Clinical characteristics and chest CT imaging features of critically ill COVID-19 patients

The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model

BACKGROUND

Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD.

PURPOSE

The aim of this study is to investigate the value of the deep learning and radiomics features for the severity evaluation and the nucleic acid turning-negative time prediction in COVID-19 patients with COPD including two phenotypes of chronic bronchitis predominant patients and emphysema predominant patients.

METHODS

A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema group of 94 patients, COVID-19 with chronic bronchitis group of 92 patients. All patients underwent chest computed tomography (CT) scans and recorded clinical data. The U-net model was pretrained to segment the pulmonary involvement area on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation of the data and visualize it through a heatmap. Then we establish a deep learning model (model 1) and a fusion model (model 2) combined deep learning with radiomics features to predict nucleic acid turning-negative time.

RESULTS

COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time for nucleic acid turning negative (r=-0.145, P < 0.05). Importantly, our results demonstrated that model 2 achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time.

CONCLUSION

The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement of COVID-19 patients. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice.

Lung field-based severity score (LFSS): a feasible tool to identify COVID-19 patients at high risk of progressing to critical disease

Background

Coronavirus disease 2019 (COVID-19) still poses a threat to people's physical and mental health. We proposed a new semi-quantitative visual classification method for COVID-19, and this study aimed to evaluate the clinical usefulness and feasibility of lung field-based severity score (LFSS).

Methods

This retrospective study included 794 COVID-19 patients from two hospitals in China between December 2022 and January 2023. Six lung fields on the axial computed tomography (CT) were defined. LFSS and eighteen clinical characteristics were evaluated. LFSS was based on summing up the parenchymal opacification involving each lung field, which was scored as 0 (0%), 1 (1-24%), 2 (25-49%), 3 (50-74%), or 4 (75-100%), respectively (range of LFSS from 0 to 24). Total pneumonia burden (TPB) was calculated using the U-net model. The correlation between LFSS and TPB was analyzed. After performing logistic regression analysis, an LFSS-based model, clinical-based model and combined model were developed. Receiver operating characteristic curves were used to evaluate and compare the performance of three models.

Results

LFSS, age, chronic liver disease, chronic kidney disease, white blood cell, neutrophils, lymphocytes and C-reactive protein differed significantly between the non-critical and critical group (all P<0.05). There was a strong positive correlation of LFSS and TPB (Pearson correlation coefficient =0.767, P<0.001). The area under curves of LFSS-based model, clinical-based model and combined model were 0.799 [95% confidence interval (CI): 0.770-0.827], 0.758 (95% CI: 0.727-0.788), and 0.848 (95% CI: 0.821-0.872), respectively.

Conclusions

The LFSS derived from chest CT may be a potential new tool to help identify COVID-19 patients at high risk of progressing to critical disease.

Early predictors of intensive care unit admission among COVID-19 patients in Qatar

Background

COVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19.

Methods

This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender.

Results

A total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001).

Conclusion

Having cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.

Artificial intelligence-based analysis of the spatial distribution of abnormal computed tomography patterns in SARS-CoV-2 pneumonia: association with disease severity

BACKGROUND

The substantial heterogeneity of clinical presentations in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia still requires robust chest computed tomography analysis to identify high-risk patients. While extension of ground-glass opacity and consolidation from peripheral to central lung fields on chest computed tomography (CT) might be associated with severely ill conditions, quantification of the central-peripheral distribution of ground glass opacity and consolidation in assessments of SARS-CoV-2 pneumonia remains unestablished. This study aimed to examine whether the central-peripheral distributions of ground glass opacity and consolidation were associated with severe outcomes in patients with SARS-CoV-2 pneumonia independent of the whole-lung extents of these abnormal shadows.

METHODS

This multicenter retrospective cohort included hospitalized patients with SARS-CoV-2 pneumonia between January 2020 and August 2021. An artificial intelligence-based image analysis technology was used to segment abnormal shadows, including ground glass opacity and consolidation. The area ratio of ground glass opacity and consolidation to the whole lung (GGO%, CON%) and the ratio of ground glass opacity and consolidation areas in the central lungs to those in the peripheral lungs (GGO(C/P)) and (CON(C/P)) were automatically calculated. Severe outcome was defined as in-hospital death or requirement for endotracheal intubation.

RESULTS

Of 512 enrolled patients, the severe outcome was observed in 77 patients. GGO% and CON% were higher in patients with severe outcomes than in those without. Multivariable logistic models showed that GGO(C/P), but not CON(C/P), was associated with the severe outcome independent of age, sex, comorbidities, GGO%, and CON%.

CONCLUSION

In addition to GGO% and CON% in the whole lung, the higher the ratio of ground glass opacity in the central regions to that in the peripheral regions was, the more severe the outcomes in patients with SARS-CoV-2 pneumonia were. The proposed method might be useful to reproducibly quantify the extension of ground glass opacity from peripheral to central lungs and to estimate prognosis.

Inflammation associated with lung function abnormalities in COVID-19 survivors

BACKGROUND

Activation of inflammatory pathways promotes organ dysfunction in COVID-19. Currently, there are reports describing lung function abnormalities in COVID-19 survivors; however, the biological mechanisms remain unknown. The aim of this study was to analyze the association between serum biomarkers collected during and following hospitalization and pulmonary function in COVID-19 survivors.

METHODS

Patients recovering from severe COVID-19 were prospectively evaluated. Serum biomarkers were analyzed from admission to hospital, peak during hospitalization, and at the time of discharge. Pulmonary function was measured approximately 6 weeks after discharge.

RESULTS

100 patients (63% male) were included (age 48 years, SD ± 14) with 85% having at least one comorbidity. Patients with a restrictive spirometry pattern (n = 46) had greater inflammatory biomarkers compared to those with normal spirometry (n = 54) including peak Neutrophil-to-Lymphocyte ratio (NLR) value [9.3 (10.1) vs. 6.5 (6.6), median (IQR), p = 0.027] and NLR at hospital discharge [4.6 (2.9) vs. 3.2 (2.9) p = 0.005] and baseline C-reactive protein value [164.0 (147.0) vs. 106.5 (139.0) mg/dL, p = 0.083). Patients with an abnormal diffusing capacity (n = 35) had increased peak NLR [8.9 (5.9) vs. 5.6 (5.7) mg/L, p = 0.029]; baseline NLR [10.0 (19.0) vs. 4.0 (3.0) pg/ml, p = 0.002] and peak Troponin-T [10.0 (20.0) vs. 5.0 (5.0) pg/ml, p = 0.011] compared to patients with normal diffusing capacity (n = 42). Multivariable linear regression analysis identified predictors of restrictive spirometry and low diffusing capacity, but only accounted for a low degree of variance in pulmonary function outcome.

CONCLUSION

Overexpression of inflammatory biomarkers is associated with subsequent lung function abnormalities in patients recovered from severe COVID-19.

Contribution of genotypes in Prothrombin and Factor V Leiden to COVID-19 and disease severity in patients at high risk for hereditary thrombophilia

Thrombotic and microangiopathic effects have been reported in COVID-19 patients. This study examined the contribution of the hereditary thrombophilia factors Prothrombin (FII) and Factor V Leiden (FVL) genotypes to the severity of COVID-19 disease and the development of thrombosis. This study investigated FII and FVL alleles in a cohort of 9508 patients (2606 male and 6902 female) with thrombophilia. It was observed that 930 of these patients had been infected by SARS-CoV-2 causing COVID-19. The demographic characteristics of the patients and their COVID-19 medical history were recorded. Detailed clinical manifestations were analyzed in a group of cases (n = 4092). This subgroup was age and gender-matched. FII and FVL frequency data of healthy populations without thrombophilia risk were obtained from Bursa Uludag University Medical Genetic Department's Exome Databank. The ratio of males (31.08%; 27.01%) and the mean age (36.85 ± 15.20; 33.89 ± 14.14) were higher among COVID-19 patients compared to non-COVID-19 patients. The prevalence of FVL and computerized tomography (CT) positivity in COVID-19 patients was statistically significant in the thrombotic subgroup (p < 0.05). FVL prevalence, CT positivity rate, history of thrombosis, and pulmonary thromboembolism complication were found to be higher in deceased COVID-19 patients (p < 0.05). Disease severity was mainly affected by FVL and not related to genotypes at the Prothrombin mutations. Overall, disease severity and development of thrombosis in COVID-19 are mainly affected by the variation within the FVL gene. Possible FVL mutation should be investigated in COVID-19 patients and appropriate treatment should be started earlier in FVL-positive patients.

Correlation between the initial CT chest findings and short-term prognosis in Egyptian patients with COVID-19 pneumonia

Background

The recent pandemic of COVID‐19 has thrown the world into chaos due to its high rate of transmissions. This study aimed to highlight the encountered CT findings in 910 patients with COVID-19 pneumonia in Egypt including the mean severity score and also correlation between the initial CT finding and the short-term prognosis in 320 patients.

Results

All patients had confirmed COVID-19 infection. Non-contrast CT chest was performed for all cases; in addition, the correlation between each CT finding and disease severity or the short-term prognosis was reported. The mean age was higher for patients with unfavorable prognosis (P < 0.01). The patchy pattern was the most common, found in 532/910 patients (58.4%), the nodular pattern was the least common 123/910 (13.5%). The diffuse pattern was reported in 124 (13.6%). The ground glass density was the most common reported density in the study 512/910 (56.2%). The crazy pavement sign was reported more frequently in patients required hospitalization or ICU and was reported in 53 (56.9%) of patients required hospitalization and in 29 (40.2%) patients needed ICU, and it was reported in 11 (39.2%) deceased patients. Air bronchogram was reported more frequently in patients with poor prognosis than patients with good prognosis (16/100; 26% Vs 12/220; 5.4%). The mean CT severity score for patients with poor prognosis was 15.2. The mean CT severity score for patients with good prognosis 8.7., with statistically significant difference (P = 0.001).

Conclusion

Our results confirm the important role of the initial CT findings in the prediction of clinical outcome and short-term prognosis. Some signs like subpleural lines, halo sign, reversed halo sign and nodular shape of the lesions predict mild disease and favorable prognosis. The crazy paving sign, dense vessel sign, consolidation, diffuse shape and high severity score predict more severe disease and probably warrant early hospitalization. The high severity score is most important in prediction of unfavorable prognosis. The nodular shape of the lesions is the most important predictor of good prognosis.

An Entropy-Based Measure of Complexity: An Application in Lung-Damage

The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.

The Role of Neutrophil-to-Lymphocyte Ratio in Risk Stratification and Prognostication of COVID-19: A Systematic Review and Meta-Analysis

Several studies have proposed that the neutrophil−lymphocyte ratio (NLR) is one of the various biomarkers that can be useful in assessing COVID-19 disease-related outcomes. Our systematic review analyzes the relationship between on-admission NLR values and COVID-19 severity and mortality. Six different severity criteria were used. A search of the literature in various databases was conducted from 1 January 2020 to 1 May 2021. We calculated the pooled standardized mean difference (SMD) for the collected NLR values. A meta-regression analysis was performed, looking at the length of hospitalization and other probable confounders, such as age, gender, and comorbidities. A total of sixty-four studies were considered, which included a total of 15,683 patients. The meta-analysis showed an SMD of 3.12 (95% CI: 2.64−3.59) in NLR values between severe and non-severe patients. A difference of 3.93 (95% CI: 2.35−5.50) was found between survivors and non-survivors of the disease. Upon summary receiver operating characteristics analysis, NLR showed 80.2% (95% CI: 74.0−85.2%) sensitivity and 75.8% (95% CI: 71.3−79.9%) specificity for the prediction of severity and 78.8% (95% CI: 73.5−83.2%) sensitivity and 73.0% (95% CI: 68.4−77.1%) specificity for mortality, and was not influenced by age, gender, or co-morbid conditions. Conclusion: On admission, NLR predicts both severity and mortality in COVID-19 patients, and an NLR > 6.5 is associated with significantly greater the odds of mortality.

The Impact of Neutrophil-Lymphocyte Count Ratio in COVID-19: A Systematic Review and Meta-Analysis

Background: The neutrophil-lymphocyte count ratio (NLR) has emerged as a potential prognostic tool for different diseases. In the current coronavirus disease (COVID-19) pandemic, the NLR may be a useful tool for risk scarification and the optimal utilization of limited healthcare resources. However, there is no consensus regarding the optimal value of NLR, and the association with disease severity and mortality. Thus, this study aims to systematically analyze the current evidence of the utility of baseline NLR as a predictive tool for mortality, disease severity in COVID-19 patients. Methods: A compendious screening of electronic databases up to June 15, 2021, was done after enlisting the protocol in PROSPERO (CRD42020202659). Studies evaluating the utility of baseline NLR in COVID-19 are included for this review as per the PRISMA statement. Results: We retrieved a total of 13112 and 12986 COVID-19 patients for survivability and severity over 90 studies. The expired and critically sick patients had elevated baseline NLR on admission, in comparison to survivors and noncritical patients. (SMD = 3.82; 95% CI: 2.79-4.85; I2 = 100% and SMD = 1.42; 95% CI: 1.22-1.63; I2 = 95%, respectively). The summary receiver operating curve analysis for mortality (AUC = 0.87; 95% CI: 0.86-0.87; I2 = 94.7%), and severity (AUC = 0.82; 95% CI: 0.80-0.84; I2 = 79.7%) were also suggestive of its significant predictive value. Conclusions: The elevated NLR on admission in COVID-19 patients is associated with poor outcomes.

Novel COVID-19 Diagnosis Delivery App Using Computed Tomography Images Analyzed with Saliency-Preprocessing and Deep Learning

This app project was aimed to remotely deliver diagnoses and disease-progression information to COVID-19 patients to help minimize risk during this and future pandemics. Data collected from chest computed tomography (CT) scans of COVID-19-infected patients were shared through the app. In this article, we focused on image preprocessing techniques to identify and highlight areas with ground glass opacity (GGO) and pulmonary infiltrates (PIs) in CT image sequences of COVID-19 cases. Convolutional neural networks (CNNs) were used to classify the disease progression of pneumonia. Each GGO and PI pattern was highlighted with saliency map fusion, and the resulting map was used to train and test a CNN classification scheme with three classes. In addition to patients, this information was shared between the respiratory triage/radiologist and the COVID-19 multidisciplinary teams with the application so that the severity of the disease could be understood through CT and medical diagnosis. The three-class, disease-level COVID-19 classification results exhibited a macro-precision of more than 94.89% in a two-fold cross-validation. Both the segmentation and classification results were comparable to those made by a medical specialist.

Tocilizumab in severe COVID-19 - A randomized, double-blind, placebo-controlled trial

Background

The therapeutic effectiveness of interleukin-6 receptor inhibitor in critically ill hospitalized patients with coronavirus disease 2019 (COVID-19) is uncertain.

Methods

To evaluate the efficacy and safety of the outcome as recovery or death of tocilizumab for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection, we conducted a randomized, double-blinded, placebo-controlled phase 2 trial in critically ill COVID-19 adult patients. The patients were randomly assigned in a 4:1 ratio to receive standard medical treatment plus the recommended dose of either tocilizumab or the placebo drug. Randomization was stratified. The primary outcome was the recovery or death after administration of tocilizumab or a placebo drug. The secondary outcomes were clinical recovery or worsening of the patients' symptoms and inflammatory markers and discharge from the hospital.

Results

Of 190 patients included in this study, 152 received tocilizumab, and 38 received a placebo. The duration of hospital stay of the interventional group was 12.9 ± 9.2, while the placebo group had a more extended hospital stay (15.6 ± 8.8). The mortality ratio for the primary outcome, ie, mortality or recovery in the tocilizumab group was 17.8%; p = 0.58 by log-rank test). The mortality ratio in the placebo group was 76.3%; p = 0.32 by log-rank test). The inflammatory markers in the tocilizumab group significantly declined by day 16 compared to the placebo group.

Conclusions

The use of tocilizumab was associated with decreased mortality, earlier improvement of inflammatory markers, and reduced hospital stay in patients with severe COVID-19.

Prognostic factors of worse outcome for hospitalized COVID-19 patients, with emphasis on chest computed tomography data: a retrospective study

OBJECTIVE

To evaluate anthropometric and clinical data, muscle mass, subcutaneous fat, spine bone mineral density, extent of acute pulmonary disease related to COVID-19, quantification of pulmonary emphysema, coronary calcium, and hepatic steatosis using chest computed tomography of hospitalized patients with confirmed diagnosis of COVID-19 pneumonia and verify its association with disease severity.

METHODS

A total of 123 adults hospitalized due to COVID-19 pneumonia were enrolled in the present study, which evaluated the anthropometric, clinical and chest computed tomography data (pectoral and paravertebral muscle area and density, subcutaneous fat, thoracic vertebral bodies density, degree of pulmonary involvement by disease, coronary calcium quantification, liver attenuation measurement) and their association with poorer prognosis characterized through a combined outcome of intubation and mechanical ventilation, need of intensive care unit, and death.

RESULTS

Age (p=0.013), body mass index (p=0.009), lymphopenia (p=0.034), and degree of pulmonary involvement of COVID-19 pneumonia (p<0.001) were associated with poor prognosis. Extent of pulmonary involvement by COVID-19 pneumonia had an odds ratio of 1,329 for a poor prognosis and a cutoff value of 6.5 for increased risk, with a sensitivity of 64.9% and specificity of 67.1%.

CONCLUSION

The present study found an association of high body mass index, older age, extent of pulmonary involvement by COVID-19, and lymphopenia with severity of COVID-19 pneumonia in hospitalized patients.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

Chronic diseases, chest computed tomography, and laboratory tests as predictors of severe respiratory failure and death in elderly Brazilian patients hospitalized with COVID-19: a prospective cohort study

BACKGROUND

The primary risk factors for severe respiratory failure and death in the elderly hospitalized with COVID-19 remain unclear.

OBJECTIVE

To determine the association of chronic diseases, chest computed tomography (CT), and laboratory tests with severe respiratory failure and mortality in older adults hospitalized with COVID-19.

METHOD

This was a prospective cohort with 201 hospitalized older adults with COVID-19. Chronic diseases, chest CT, laboratory tests, and other data were collected within the first 48 h of hospitalization. Outcomes were progression to severe respiratory failure with the need of mechanical ventilation (SRF/MV) and death.

RESULTS

The mean age was 72.7 ± 9.2 years, and 63.2% were men. SRF/MV occurred in 16.9% (p < 0.001), and death occurred in 8%. In the adjusted regression analyses, lung involvement over 50% [odds ratio (OR): 3.09 (1.03-9.28; 0.043)], C-reactive protein (CRP) > 80 ng/mL [OR: 2.97 (0.99-8.93; 0.052)], Vitamin D < 40 ng/mL [OR: 6.41 (1.21-33.88; 0.029)], and hemoglobin < 12 g/mL [OR: 3.32 (1.20-9.20; 0.020)] were independent predictors for SFR/MV, while chronic atrial fibrillation [OR: 26.72 (3.87-184.11; 0.001)], cancer history [OR:8.32 (1.28-53.91; 0.026)] and IL-6 > 40 pg/mL [OR:10.01 (1.66-60.13; 0.012)] were independent predictors of death.

CONCLUSION

In hospitalized older adults with COVID-19, tomographic pulmonary involvement > 50%, anemia, vitamin D below 40 ng/mL, and CRP above 80 mg/L were independent risk factors for progression to SRF/MV. The presence of chronic atrial fibrillation, previous cancer, IL-6 > 40 pg/mL, and anemia were independent predictors of death.

Predictive Value of Systemic Immune-Inflammation index and Neutrophil-to-Lymphocyte Ratio in Patients with Severe COVID-19

Objective: It was initially reported that a novel coronavirus (COVID-19) had been identified in Wuhan, China, in December 2019.To date, COVID-19 is still threatening all humanity and has affected the public healthcare system and the world economic situation. Neutrophil-to-lymphocyte ratio (NLR) has also been demonstrated that associated with severity of COVID-19, but little is known about systemic immune-inflammation index (SII) relation with COVID-19. Methods: One hundred and twenty-five patients with diagnosed COVID-19 including non-severe cases (n = 77) and severe cases (n = 48) were enrolled in this study. Each patient of clinical characteristic information, blood routine parameters, and the haemogram-derived ratios were collected, calculated, and retrospectively analyzed. Receiver operating characteristics (ROC) was performed to investigate whether these parameters could be used to the predictive value of patients with severe COVID-19. Results: White blood cell count (WBC), neutrophil count (NEU), red cell volume distribution width (RDW), NLR, Platelet to lymphocyte ratio (PLR), neutrophil-to-platelet ratio (NPR), and SII were significantly higher in the severe groups than in the non-severe group (p < 0.01).Conversely, the severe group had a markedly decreased lymphocyte count, basophil (Baso#) count, red blood cell count (RBC), Hemoglobin (HGB), hematocrit (HCT), and lymphocyte-to-monocyte ratio (LMR) (P < 0.01).ROC curve analysis showed the AUC, optimal cut-off value, sensitivity, specificity of NLR and SII to early predict severe-patients with COVID-19 were 0.867, 7.25, 70.83%, 92.21% and 0.860, 887.20, 81.25%, 81.82%, respectively. Conclusion The results suggest that the SII and NLR is a potential new diagnosed biomarker in severe-patients with COVID-19.

Relationship between chest CT scan findings with SOFA score, CRP, comorbidity, and mortality in ICU patients with COVID-19

OBJECTIVE

This study aimed to investigate the relationship between chest computed tomography (CT) scan findings with sequential organ failure assessment (SOFA) score, C-reactive protein (CRP), comorbidity, and mortality in intensive care unit (ICU) patients with coronavirus disease 19 (COVID-19).

METHOD

Adult patients (≥18 years old) with COVID-19 who were consecutively admitted to the Imam-Reza Hospital, Tabriz, East-Azerbaijan Province, North-West of Iran between March 2020 and August 2020 were screened and total of 168 patients were included. Demographic, clinical, and mortality data were gathered. Severity of disease was evaluated using the SOFA score system. CRP levels were measured and chest CT scans were performed.

RESULTS

Most of patients had multifocal and bilateral ground glass opacity (GGO) pattern in chest CT scan. There were significant correlations between SOFA score on admission with multifocal and bilateral GGO (P = .010 and P = .011, respectively). Significant relationships were observed between unilateral and bilateral GGO patterns with CRP (P = .049 and P = .046, respectively). There was significant relationship between GGO patterns with comorbidities including overweight/obesity, heart failure, cardiovascular diseases, and malignancy (P < .05). No significant relationships were observed between chest CT scan results with mortality (P > .05).

CONCLUSION

Multifocal bilateral GGO was the most common pattern. Although chest CT scan characteristics were significantly related with SOFA score, CRP, and comorbidity in ICU patients with COVID-19, a relationship with mortality was not significant.

The epidemiology, pathophysiological mechanisms, and management toward COVID-19 patients with Type 2 diabetes: A systematic review

This review comprehensively summarizes epidemiologic evidence of COVID-19 in patients with Type 2 diabetes, explores pathophysiological mechanisms, and integrates recommendations and guidelines for patient management. We found that diabetes was a risk factor for diagnosed infection and poor prognosis of COVID-19. Patients with diabetes may be more susceptible to adverse outcomes associated with SARS-CoV-2 infection due to impaired immune function and possible upregulation of enzymes that mediate viral invasion. The chronic inflammation caused by diabetes, coupled with the acute inflammatory reaction caused by SARS-CoV-2, results in a propensity for inflammatory storm. Patients with diabetes should be aware of their increased risk for COVID-19.

Dynamic Profile of Prognostic Hematologic Indicators in Patient Under Intensive Care for COVID-19 Disease: A One-Year Study at a Tertiary Care Centre in South India

Introduction

Viral pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS COV-2) releases cytokines which result in neutrophils migration to the bloodstream and cytotoxic effect on lymphocytes. The ongoing pathology is reflected in the derangement of blood cells and the variations and calculations based on them that help in assessing the severity of the disease and prognosis.

Aim

This study aimed to compare the differences in the dynamic changes of the blood cells among survivors and non-survivors of COVID-19 disease so that cut-offs can be arrived at to aid triage at the intensive care unit (ICU) and to predict mortality. 

Material and methods

A one-year study was conducted on patients hospitalized in the ICU. The demography and laboratory values of neutrophils and lymphocytes in percentages and absolute values, and platelet count in numbers were retrieved for eight consecutive values. Neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) was calculated from absolute counts. Statistical analysis was done using the Chi-Square test and Mann-Whitney test and a P-value of <0.05 is considered significant. The comparison was done between survivors and non-survivors.

Result

Among the 3142 patients admitted for COVID-19 disease, 7.6% required ICU care of whom 65.5% survived and 35.5% succumbed to the illness. Survivors were younger and comparable between both sexes. Though both groups had an ascending trend of neutrophils, lymphocytes, NLR, and PLR, the baseline characteristics were significantly lower in those who survived on a day-to-day basis. Neutrophilia above 80%, NLR 7.96, PLR 200 predicted the need for admission in ICU. Neutrophilia of 87% and lymphopenia of 10% were associated with adverse outcomes (mortality). Mortality can be predicted when neutrophil rises above 93% or lymphocytes fall below 5.2%. An initial NLR of 7.96 and PLR of 160 as well as peak NLR of 12.29 and peak PLR 400 predict mortality.

Conclusion

Serial blood counts are essential for hospitalized patients with COVID-19 for early triaging, and to assess severity and prognosis. The NLR of 6.7 and PLR of 160 require intensive care. The dynamic increase of NLR and PLR show worsening of the disease process and NLR of 40.95 and PLR of 400 predict mortality.

CT Examinations for COVID-19: A Systematic Review of Protocols, Radiation Dose, and Numbers Needed to Diagnose and Predict

Purpose

Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test.

Materials and Methods

We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%-96%) and specificity of 37% (95% CI: 26%-50%), and applied to the early outbreak in Wuhan, New York, and Italy.

Results

From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standard-dose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2-6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710-56755) to 44840 (TPR, 38%; 95% CI: 35161-68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks.

Conclusion

Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.

Differences in Dynamics of Lung Computed Tomography Patterns between Survivors and Deceased Adult Patients with COVID-19

This study’s aim was to investigate CT (computed tomography) pattern dynamics differences within surviving and deceased adult patients with COVID-19, revealing new prognostic factors and reproducing already known data with our patients’ cohort: 635 hospitalized patients (55.3% of them were men, 44.7%—women), of which 87.3% had a positive result of RT-PCR (reverse transcription-polymerase chain reaction) at admission. The number of deaths was 53 people (69.8% of them were men and 30.2% were women). In total, more than 1500 CT examinations were performed on patients, using a GE Optima CT 660 computed tomography (General Electric Healthcare, Chicago, IL, USA). The study was performed at hospital admission, the frequency of repetitive scans further varied based on clinical need. The interpretation of the imaging data was carried out by 11 radiologists with filling in individual registration cards that take into account the scale of the lesion, the location, contours, and shape of the foci, the dominating types of changes, as well as the presence of additional findings and the dynamics of the process—a total of 45 parameters. Statistical analysis was performed using the software packages SPSS Statistics version 23.0 (IBM, Armonk, NY, USA) and R software version 3.3.2. For comparisons in pattern dynamics across hospitalization we used repeated measures general linear model with outcome and disease phase as factors. The crazy paving pattern, which is more common and has a greater contribution to the overall CT picture in different phases of the disease in deceased patients, has isolated prognostic significance and is probably a reflection of faster dynamics of the process with a long phase of progression of pulmonary parenchyma damage with an identical trend of changes in the scale of the lesion (as recovered) in this group of patients. Already known data on typical pulmonological CT manifestations of infection, frequency of occurrence, and the prognostic significance of the scale of the lesion were reproduced, new differences in the dynamics of the process between recovered and deceased adult patients were also found that may have prognostic significance and can be reflected in clinical practice.

Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review With Cumulative Meta-Analysis

Introduction: Given the ongoing coronavirus disease 2019 (COVID-19) pandemic and the consequent global healthcare crisis, there is an urgent need to better understand risk factors for symptom deterioration and mortality among patients with COVID-19. This systematic review aimed to meet the need by determining the predictive value of chronic diseases for COVID-19 severity and mortality.

Methods: We searched PubMed, Embase, Web of Science, and Cumulative Index to Nursing and Allied Health Complete to identify studies published between December 1, 2019, and December 31, 2020. Two hundred and seventeen observational studies from 26 countries involving 624,986 patients were included. We assessed the risk of bias of the included studies and performed a cumulative meta-analysis.

Results: We found that among COVID-19 patients, hypertension was a very common condition and was associated with higher severity, intensive care unit (ICU) admission, acute respiratory distress syndrome, and mortality. Chronic obstructive pulmonary disease was the strongest predictor for COVID-19 severity, admission to ICU, and mortality, while asthma was associated with a reduced risk of COVID-19 mortality. Patients with obesity were at a higher risk of experiencing severe symptoms of COVID-19 rather than mortality. Patients with cerebrovascular disease, chronic liver disease, chronic renal disease, or cancer were more likely to become severe COVID-19 cases and had a greater probability of mortality.

Conclusions: COVID-19 patients with chronic diseases were more likely to experience severe symptoms and ICU admission and faced a higher risk of mortality. Aggressive strategies to combat the COVID-19 pandemic should target patients with chronic diseases as a priority.

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

Factors Predicting Outcome in Intensive Care Unit-Admitted COVID-19 Patients: Using Clinical, Laboratory, and Radiologic Characteristics

PURPOSE

To investigate the factors contributing to mortality in coronavirus disease 2019 (COVID-19) patients admitted in the intensive care unit (ICU) and design a model to predict the mortality rate.

METHOD

We retrospectively evaluated the medical records and CT images of the ICU-admitted COVID-19 patients who had an on-admission chest CT scan. We analyzed the patients' demographic, clinical, laboratory, and radiologic findings and compared them between survivors and nonsurvivors.

RESULTS

Among the 121 enrolled patients (mean age, 62.2 ± 14.0 years; male, 82 (67.8%)), 41 (33.9%) survived, and the rest succumbed to death. The most frequent radiologic findings were ground-glass opacity (GGO) (71.9%) with peripheral (38.8%) and bilateral (98.3%) involvement, with lower lobes (94.2%) predominancy. The most common additional findings were cardiomegaly (63.6%), parenchymal band (47.9%), and crazy-paving pattern (44.4%). Univariable analysis of radiologic findings showed that cardiomegaly (p : 0.04), pleural effusion (p : 0.02), and pericardial effusion (p : 0.03) were significantly more prevalent in nonsurvivors. However, the extension of pulmonary involvement was not significantly different between the two subgroups (11.4 ± 4.1 in survivors vs. 11.9 ± 5.1 in nonsurvivors, p : 0.59). Among nonradiologic factors, advanced age (p : 0.002), lower O2 saturation (p : 0.01), diastolic blood pressure (p : 0.02), and hypertension (p : 0.03) were more commonly found in nonsurvivors. There was no significant difference between survivors and nonsurvivors in terms of laboratory findings. Three following factors remained significant in the backward logistic regression model: O2 saturation (OR: 0.91 (95% CI: 0.84-0.97), p : 0.006), pericardial effusion (6.56 (0.17-59.3), p : 0.09), and hypertension (4.11 (1.39-12.2), p : 0.01). This model had 78.7% sensitivity, 61.1% specificity, 90.0% positive predictive value, and 75.5% accuracy in predicting in-ICU mortality.

CONCLUSION

A combination of underlying diseases, vital signs, and radiologic factors might have prognostic value for mortality rate prediction in ICU-admitted COVID-19 patients.

The dynamics of inflammatory markers in coronavirus disease-2019 (COVID-19) patients: A systematic review and meta-analysis

BACKGROUND

Coronavirus disease-2019 (COVID-19) is a global pandemic and high mortality rate among severe or critical COVID-19 is linked with SARS-CoV-2 infection-induced hyperinflammation of the innate and adaptive immune systems and the resulting cytokine storm. This paper attempts to conduct a systematic review and meta-analysis of published articles, to evaluate the association of inflammatory parameters with the severity and mortality in COVID-19 patients.

METHODS

A comprehensive systematic literature search of medical electronic databases including Pubmed/Medline, Europe PMC, and Google Scholar was performed for relevant data published from January 1, 2020 to June 26, 2020. Observational studies reporting clear extractable data on inflammatory parameters in laboratory-confirmed COVID-19 patients were included. Screening of articles, data extraction and quality assessment were carried out by two authors independently. Standardized mean difference (SMD)/mean difference (MD/WMD) and 95% confidence intervals (CIs) were calculated using random or fixed-effects models.

RESULTS

A total of 83 studies were included in the meta-analysis. Of which, 54 studies were grouped by severity, 25 studies were grouped by mortality, and 04 studies were grouped by both severity and mortality. Random effect model results demonstrated that patients with severe COVID-19 group had significantly higher levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), interleukin-6 (IL-6), interleukin-10 (IL-10), interleukin-2R (IL-2R), serum amyloid A (SAA) and neutrophil-to-lymphocyte ratio (NLR) compared to those in the non-severe group. Similarly, the fixed-effect model revealed significant higher ferritin level in the severe group when compared with the non-severe group. Furthermore, the random effect model results demonstrated that the non-survivor group had significantly higher levels of CRP, PCT, IL-6, ferritin, and NLR when compared with the survivor group.

CONCLUSION

In conclusion, the measurement of these inflammatory parameters could help the physicians to rapidly identify severe COVID-19 patients, hence facilitating the early initiation of effective treatment.

PROSPERO REGISTRATION NUMBER

CRD42020193169.

Chest CT Imaging Characteristics of COVID-19 Pneumonia in Surviving and Non-surviving Hospitalized Patients: A Retrospective Study in a Referral Center in Tehran, Iran

Background: Coronavirus disease 2019 (COVID-19) has several chest computed tomography (CT) characteristics, which are important for the early management of this disease, because viral detection via RT-PCR can be time-consuming, resulting in a delayed pneumonia diagnosis. The Radiological Society of North America (RSNA) proposed a reporting language for CT findings related to COVID-19 and defined four CT categories: typical, indeterminate, atypical, and negative. Objectives: To retrospectively evaluate the chest CT characteristics of patients with COVID-19 pneumonia. Patients and Methods: A total of 115 hospitalized laboratory-verified COVID-19 cases, underdoing chest CT scan, were included in this study from April 30 to May 15, 2020. Of 115 cases, 53 were discharged from the hospital, and 62 expired. The initial clinical features and chest CT scans were assessed for the type, pattern, distribution, and frequency of lesions. Moreover, the findings were compared between ward-hospitalized, intensive care unit (ICU)-admitted, and non-surviving (expired) patients. Results: Of four CT categories, typical CT findings for COVID-19 were more frequent in the expired group (77.4%), compared to the ward-admitted (44.8%) and ICU-admitted (70.8%) groups (P = 0.017). However, no significant difference was observed in the prevalence of intermediate or atypical CT findings between the groups. Negative CT scans for the diagnosis of COVID-19 were significantly fewer in the expired group (0%) as compared to the ward-admitted (10.3%) and ICU-admitted (8.3%) groups (P = 0.0180). Also, the mean number of involved lung lobes and segments was significantly higher in the expired group compared to the other two groups (P = 0.032 and 0.010, respectively). The right upper lobe involvement, right middle lobe involvement, bilateral involvement, central lesion, air bronchogram, and pleural effusion were among CT scan findings with a significantly higher prevalence in non-surviving cases (P < 0.0001, 0.047, 0.01, 0.036, 0.038, and 0.047, respectively). Conclusion: The increased number of involved lung lobes and segments, bilateral and central distribution patterns, air bronchogram, and severe pleural effusion in the initial chest CT scan can be related to the increased severity and poor prognosis of COVID-19.

Diabetes prevalence and mortality in COVID-19 patients: a systematic review, meta-analysis, and meta-regression

BACKGROUND

The coronavirus disease 2019 (COVID-19) patients with diabetes mellitus (DM) are at high risk of fatal outcomes. This meta-analysis quantifies the prevalence of mortality among (1) diabetic and (2) non-diabetic, and (3) the prevalence of DM, in hospitalized COVID-19 patients.

METHODS

Published studies were retrieved from four electronic databases (PubMed, Embase, Scopus, and medRxiv) and appraised critically utilizing the National Heart, Lung, and Blood Institute's tool. Meta-analyses were performed using the random-effects model. The measures of heterogeneity were ascertained by I- squared (I 2 ) and Chi-squared (Chi 2 ) tests statistics. Predictors of heterogeneity were quantified using meta-regression models.

RESULTS

Of the reviewed 475 publications, 22 studies (chiefly case series (59.09 %)), sourcing data of 45,775 hospitalized COVID-19 patients, were deemed eligible. The weighted prevalence of mortality in hospitlized COVID-19 patients with DM (20.0 %, 95 % CI: 15.0-26.0; I 2 , 96.8 %) was 82 % (1.82-time) higher than that in non-DM patients (11.0 %, 95 % CI: 5.0-16.0; I 2 , 99.3 %). The prevalence of mortality among DM patients was highest in Europe (28.0 %; 95 % CI: 14.0-44.0) followed by the United States (20.0 %, 95 % CI: 11.0-32.0) and Asia (17.0 %, 95 % CI: 8.0-28.0). Sample size and severity of the COVID-19 were associated (p < 0.05) with variability in the prevalence of mortality. The weighted prevalence of DM among hospitalized COVID-19 patients was 20 % (95 % confidence interval [CI]: 15-25, I 2 , 99.3 %). Overall, the quality of the studies was fair.

CONCLUSIONS

Hospitalized COVID-19 patients were appreciably burdened with a high prevalence of DM. DM contributed to the increased risk of mortality among hospitalized COVID-19 patients compared to non-DM patients, particularly among critically ill patients. Registration: PROSPERO (registration no. CRD42020196589).

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40200-021-00779-2.

COVID-19 pneumonia: current evidence of chest imaging features, evolution and prognosis

COVID-19 pneumonia represents a global threatening disease, especially in severe cases. Chest imaging, with X-ray and high-resolution computed tomography (HRCT), plays an important role in the initial evaluation and follow-up of patients with COVID-19 pneumonia. Chest imaging can also help in assessing disease severity and in predicting patient’s outcome, either as an independent factor or in combination with clinical and laboratory features. This review highlights the current knowledge of imaging features of COVID-19 pneumonia and their temporal evolution over time, and provides recent evidences on the role of chest imaging in the prognostic assessment of the disease.

Chest computed tomography signs associated with pejorative evolution in COVID-19 patients

PURPOSE

The aim of this study was to evaluate how chest computed tomography (CT) can predict pejorative evolution in COVID-19 patients.

MATERIAL AND METHODS

Data on 349 consecutive patients who underwent a chest CT either for severe suspected COVID-19 pneumonia or clinical aggravation and with COVID-19 were retrospectively analysed. In total, 109 had laboratory-confirmed COVID-19 infection by a positive reverse-transcription polymerase chain reaction (RT-PCR) and were included. The main outcomes for pejorative evolution were death and the need for invasive endotracheal ventilation (IEV). All the CT images were retrospectively reviewed, to analyse the CT signs and semiologic patterns of pulmonary involvement.

RESULTS

Among the 109 COVID-19 patients, 73 (67%) had severe symptoms of COVID-19, 28 (25.7%) needed an IEV, and 11 (10.1%) died. The following signs were significantly associated with both mortality and need for IEV: traction bronchiectasis and total affected lung volume ≥ 50% (p < 10-3). Other CT signs were only associated with the need of IEV: vascular dilatation, air bubble sign, peribronchovascular thickening, interlobular thickening, and number of involved lobes ≥ 4 (p < 10-3).

CONCLUSIONS

On a chest CT performed during the first week of the symptoms, the presence of traction bronchiectasis and high values of affected lung volume are associated with the need for IEV, and with mortality, in COVID-19 patients.

Radiologic diagnosis of patients with COVID-19

The pandemia caused by the SARS-CoV-2 virus has triggered an unprecedented health and economic crisis. Although the diagnosis of infection with SARS-CoV-2 is microbiological, imaging techniques play an important role in supporting the diagnosis, grading the severity of disease, guiding treatment, detecting complications, and evaluating the response to treatment. The lungs are the main organ involved, and chest X-rays, whether obtained in conventional X-ray suites or with portable units, are the first-line imaging test because they are widely available and economical. Chest CT is more sensitive than plain chest X-rays, and CT studies make it possible to identify complications in addition to pulmonary involvement, as well as to suggestive alternative diagnoses. The most common radiologic findings in COVID-19 are airspace opacities (consolidations and/or ground-glass opacities), which are typically bilateral, peripheral, and located primarily in the lower fields.

Radiologic diagnosis of patients with COVID-19

The pandemia caused by the SARS-CoV-2 virus has triggered an unprecedented health and economic crisis. Although the diagnosis of infection with SARS-CoV-2 is microbiological, imaging techniques play an important role in supporting the diagnosis, grading the severity of disease, guiding treatment, detecting complications, and evaluating the response to treatment. The lungs are the main organ involved, and chest X-rays, whether obtained in conventional X-ray suites or with portable units, are the first-line imaging test because they are widely available and economical. Chest CT is more sensitive than plain chest X-rays, and CT studies make it possible to identify complications in addition to pulmonary involvement, as well as to suggestive alternative diagnoses. The most common radiologic findings in COVID-19 are airspace opacities (consolidations and/or ground-glass opacities), which are typically bilateral, peripheral, and located primarily in the lower fields.

CT changes of severe coronavirus disease 2019 based on prognosis

This study aimed to determine the characteristics of CT changes in patients with severe coronavirus disease 2019 (COVID-19) based on prognosis. Serial CT scans in 47 patients with severe COVID-19 were reviewed. The patterns, distribution and CT score of lung abnormalities were assessed. Scans were classified according to duration in weeks after onset of symptoms. These CT abnormalities were compared between discharged and dead patients. Twenty-six patients were discharged, whereas 21 passed away. Discharged patients were characterized by a rapid rise in CT score in the first 2 weeks followed by a slow decline, presence of reticular and mixed patterns from the second week, and prevalence of subpleural distribution of opacities in all weeks. In contrast, dead patients were characterized by a progressive rise in CT score, persistence of ground-glass opacity and consolidation patterns in all weeks, and prevalence of diffuse distribution from the second week. CT scores of death group were significantly higher than those of discharge group (P < 0.05). The CT changes differed between the discharged and dead patients. An understanding of these differences can be of clinical significance in the assessment of the prognosis of severe COVID-19 patients.

Chest CT imaging features of COVID-19 and its correlation with the PaO2/FiO2 ratio: a multicenter study in Upper Egypt

Background

The main challenge in managing COVID-19 pandemic is containment of the infection by early detection of the disease and wide dissemination of diagnostic tests with high sensitivity and specificity. Various imaging features were identified by chest CT with different patterns from early disease to diffuse disease with complications. However, CT cannot be performed for all patients. The arterial oxygen partial pressure/fraction of inspired oxygen (PaO₂/FiO₂) ratio is evaluated as a rapid and widely available test for the preliminary assessment of disease severity. This study aimed to evaluate the clinical and chest CT imaging features of COVID-19 in Egyptian patients as well as assess the correlation between the chest CT total severity score and the PaO₂/FiO₂ ratio to determine its value for preliminary assessment of disease severity.

Results

The most common symptoms were fever (83.2%), dry cough (77%), malaise (68.8%), prolonged headaches (48.5%), and dyspnea (37.6%). CT was positive in 79.2% of the patients. The CT features at presentation were ground-glass opacities only (40%), ground-glass opacities with consolidation (34.4%), and consolidation only (25.6%). Associated findings included crazy paving (17.5%), interlobular septal thickening (47.5%), air bronchogram (15%), bronchiectasis (12.8%), fibrous bands (8.1%), vascular enlargement within the lesion (45.6%), nodules (6.8%), pericardial thickening (5%), and pleural thickening (24.7%). The lesions were typically multilobar (50.5%), posterior (58.1%) with peripheral and central distribution (41.9%). Moderate negative correlation was observed between the CT total lung severity score and PaO₂/FiO₂ ratio with r = − 0.42 and P < 0.001.

Conclusion

The most common pattern of COVID-19 pneumonia in multiple quarantine hospitals was peripheral and central ground-glass opacities with bilateral multilobe posterior involvement and fever was the most common symptom. PaO₂/FiO₂ ratio has a moderate negative correlation with the CT total severity score and thus can be used in the preliminary assessment of disease severity.

Clinical Applications of Patient-Specific 3D Printed Models in Cardiovascular Disease: Current Status and Future Directions

Three-dimensional (3D) printing has been increasingly used in medicine with applications in many different fields ranging from orthopaedics and tumours to cardiovascular disease. Realistic 3D models can be printed with different materials to replicate anatomical structures and pathologies with high accuracy. 3D printed models generated from medical imaging data acquired with computed tomography, magnetic resonance imaging or ultrasound augment the understanding of complex anatomy and pathology, assist preoperative planning and simulate surgical or interventional procedures to achieve precision medicine for improvement of treatment outcomes, train young or junior doctors to gain their confidence in patient management and provide medical education to medical students or healthcare professionals as an effective training tool. This article provides an overview of patient-specific 3D printed models with a focus on the applications in cardiovascular disease including: 3D printed models in congenital heart disease, coronary artery disease, pulmonary embolism, aortic aneurysm and aortic dissection, and aortic valvular disease. Clinical value of the patient-specific 3D printed models in these areas is presented based on the current literature, while limitations and future research in 3D printing including bioprinting of cardiovascular disease are highlighted.

A systematic meta-analysis of immune signatures in patients with COVID-19

Currently severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) transmission has been on the rise worldwide. Predicting outcome in COVID‐19 remains challenging, and the search for more robust predictors continues. We made a systematic meta‐analysis on the current literature from 1 January 2020 to 15 August 2020 that independently evaluated 32 circulatory immunological signatures that were compared between patients with different disease severity was made. Their roles as predictors of disease severity were determined as well. A total of 149 distinct studies that evaluated ten cytokines, four antibodies, four T cells, B cells, NK cells, neutrophils, monocytes, eosinophils and basophils were included. Compared with the non‐severe patients of COVID‐19, serum levels of Interleukins (IL)‐2, IL‐2R, IL‐4, IL‐6, IL‐8, IL‐10 and tumor necrosis factor α were significantly up‐regulated in severe patients, with the largest inter‐group differences observed for IL‐6 and IL‐10. In contrast, IL‐5, IL‐1β and Interferon (IFN)‐γ did not show significant inter‐group difference. Four mediators of T cells count, including CD3⁺ T, CD4⁺ T, CD8⁺ T, CD4⁺CD25⁺CD127^‐ Treg, together with CD19⁺ B cells count and CD16⁺CD56⁺ NK cells were all consistently and significantly depressed in severe group than in non‐severe group. SARS‐CoV‐2 specific IgA and IgG antibodies were significantly higher in severe group than in non‐severe group, while IgM antibody in the severe patients was slightly lower than those in the non‐severe patients, and IgE antibody showed no significant inter‐group differences. The combination of cytokines, especially IL‐6 and IL‐10, and T cell related immune signatures can be used as robust biomarkers to predict disease severity following SARS‐CoV‐2 infection.

Contribution of CT Features in the Diagnosis of COVID-19

The outbreak of novel coronavirus disease 2019 (COVID-19) first occurred in Wuhan, Hubei Province, China, and spread across the country and worldwide quickly. It has been defined as a major global health emergency by the World Health Organization (WHO). As this is a novel virus, its diagnosis is crucial to clinical treatment and management. To date, real-time reverse transcription-polymerase chain reaction (RT-PCR) has been recognized as the diagnostic criterion for COVID-19. However, the results of RT-PCR can be complemented by the features obtained in chest computed tomography (CT). In this review, we aim to discuss the diagnosis and main CT features of patients with COVID-19 based on the results of the published literature, in order to enhance the understanding of COVID-19 and provide more detailed information regarding treatment.

Use of Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in COVID-19

BACKGROUND

As the pandemic of coronavirus disease 2019 (COVID-19) continues, prognostic markers are now being identified. The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are easily accessible values that have been known to correlate with inflammation and prognosis in several conditions. We used the available data to identify the association of NLR and PLR with the severity of COVID-19.

METHODS

A literature search using EMBASE, MEDLINE, and Google Scholar for studies reporting the use of NLR and PLR in COVID-19 published until April 28, 2020, was performed. Random effects meta-analysis was done to estimate standard mean difference (SMD) of NLR and PLR values with 95% confidence interval (CI) between severe and non-severe COVID-19 cases.

RESULTS

A total of 20 studies with 3,508 patients were included. Nineteen studies reported NLR values, while five studies reported PLR values between severe and non-severe COVID-19 patients. Higher levels of NLR (SMD: 2.80, 95% CI: 2.12 - 3.48, P < 0.00001) and PLR (SMD: 1.82, 95% CI: 1.03 - 2.61, P < 0.00001)) were seen in patients with severe disease compared to non-severe disease.

CONCLUSIONS

NLR and PLR can be used as independent prognostic markers of disease severity in COVID-19.

Correlation between arterial blood gas and CT volumetry in patients with SARS-CoV-2 in the emergency department

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The severity of SARS-CoV-2 patients is difficult to quickly assess in the ED.

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The ABG test is a quick and easy tool that can help identify more severe patients.

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CT cannot be used on all suspected SARS-CoV-2 infected patients admitted in ED.

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The CT volumetry correlates well with the values of severity reported by the ABG test.