-2

BackgroundCT chest severity score (CTSS) is a semi-quantitative measure done to correlate the severity of the pulmonary involvement on the CT with the severity of the disease.The objectives of this study are to describe chest CT criteria and CTSS of the COVID-19 infection in pediatric oncology patients, to find a cut-off value of CTSS that can differentiate mild COVID-19 cases that can be managed at home and moderate to severe cases that need hospital care.A retrospective cohort study was conducted on 64 pediatric oncology patients with confirmed COVID-19 infection between 1 April and 30 November 2020. They were classified clinically into mild, moderate, and severe groups. CT findings were evaluated for lung involvement and CTSS was calculated and range from 0 (clear lung) to 20 (all lung lobes were affected).ResultsOverall, 89% of patients had hematological malignancies and 92% were under active oncology treatment. The main CT findings were ground-glass opacity (70%) and consolidation patches (62.5%). In total, 85% of patients had bilateral lung involvement, ROC curve showed that the area under the curve of CTSS for diagnosing severe type was 0.842 (95% CI 0.737–0.948). The CTSS cut-off of 6.5 had 90.9% sensitivity and 69% specificity, with 41.7% positive predictive value (PPV) and 96.9% negative predictive value (NPV). According to the Kaplan–Meier analysis, mortality risk was higher in patients with CT score > 7 than in those with CTSS < 7.ConclusionPediatric oncology patients, especially those with hematological malignancies, are more vulnerable to COVID-19 infection. Chest CT severity score > 6.5 (about 35% lung involvement) can be used as a predictor of the need for hospitalization.

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.

Discrepancy between biomarkers of lung injury and 1-year mortality in COVID-19.

BACKGROUND: COVID-19 global pandemic started in late 2019 with the first wave. In this cross-sectional and observational study, we evaluated the associations between the biomarkers, COVID-19 pneumonia severity and 1-year mortality. METHODS: A sample of 276 polymerase chain reaction (PCR)-positive patients for SARS-CoV-2 was included. Computerized tomography severity score (CT-SS) was used to assess the severity of COVID-19 pneumonia in 222 cases. Multivariate analyses were performed to find the predictors of CT-SS, severe CT-SS (≥20) and 1-year mortality. Biomarkers of ferritin, high-sensitive C-reactive protein (CRP), lactate dehydrogenase (LDH), cardiac troponin (cTn), neutrophil-to-lymphocyte ratio (NLR), uric acid (UA) and d-dimer were routinely measured. RESULTS: Severe CT-SS (>20) was observed in 86 (31.2%) cases. Mortality was observed in 75 (27.2%) patients at 1 year. LDH displayed the highest predictive accuracy for severe CT-SS (AUC 0.741, sensitivity = 81% and specificity = 68%, cut-off value: 360 mg/dl). Linear regression analysis displayed that LDH predicted CT-SS [B = 11 (95% CI for B = 5-17, p < .001)]. Age was the most significant parameter that was associated with severe CT-SS (OR 0.96, 95% CI 0.92-0.99, p = .015). d-dimer was the only biomarker that predicted with 1-year mortality (OR 1.62, 95% CI 1.08-2.42, p = .020). CONCLUSION: LDH is a sensitive and specific biomarker to determine patients with severe lung injury in COVID-19. d-dimer is the only biomarker that predicts 1-year mortality. Neither LDH nor CT-SS is associated with 1-year mortality.

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).

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.

Clinical Dilemma of Diabetic Ketoacidosis and COVID-19 Infection

The COVID-19 Pneumonia with diabetic ketoacidosis is a dreadful health condition. Diabetic ketoacidosis is one of the severe metabolic complications and it can be precipitated by infection. We presented a case of 48 years female with no known comorbidities who presented with COVID-19 symptoms and with Diabetic Ketoacidosis. The case presented with elevated inflammatory markers, high anion gap metabolic acidosis with type I respiratory failure. During admission, the oxygen saturation had marked drop, later her improvement was steady followed by gradual tapering of the oxygenation. Marked improvement was noticed in the subsequent follow-up. COVID-19 infection can be precipitated by preexisting diabetes or newly diagnosed diabetes and the severity of COVID-19 infection is more pronounced in patients with diabetes mellitus, thus should be managed timely and accordingly. The scarce studies among the COVID-19 cases with diabetic ketoacidosis reflect the need for further studies for the availability of a wider range of information. Copyright © 2021, Kathmandu University. All rights reserved.

Interim Analysis of Impact of Adding Low Dose Pulmonary Radiotherapy to Moderate COVID-19 Pneumonia Patients: IMpaCt-RT Study.

Background: Treatment for coronavirus disease 2019 (COVID-19) pneumonia remains largely supportive till date and multiple clinical trials took place within the short span of time to evaluate the role of investigational therapies. The anti-inflammatory effect of low dose whole lung radiation in treating pneumonia has been documented earlier. This clinical trial analyzed the effect of low dose radiation therapy (LDRT) in a moderately affected COVID-19 pneumonia patient cohort and has evaluated its effect in stopping the conversion of moderate disease into severe disease. Methods: Patients with moderate COVID-19 pneumonia as characterized by the Ministry of Health and Family Welfare (MOHFW), Government of India, were randomized (1:1) to low dose whole lung radiation versus no radiation. All treatment of patients was concurrently being given as per institutional protocol. Patients were followed up with clinical and laboratory parameters monitored on Days 1, 3, 7, and 14. Computed tomography scan (CT scan) of thorax was performed on Days 1 and 7. Patients were evaluated for conversion of moderate into severe disease as per National Early Warning Score-2 (NEWS-2 score) as the primary end point. The secondary endpoints included changes in ratio between peripheral capillary oxygen saturation and fraction of inspired oxygen (SpO2/FiO2), biochemical markers, 25-point CT severity score, and radiation induced acute pulmonary toxicities. Findings: At the interim analysis, there were seven patients in the radiation arm and six in the control. A whole lung LDRT improved the outcome of SpO2/FiO2 at Day 3; however it did not convert into a statistically significant improvement for the NEWS-2 score. The serum levels of LDH, CRP, Ferritin and D-dimer were significantly reduced on 14 days in the LDRT arm in comparison to the baseline value but were not significant between the two groups. Interpretation: LDRT seems to have the potential to prevent moderate COVID-19 pneumonia from a deteriorating to severe category. However, further randomized clinical trial with an adequate number of such patients is warranted to establish the definitive role of LDRT in the management of COVID-19 pneumonia. Funding: An intramural research project bearing code: I-27/621, was sanctioned from the All India Institute of Medical Sciences, Patna, India. Clinical Trial Registration: Clinical Trials Registry-India (CTRI/2021/06/033912, 25th May 2021) ctri.nic.in/Clinicaltrials/login.php.

Chest CT features of coronavirus disease 2019 (COVID-19) in pediatric patients and its correlation with the clinical condition

Background: All pediatric health organizations are concerned about the impact of coronavirus disease on children, especially on those with other comorbidities;fortunately, pediatric cases appear to be less severe than in adults (De Luca et al. in Pediatr Respir Rev 5:9-14, 2020). The purpose of this study is to characterize chest CT findings of children with and without comorbidities who had confirmed coronavirus disease (COVID-19) and to investigate the relation between chest CT findings and the clinical severity of COVID-19 pneumonia and their laboratory findings.Results: The study was conducted on 36 patients, 72.2% of whom had associated comorbidities. Twenty-three patients (63.88%) had abnormal CT findings. Consolidative patches were the most common radiological sign (55.6%) followed by ground glass opacities (50%). The lesions were bilateral (58.3%), having predominantly peripheral distribution (38.9%) with predominant left lower lobe affection (25%). Cases with clinically severe chest conditions had significantly more prevalent consolidative patches (p = 0.026) which show a higher CT density (p = 0.01) and a significantly higher CT severity score (SS) compared to other groups (p = 0.029). The cutoff of severity score 4/20 had 100% sensitivity and 78.12% specificity in the diagnosis of severe cases. There were no statistically significant differences between patients with or without comorbidities regarding CT-SS or any radiological signs.Conclusions: Consolidation was the most common radiological finding in children with COVID-19 and was more prevalent and denser in severe cases. The CT-SS may be used as a complementary tool for the evaluation of the severity of the chest condition. Chest CT-SS more than 4 can be used as an indicator of severe cases, yet no significant difference in CT-SS between patients with associated comorbidities or not.

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.

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

Background: Lung involvement in COVID-19 can be quantified by chest CT scan with some triage and prognostication value. Optimizing initial triage of patients could help decrease adverse health impacts of the disease through better clinical management. At least 6 CT severity score (CTSS) systems have been proposed. We aimed to evaluate triage and prognostication performance of seven different CTSSs, including one proposed by ourselves, in hospitalized COVID-19 patients diagnosed by positive polymerase chain reaction (PCR). Result(s): After exclusion of 14 heart failure and significant preexisting pulmonary disease patients, 96 COVID-19, PCR-positive patients were included into our retrospective study, admitted from February 20, 2020, to July 22. Their mean age was 63.6 +/- 17.4 years (range 21-88, median 67). Fifty-seven (59.4%) were men, and 39 (40.6%) were women. All CTSSs showed good interrater reliability as calculated intraclass correlation coefficients (ICCs) between two radiologists were 0.764-0.837. Those CTSSs with more numerous segmentations showed the best ICCs. As judged by area under curve (AUC) for each receiver operator characteristic (ROC) curve, only three CTSSs showed acceptable AUCs (AUC = 0.7) for triage of severe/critical patients. All CTSSs showed acceptable AUCs for prognostication (AUCs = 0.76-0.79). Calculated AUCs for different CTSSs were not significantly different for triage and for prediction of severe/critical disease, but some difference was shown for prediction of critical disease. Conclusion(s): Men are probably affected more frequently than women by COVID-19. Quantification of lung disease in COVID-19 is a readily available and easy tool to be used in triage and prognostication, but we do not advocate its use in heart failure or chronic respiratory disease patients. The scoring systems with more numerous segmentations are recommended if any future imaging for comparison is contemplated. CTSS performance in triage was much lower than earlier reports, and only three CTSSs showed acceptable AUCs in this regard. CTSS performed better for prognostic purposes than for triage as all 7 CTSSs showed acceptable AUCs in both types of prognostic ROC curves. There is not much difference among performance of different CTSSs. Copyright © 2022, The Author(s).

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.

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.

Correlation of CT severity grade with laboratory findings in COVID-19 patients

Introduction: CT chest is strongly recommended for evaluation in COVID-19 cases as it involves the respiratory system. In the current study, we correlate the CT chest with the most commonly encountered laboratory abnormalities in COVID-19 patients based on their CT severity grade. Materials and methods: This was a retrospective study, conducted in a designated COVID center in 123 hospitalized patients who were confirmed COVID-19 positive. The research was conducted over three months (August 2020 to October 2020). Patient demographics, chest CT findings with CT severity scores of the affected lung parenchyma, and laboratory values like serum D-dimer, CRP, ferritin, and lymphocyte count were reported. The association between the severity of a chest CT scan and the levels of laboratory parameters was investigated. Before the study, the local ethics committee granted its approval. Results: There were total of 123 cases, out of which 86 (30.1%) study subjects were males and 37 (69.9%) were females. There was no discernible link between gender and severity score. A positive correlation was seen between the CT imaging findings and serum D-dimer, CRP, and ferritin levels;however, a negative correlation was seen with lymphocyte count. Conclusion: A significant correlation is seen between the CT severity score with laboratory values and the disease severity. Chest CT score is an important signal of the amount of systemic inflammation and can help speed up the diagnostic procedure in symptomatic 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.