Quantitative assessment of lung involvement on chest CT at admission: Impact on hypoxia and outcome in COVID-19 patients

BackgroundThe aim of this study was to quantify COVID-19 pneumonia features using CT performed at time of admission to emergency department in order to predict patients' hypoxia during the hospitalization and outcome.MethodsConsecutive chest CT performed in the emergency department between March 1st and April 7th 2020 for COVID-19 pneumonia were analyzed. The three features of pneumonia (GGO, semi-consolidation and consolidation) and the percentage of well-aerated lung were quantified using a HU threshold based software. ROC curves identified the optimal cut-off values of CT parameters to predict hypoxia worsening and hospital discharge. Multiple Cox proportional hazards regression was used to analyze the capability of CT quantitative features, demographic and clinical variables to predict the time to hospital discharge.ResultsSeventy-seven patients (median age 56-years-old, 51 men) with COVID-19 pneumonia at CT were enrolled. The quantitative features of COVID-19 pneumonia were not associated to age, sex and time-from-symptoms onset, whereas higher number of comorbidities was correlated to lower well-aerated parenchyma ratio (rho = −0.234, p = 0.04) and increased semi-consolidation ratio (rho = −0.303, p = 0.008).Well-aerated lung (≤57%), semi-consolidation (≥17%) and consolidation (≥9%) predicted worst hypoxemia during hospitalization, with moderate areas under curves (AUC 0.76, 0.75, 0.77, respectively). Multiple Cox regression identified younger age (p < 0.01), female sex (p < 0.001), longer time-from-symptoms onset (p = 0.049), semi-consolidation ≤17% (p < 0.01) and consolidation ≤13% (p = 0.03) as independent predictors of shorter time to hospital discharge.ConclusionQuantification of pneumonia features on admitting chest CT predicted hypoxia worsening during hospitalization and time to hospital discharge in COVID-19 patients.     

Complications in COVID-19 patients: Characteristics of pulmonary embolism

ObjectiveThe purpose of this study is to evaluate chest CT imaging features, clinical characteristics, laboratory values of COVID-19 patients who underwent CTA for suspected pulmonary embolism. We also examined whether clinical, laboratory or radiological characteristics could be associated with a higher rate of PE.Materials and methodsThis retrospective study included 84 consecutive patients with laboratory-confirmed SARS-CoV-2 who underwent CTA for suspected PE. The presence and localization of PE as well as the type and extent of pulmonary opacities on chest CT exams were examined and correlated with the information on comorbidities and laboratory values for all patients.ResultsOf the 84 patients, pulmonary embolism was discovered in 24 patients. We observed that 87% of PE was found to be in lung parenchyma affected by COVID-19 pneumonia. Compared with no-PE patients, PE patients showed an overall greater lung involvement by consolidation (p = 0.02) and GGO (p < 0.01) and a higher level of D-Dimer (p < 0,01). Moreover, the PE group showed a lower level of saturation (p = 0,01) and required more hospitalization (p < 0,01).ConclusionOur study showed a high incidence of PE in COVID-19 pneumonia. In 87% of patients, PE was found in lung parenchyma affected by COVID-19 pneumonia with a worse CT severity score and a greater number of lung lobar involvement compared with non-PE patients. CT severity, lower level of saturation, and a rise in D-dimer levels could be an indication for a CTPA.Advances in knowledgeCertain findings of non-contrast chest CT could be an indication for a CTPA.     

Hallazgos tomográficos entre sobrevivientes y no-sobrevivientes con COVID-19 y utilidad clínica de una puntuación de tomografía torácica

BackgroundMany patients with coronavirus disease 2019 (COVID-19) have been diagnosed with computed tomography (CT). A prognostic tool based on CT findings could be useful for predicting death from COVID-19.ObjectivesTo compare the chest CT findings of patients who survived COVID-19 versus those of patients who died of COVID-19 and to determine the usefulness the clinical usefulness of a CT scoring system for COVID-19.MethodsWe included 124 patients with confirmed SARS-CoV-2 infections who were hospitalized between April 1, 2020 and July 25, 2020.ResultsWhereas ground-glass opacities were the most common characteristic finding in survivors (75%), crazy paving was the most characteristic finding in non-survivors (65%). Atypical findings were present in 46% of patients. The chest CT score was directly proportional to mortality; a score ≥ 18 was the best cutoff for predicting death, yielding 70% sensitivity (95%CI: 47%-87%).ConclusionsOur results suggest that atypical lesions are more prevalent in this cohort. The chest CT score had high sensitivity for predicting hospital mortality     

Coronary and total thoracic calcium scores predict mortality and provides pathophysiologic insights in COVID-19 patients

BackgroundCoronavirus disease 2019 (COVID-19) has spread worldwide determining dramatic impacts on healthcare systems. Early identification of high-risk parameters is required in order to provide the best therapeutic approach. Coronary, thoracic aorta and aortic valve calcium can be measured from a non-gated chest computer tomography (CT) and are validated predictors of cardiovascular events and all-cause mortality. However, their prognostic role in acute systemic inflammatory diseases, such as COVID-19, has not been investigated.ObjectivesThe aim was to evaluate the association of coronary artery calcium and total thoracic calcium on in-hospital mortality in COVID-19 patients.Methods1093 consecutive patients from 16 Italian hospitals with a positive swab for COVID-19 and an admission chest CT for pneumonia severity assessment were included. At CT, coronary, aortic valve and thoracic aorta calcium were qualitatively and quantitatively evaluated separately and combined together (total thoracic calcium) by a central Core-lab blinded to patients’ outcomes.ResultsNon-survivors compared to survivors had higher coronary artery [Agatston (467.76 ​± ​570.92 vs 206.80 ​± ​424.13 ​mm², p ​< ​0.001); Volume (487.79 ​± ​565.34 vs 207.77 ​± ​406.81, p ​< ​0.001)], aortic valve [Volume (322.45 ​± ​390.90 vs 98.27 ​± ​250.74 mm², p ​< ​0.001; Agatston 337.38 ​± ​414.97 vs 111.70 ​± ​282.15, p ​< ​0.001)] and thoracic aorta [Volume (3786.71 ​± ​4225.57 vs 1487.63 ​± ​2973.19 mm², p ​< ​0.001); Agatston (4688.82 ​± ​5363.72 vs 1834.90 ​± ​3761.25, p ​< ​0.001)] calcium values. Coronary artery calcium (HR 1.308; 95% CI, 1.046–1.637, p ​= ​0.019) and total thoracic calcium (HR 1.975; 95% CI, 1.200–3.251, p ​= ​0.007) resulted to be independent predictors of in-hospital mortality.ConclusionCoronary, aortic valve and thoracic aortic calcium assessment on admission non-gated CT permits to stratify the COVID-19 patients in-hospital mortality risk.     

Does COVID-19 cause an increase in spleen dimensions? Possible effects of immune activation,hematopoietic suppression and microthrombosis

PurposeTo radiologically examine how the spleen size, which has important functions in hematological and immunological balance, is affected in COVID-19.MethodsBetween July 1 and August 31, 2020, consecutive patients diagnosed with COVID-19 were analyzed. Among these patients, those who underwent chest computed tomography (CT) examination at the time of presentation, patients with follow-up CT due to clinical deterioration were included in the study. The CTs of the patients were evaluated in terms of spleen size and volume.ResultsA total of 160 patients (88 females, 55%) were included in the study. The mean time between the initial and follow-up CT was 7.2 ± 2.8 days. The splenic volume (244.3 ± 136.7 vs. 303.5 ± 156.3 cm³) and splenic index (421.2 ± 235.5 vs. 523.2 ± 269.4 cm³) values were significantly higher in the follow-up CT compared to the initial CT (p < 0.001). The increase in the splenic volume and splenic index values was 59.2 ± 52.4 cm³ and 101.9 ± 90.3 cm³ (p < 0.001), respectively. The COVID-19 severity score was significantly higher in the follow-up CT compared to the initial CT (3.7 ± 4.2 vs. 12.5 ± 5.7, respectively; p < 0.001). The spleen width measured separately on the initial and follow-up CTs showed a highest positive correlation (r = 0.982, p < 0.001).ConclusionOur study indicates that spleen size increases slightly-moderately in the first stages of the infection, and this increase is correlated with the COVID-19 severity score calculated on the chest CT data, and in this respect, it is similar to infections presenting with cytokine storm.     

Coronary CT Calcium Score in Patients With Prior Nongated CT,Is it Necessary?

PurposeTo evaluate the percentage of patients undergoing gated coronary artery calcium score CTs that had a prior nongated chest CT. To assess the accuracy of prior nongated chest CTs in the detection of coronary calcium.BackgroundCardiovascular disease is the most common cause of death worldwide. Quantifying coronary artery calcification on gated calcium score CT has proven to be strongly predictive of adverse coronary artery disease events. However, visual estimation and ordinal scoring on nongated chest CTs is predictive of coronary calcium burden.MethodsConsecutive gated calcium score CTs at a single institution from 10/2014 to 10/2016 were retrospectively evaluated with IRB approval/waiver of informed consent. The presence or absence of coronary calcium and ordinal score on nongated chest CT was compared to Agatston score on gated calcium score CT.ResultsForty-two of 441 patients (9.5%) with a gated calcium score had a prior nongated chest CT, with a mean time difference of 810 days. Of the 42 prior chest CTs, 69% had coronary artery calcium (CAC) and 31% did not, with 100% predictive accuracy for the presence or absence of CAC on subsequent gated calcium score CTs. There was 86% correlation of Agatston score on gated calcium score CT with ordinal score on the prior chest CT. Ordinal score divided into independent groups of severity was related to increased severity of Agatston score on the gated calcium score CT (P< 0.001). A majority of prior chest CT studies with coronary calcium failed to include this information in the final report.ConclusionsA large percentage of gated calcium score CTs were performed despite a prior chest CT. The ordinal score on chest CTs correlated with Agatston score on gated calcium score CTs. The presence of CAC on chest CTs was underreported in a majority of cases.     

Structured reporting of chest CT provides high sensitivity and specificity for early diagnosis of COVID-19 in a clinical routine setting

Objectives:Although chest CT has been widely used in patients with COVID-19, its role for early diagnosis of COVID-19 is unclear. We report the diagnostic performance of chest CT using structured reporting in a routine clinical setting during the early phase of the epidemic in Germany.Methods:Patients with clinical suspicion of COVID-19 and moderate-to-severe symptoms were included in this retrospective study. CTs were performed and reported before RT-PCR results (reference standard) became available. A structured reporting system was used that concluded in a recently described five-grade score (“CO-RADS”), indicating the level of suspicion for pulmonary involvement of COVID-19 from 1 = very low to 5 = very high. Structured reporting was performed by three Radiologists in consensus.Results:In 96 consecutive patients (50 male, mean age 64), RT-PCR was positive in 20 (21%) cases. CT features significantly more common in RT-PCR-positive patients were ground-glass opacities as dominant feature, crazy paving, hazy margins of opacities, and multifocal bilateral distribution (p < 0.05). Using a cut-off point between CO-RADS 3 and 4, sensitivity was 90%, specificity 91%, positive predictive value 72%, negative predictive value 97%, and accuracy 91%. ROC analysis showed an AUC of 0.938.Conclusions:Structured reporting of chest CT with a five-grade scale provided accurate diagnosis of COVID-19. Its use was feasible and helpful in clinical routine.Advances in knowledge:Chest CT with structured reporting may be a provisional diagnostic alternative to RT-PCR testing for early diagnosis of COVID-19, especially when RT-PCR results are delayed or test capacities are limited.     

Chest CT features and progression of patients with coronavirus disease 2019

Objectives:Coronavirus disease 2019 (COVID-19) is a major public health emergency. It poses a grave threat to human life and health. The purpose of the study is to investigate the chest CT findings and progression of the disease observed in COVID-19 patients.Methods:Forty-nine confirmed cases of adult COVID-19 patients with common type, severe and critically severe type were included in this retrospective single-center study. The thin-section chest CT features and progress of the disease were evaluated. The clinical and chest imaging findings of COVID-19 patients with different severity types were compared. The CT severity score and MuLBSTA score (a prediction of mortality risk) were calculated in those patients.Results:Among the 49 patients, 35 patients (71%) were common type and 14 patients (28%) were severe and critically severe type. Nearly all patients (98%) had pure ground-glass opacities (GGO) in CT imaging. Of the severe and critically severe type patients, 86% exhibited GGO with consolidation, in comparison with 54% of the patients with common type. Fibrosis presented in 79% of the severe and critically severe type patients and 43% of the common type patients. The severe and critically severe type patients were significantly more prone to experience five-lobe involvement compared to the common type patients (p = 0.002). The severe and critically severe type patients also had higher CT severity and MuLBSTA scores than the common type patients (5.43 ± 2.38 vs 3.37 ± 2.40, p < 0.001;and 10.21 ± 3.83 vs 4.63 ± 3.43, p < 0.001, respectively). MuLBSTA score was positively correlated with admittance to the intensive care unit (p = 0.005, r = 0.351). Nineteen patients underwent three times CT scan. The interval between first and second CT scan was 4[4,8] days, second and third was 3[2,4] days. There were greater improvements in the third CT follow-up findings compared to the second (p = 0.002).Conclusions:The severe and critically severe type patients often experienced more severe lung lesions, including GGO with consolidation. The CT severity score and MuLBSTA score may be helpful for the assessment of COVID-19 severity and progression.Advances in knowledge:Chest CT has the value of evaluated radiographical features of COVID-19 and allow for dynamic observation of the disease progression. Considering coagulation disorder of COVID-19, MuLBSTA score may need to be updated to increase new understanding of COVID-19.     

Lung perfusion changes in COVID-19 pneumonia: a dual energy computerized tomography study

Objective:There is limited and contradictory information about pulmonary perfusion changes detected with dual energy computed tomography (DECT) in COVID-19 cases. The purpose of this study was to define lung perfusion changes in COVID-19 cases with DECT, as well as to reveal any possible links between perfusion changes and laboratory findings.Methods:Patients who had a positive RT-PCR for SARS-CoV-2 and a contrast-enhanced chest DECT examination were included in the study. The pattern and severity of perfusion deficits were evaluated, as well as the relationships between perfusion deficit severity and laboratory results and CT severity ratings. The paired t-test, Wilcoxon test, and Student’s t-test were used to examine the changes in variables and perfusion deficits. p < 0.05 was regarded as statistically significant.Results:Study population consisted of 40 patients. Mean age was 60.73 ± 14.73 years. All of the patients had perfusion deficits at DECT images. Mean perfusion deficit severity score of the population was 8.45 ± 4.66 (min.-max, 1–19). In 24 patients (60%), perfusion deficits and parenchymal lesions matched completely. In 15 patients (37.5%), there was partial match. D dimer, CRP levels, CT severity score, and perfusion deficit severity score all had a positive correlationConclusions:Perfusion deficits are seen not only in opacification areas but also in parenchyma of normal appearance. The CT severity score, CRP, D-dimer, and SpO2 levels of the patients were determined to be related with perfusion deficit severity.Advances in knowledge:Findings of the current study may confirm the presence of micro-thrombosis in COVID-19 pneumonia.     

Utilidad de la radiografía de tórax para evaluar el pronóstico de pacientes con COVID-19

Background and aimsThe pandemia caused by SARS-CoV-2 (COVID-19) has been a diagnostic challenge in which chest X-rays have had a key role. This study aimed to determine whether the Radiological Scale for Evaluating Hospital Admission (RSEHA) applied to chest X-rays of patients with COVID-19 when they present at the emergency department is related with the severity of COVID-19 in terms of the need for admission to the hospital, the need for admission to the intensive care unit (ICU), and/or mortality.Material and methodsThis retrospective study included 292 patients with COVID-19 who presented at the emergency department between March 16, 2020 and April 30, 2020. To standardize the radiologic patterns, we used the RSEHA, categorizing the radiologic pattern as mild, moderate, or severe. We analyzed the relationship between radiologic severity according to the RSEHA with the need for admission to the hospital, admission to the ICU, and mortality.ResultsHospital admission was necessary in 91.4% of the patients. The RSEHA was significantly associated with the need for hospital admission (p = 0.03) and with the need for ICU admission (p< 0.001). A total of 51 (17.5%) patients died; of these, 57% had the severe pattern on the RSEHA. When we analyzed mortality by grouping patients according to their results on the RSEHA and their age range, the percentage of patients who died increased after age 70 years in patients classified as moderate or severe on the RSEHA.ConclusionsChest X-rays in patients with COVID-19 obtained in the emergency department are useful for determining the prognosis in terms of admission to the hospital, admission to the ICU, and mortality; radiologic patterns categorized as severe on the RSEHA are associated with greater mortality and admission to the ICU.     

COVID-19 follow-up: Chest X-ray findings with clinical and radiological relationship three months after recovery

IntroductionTo evaluate the radiological sequelae of coronavirus disease (COVID-19) in a mid-term follow-up and investigate their relationship with clinical-radiological findings.MethodsThis prospective study included COVID-19 patients who underwent a CXR three months after discharge. The relationship between CXR score at three months after discharge and clinical findings and previous CXR scores, at admission and before the discharge, were evaluated. Then, based on mid-term follow-up CXR score, patients were divided in Group A (score = 0) and Group B (score≥1), and clinical-radiological findings were compared between two Groups. Finally, we calculated the CXR scores at admission and before the discharge with the highest sensitivity and specificity to predict normal and abnormal CXR score at mid-term follow-up.ResultsThe study included 119 patients, mean age 65.9 ± 14.6 years. The oxygen saturation (Sa_O2) (p = 0.0006), the days of hospitalization (p < 0.0001) and the CXR score before the discharge (p = 0.0091) were independent factors to predict the mid-term follow-up CXR score. The Group A, 59 (49.6%) patients, had CXR scores at admission and before the discharge lower than Group B. The CXR scores at admission and before the discharge with the highest sensitivity and specificity to predict normal and abnormal CXR score at mid-term follow-up were, respectively, 3 and 2 (p < 0.0001).ConclusionsThe radiological abnormalities were present in about half patients three months after discharge, which had higher age, previous CXR scores and longer hospitalization. The S_O2, days of hospitalization and previous CXR scores were independent factors for predicting the CXR at three months.Implications for practiceThe radiologist with CXR could play a central role in mid to long-term follow-up of COVID-19, assessing the radiological sequelae of patients and identifying those who might require a closer follow-up.     

Role of complementary Ct chest in patients presenting with acute abdominal symptoms during covid-19 pandemic: a UK experience

BackgroundIn March 2020, the UK Intercollegiate General Surgery Guidance on COVID-19 recommended that patients undergoing emergency abdominal CT should have a complementary CT chest for COVID-19 screening.PurposeTo establish if complementary CT chest was performed as recommended, and if CT chest influenced surgical intervention decision. To assess detection rate of COVID-19 on CT and its correlation with RT-PCR swab results. To determine if COVID-19 changes is reliably detected within the lung bases which are usually imaged in standard abdominal CT.MethodsPatients with acute abdominal symptoms presenting to a single institution between 1st and 30th April 2020 who had abdominal CT and complementary CT chest were retrospectively extracted from Computerised Radiology Information System. CT COVID-19 changes were categorised according to British Society of Thoracic Radiology reporting guidance. Patient demographics (age and gender), RT-PCR swab results and management pathway (conservative or intervention) were recorded from electronic patient records. Statistical analyses were performed to evaluate any significant association between variables. p values ≤0.05 were regarded as statistically significant.ResultsCompliancy rate in performing complementary CT chest was 92.5% (148/160). Thirty-five patients (35/148,23.6%) underwent intervention during admission. There was no significant association (p = 0.9085) between acquisition of CT chest and management pathway (conservative vs intervention). CT chest had 57% sensitivity (CI 18.41% to 90.1%) and 100% specificity (CI 92% to 100%) in COVID-19 diagnosis. Three of ten patients who had classic COVID-19 changes on CT chest did not have corresponding changes in lung bases.ConclusionCompliance with performing complementary CT chest in acute abdomen patients for COVID-19 screening was high and it did not influence subsequent surgical or interventional management.     

Correlation of chest radiography findings with the severity and progression of COVID-19 pneumonia

PurposeAim is to assess the temporal changes and prognostic value of chest radiograph (CXR) in COVID-19 patients.Material and methodsWe performed a retrospective study of confirmed COVID-19 patients presented to the emergency between March 07–17, 2020. Clinical & radiological findings were reviewed. Clinical outcomes were classified into critical & non-critical based on severity. Two independent radiologists graded frontal view CXRs into COVID-19 pneumonia category 1 (CoV-P1) with <4 zones and CoV-P2 with ≥4 zones involvement. Interobserver agreement of CoV-P category for the CXR preceding the clinical outcome was assessed using Kendall's τ coefficient. Association between CXR findings and clinical deterioration was calculated along with temporal changes of CXR findings with disease progression.ResultsSixty-two patients were evaluated for clinical features. 56 of these (total: 325 CXRs) were evaluated for radiological findings. Common patterns were progression from lower to upper zones, peripheral to diffuse involvement, & from ground glass opacities to consolidation. Consolidations starting peripherally were noted in 76%, 93% and 48% with critical outcomes, respectively. The interobserver agreement of the CoV-P category of CXRs in the critical and non-critical outcome groups were good and excellent, respectively (τ coefficient = 0.6 & 1.0). Significant association was observed between CoV-P2 and clinical deterioration into a critical status (χ2 = 27.7, p = 0.0001) with high sensitivity (95%) and specificity (71%) within a median interval time of 2 days (range: 0–4 days).ConclusionInvolvement of predominantly 4 or more zones on frontal chest radiograph can be used as predictive prognostic indicator of poorer outcome in COVID-19 patients.     

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

ObjectiveTo develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables.Materials and MethodsClinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists.ResultsAmong 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively.ConclusionCT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.     

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs

ObjectivesFor optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes.MethodsIn this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis.ResultsOf all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified.Conclusions:Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management.Advances in knowledgeWe report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.     

Mediastinal lymphadenopathy may predict 30-day mortality in patients with COVID-19

PurposeThere is scarce data on the impact of the presence of mediastinal lymphadenopathy on the prognosis of coronavirus-disease 2019 (COVID-19). We aimed to investigate whether its presence is associated with increased risk for 30-day mortality in a large group of patients with COVID-19.MethodIn this retrospective cross-sectional study, 650 adult laboratory-confirmed hospitalized COVID-19 patients were included. Patients with comorbidities that may cause enlarged mediastinal lymphadenopathy were excluded. Demographics, clinical characteristics, vital and laboratory findings, and outcome were obtained from electronic medical records. Computed tomography scans were evaluated by two blinded radiologists. Univariate and multivariate logistic regression analyses were performed to determine independent predictive factors of 30-day mortality.ResultsPatients with enlarged mediastinal lymphadenopathy (n = 60, 9.2%) were older and more likely to have at least one comorbidity than patients without enlarged mediastinal lymphadenopathy (p = 0.03, p = 0.003). There were more deaths in patients with enlarged mediastinal lymphadenopathy than in those without (11/60 vs 45/590, p = 0.01). Older age (OR:3.74, 95% CI: 2.06–6.79; p < 0.001), presence of consolidation pattern (OR:1.93, 95% CI: 1.09–3.40; p = 0.02) and enlarged mediastinal lymphadenopathy (OR:2.38, 95% CI:1.13–4.98; p = 0.02) were independently associated with 30-day mortality.ConclusionIn this large group of hospitalized patients with COVID-19, we found that in addition to older age and consolidation pattern on CT scan, enlarged mediastinal lymphadenopathy were independently associated with increased mortality. Mediastinal evaluation should be performed in all patients with COVID-19.     

Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients

PurposeThis study aimed to identify predictive (bio-)markers for COVID-19 severity derived from automated quantitative thin slice low dose volumetric CT analysis, clinical chemistry and lung function testing.MethodsSeventy-four COVID-19 patients admitted between March 16th and June 3rd 2020 to the Asklepios Lung Clinic Munich-Gauting, Germany, were included in the study. Patients were categorized in a non-severe group including patients hospitalized on general wards only and in a severe group including patients requiring intensive care treatment. Fully automated quantification of CT scans was performed via IMBIO CT Lung Texture analysis™ software. Predictive biomarkers were assessed with receiver-operator-curve and likelihood analysis.ResultsFifty-five patients (44% female) presented with non-severe COVID-19 and 19 patients (32% female) with severe disease. Five fatalities were reported in the severe group. Accurate automated CT analysis was possible with 61 CTs (82%). Disease severity was linked to lower residual normal lung (72.5% vs 87%, p = 0.003), increased ground glass opacities (GGO) (8% vs 5%, p = 0.031) and increased reticular pattern (8% vs 2%, p = 0.025). Disease severity was associated with advanced age (76 vs 59 years, p = 0.001) and elevated serum C-reactive protein (CRP, 92.2 vs 36.3 mg/L, p < 0.001), lactate dehydrogenase (LDH, 485 vs 268 IU/L, p < 0.001) and oxygen supplementation (p < 0.001) upon admission. Predictive risk factors for the development of severe COVID-19 were oxygen supplementation, LDH >313 IU/L, CRP >71 mg/L, <70% normal lung texture, >12.5% GGO and >4.5% reticular pattern.ConclusionAutomated low dose CT analysis upon admission might be a useful tool to predict COVID-19 severity in patients.