Chest CT opportunistic biomarkers for phenotyping high-risk COVID-19 patients: a retrospective multicentre study.

OBJECTIVE: To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients. METHODS: In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test. RESULTS: The final cohort included 1669 patients (age 67.5 [58.5-77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88-95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001). CONCLUSION: Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients. CLINICAL RELEVANCE STATEMENT: In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification. KEY POINTS: • In COVID-19 patients, several information about patient comorbidities can be quantitatively extracted from chest CT, resulting associated with the severity of oxygen treatment, access to ICU, and death. • A prediction model based on multiparametric opportunistic biomarkers derived from chest CT resulted superior to a model including only clinical variables in a large cohort of 1669 patients suffering from SARS- CoV2 infection. • Opportunistic biomarkers of cardiometabolic comorbidities derived from chest CT may improve COVID-19 patients' risk stratification also in absence of detailed clinical data and laboratory tests identifying subclinical and previously unknown conditions.

Combining semi-quantitative rating and automated brain volumetry in MRI evaluation of patients with probable behavioural variant of fronto-temporal dementia: an added value for clinical practise?

PURPOSE: To evaluate the diagnostic value of combined semiquantitative and quantitative assessment of brain atrophy in the diagnostic workup of the behavioural-variant of frontotemporal dementia (bvFTD). METHODS: Three neuroradiologists defined brain atrophy grading and identified atrophy pattern suggestive of bvFTD on 3D-T1 brain MRI of 112 subjects using a semiquantitative rating scale (Kipps'). A quantitative atrophy assessment was performed using two different automated software (Quantib® ND and Icometrix®). A combined semiquantitative and quantitative assessment of brain atrophy was made to evaluate the improvement in brain atrophy grading to identify probable bvFTD patients. RESULTS: Observers' performances in the diagnosis of bvFTD were very good for Observer 1 (k value = 0.881) and 2 (k value = 0.867), substantial for Observer 3 (k value = 0.741). Semiquantitative atrophy grading of all the observers showed a moderate and a poor correlation with the volume values calculated by Icometrix® and by Quantib® ND, respectively. For the definition of neuroradiological signs presumptive of bvFTD, the use of Icometrix® software improved the diagnostic accuracy for Observer 1 resulting in an AUC of 0.974, and for Observer 3 resulting in a AUC of 0.971 (p-value < 0.001). The use of Quantib® ND software improved the diagnostic accuracy for Observer 1 resulting in an AUC of 0.974, and for Observer 3 resulting in a AUC of 0.977 (p-value < 0.001). No improvement was observed for Observer 2. CONCLUSION: Combining semiquantitative and quantitative brain imaging evaluation allows to reduce discrepancies in the neuroradiological diagnostic workup of bvFTD by different readers.

AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients.

PURPOSE: To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients' risk stratification. MATERIAL AND METHODS: In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web-mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). RESULTS: The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816-0.867) on wave 1 and was used to build a 0-100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402-0.8766). CONCLUSIONS: AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

The impact of COVID-19 pandemic on radiology residents in Northern Italy.

OBJECTIVES: To assess changes in working patterns and education experienced by radiology residents in Northwest Italy during the COVID-19 pandemic. METHODS: An online questionnaire was sent to residents of 9 postgraduate schools in Lombardy and Piedmont, investigating demographics, changes in radiological workload, involvement in COVID-19-related activities, research, distance learning, COVID-19 contacts and infection, changes in training profile, and impact on psychological wellbeing. Descriptive and χ2 statistics were used. RESULTS: Among 373 residents invited, 300 (80%) participated. Between March and April 2020, 44% (133/300) of respondents dedicated their full time to radiology; 41% (124/300) engaged in COVID-19-related activities, 73% (90/124) of whom working in COVID-19 wards; 40% (121/300) dedicated > 25% of time to distance learning; and 66% (199/300) were more involved in research activities than before the pandemic. Over half of residents (57%, 171/300) had contacts with COVID-19-positive subjects, 5% (14/300) were infected, and 8% (23/300) lost a loved one due to COVID-19. Only 1% (3/300) of residents stated that, given the implications of this pandemic scenario, they would not have chosen radiology as their specialty, whereas 7% (22/300) would change their subspecialty. The most common concerns were spreading the infection to their loved ones (30%, 91/300), and becoming sick (7%, 21/300). Positive changes were also noted, such as being more willing to cooperate with other colleagues (36%, 109/300). CONCLUSIONS: The COVID-19 pandemic changed radiology residents' training programmes, with distance learning, engaging in COVID-19-related activities, and a greater involvement in research becoming part of their everyday practice. KEY POINTS: • Of 300 participants, 44% were fully dedicated to radiological activity and 41% devoted time to COVID-19-related activities, 73% of whom to COVID-19 wards. • Distance learning was substantial for 40% of residents, and 66% were involved in research activities more than before the COVID-19 pandemic. • Over half of residents were exposed to COVID-19 contacts and less than one in twenty was infected.

Chest CT-derived pulmonary artery enlargement at the admission predicts overall survival in COVID-19 patients: insight from 1461 consecutive patients in Italy.

OBJECTIVES: Enlarged main pulmonary artery diameter (MPAD) resulted to be associated with pulmonary hypertension and mortality in a non-COVID-19 setting. The aim was to investigate and validate the association between MPAD enlargement and overall survival in COVID-19 patients. METHODS: This is a cohort study on 1469 consecutive COVID-19 patients submitted to chest CT within 72 h from admission in seven tertiary level hospitals in Northern Italy, between March 1 and April 20, 2020. Derivation cohort (n = 761) included patients from the first three participating hospitals; validation cohort (n = 633) included patients from the remaining hospitals. CT images were centrally analyzed in a core-lab blinded to clinical data. The prognostic value of MPAD on overall survival was evaluated at adjusted and multivariable Cox's regression analysis on the derivation cohort. The final multivariable model was tested on the validation cohort. RESULTS: In the derivation cohort, the median age was 69 (IQR, 58-77) years and 537 (70.6%) were males. In the validation cohort, the median age was 69 (IQR, 59-77) years with 421 (66.5%) males. Enlarged MPAD (≥ 31 mm) was a predictor of mortality at adjusted (hazard ratio, HR [95%CI]: 1.741 [1.253-2.418], p < 0.001) and multivariable regression analysis (HR [95%CI]: 1.592 [1.154-2.196], p = 0.005), together with male gender, old age, high creatinine, low well-aerated lung volume, and high pneumonia extension (c-index [95%CI] = 0.826 [0.796-0.851]). Model discrimination was confirmed on the validation cohort (c-index [95%CI] = 0.789 [0.758-0.823]), also using CT measurements from a second reader (c-index [95%CI] = 0.790 [0.753;0.825]). CONCLUSION: Enlarged MPAD (≥ 31 mm) at admitting chest CT is an independent predictor of mortality in COVID-19. KEY POINTS: • Enlargement of main pulmonary artery diameter at chest CT performed within 72 h from the admission was associated with a higher rate of in-hospital mortality in COVID-19 patients. • Enlargement of main pulmonary artery diameter (≥ 31 mm) was an independent predictor of death in COVID-19 patients at adjusted and multivariable regression analysis. • The combined evaluation of clinical findings, lung CT features, and main pulmonary artery diameter may be useful for risk stratification in COVID-19 patients.

Epicardial adipose tissue characteristics, obesity and clinical outcomes in COVID-19: A post-hoc analysis of a prospective cohort study.

BACKGROUND AND AIMS: Obesity-related cardiometabolic risk factors associate with COVID-19 severity and outcomes. Epicardial adipose tissue (EAT) is associated with cardiometabolic disturbances, is a source of proinflammatory cytokines and a marker of visceral adiposity. We investigated the relation between EAT characteristics and outcomes in COVID-19 patients. METHODS AND RESULTS: This post-hoc analysis of a large prospective investigation included all adult patients (≥18 years) admitted to San Raffaele University Hospital in Milan, Italy, from February 25th to April 19th, 2020 with confirmed SARS-CoV-2 infection who underwent a chest computed tomography (CT) scan for COVID-19 pneumonia and had anthropometric data available for analyses. EAT volume and attenuation (EAT-At, a marker of EAT inflammation) were measured on CT scan. Primary outcome was critical illness, defined as admission to intensive care unit (ICU), invasive ventilation or death. Cox regression and regression tree analyses were used to assess the relationship between clinical variables, EAT characteristics and critical illness. One-hundred and ninety-two patients were included (median [25th-75th percentile] age 60 years [53-70], 76% men). Co-morbidities included overweight/obesity (70%), arterial hypertension (40%), and diabetes (16%). At multivariable Cox regression analysis, EAT-At (HR 1.12 [1.04-1.21]) independently predicted critical illness, while increasing PaO2/FiO2 was protective (HR 0.996 [95% CI 0.993; 1.00]). CRP, plasma glucose on admission, EAT-At and PaO2/FiO2 identified five risk groups that significantly differed with respect to time to death or admission to ICU (log-rank p < 0.0001). CONCLUSION: Increased EAT attenuation, a marker of EAT inflammation, but not obesity or EAT volume, predicts critical COVID-19. TRIAL REGISTRATION: NCT04318366.

Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients.

Primary Central Nervous System Lymphoma (PCNSL) is an aggressive neoplasm with a poor prognosis. Although therapeutic progresses have significantly improved Overall Survival (OS), a number of patients do not respond to HD-MTX-based chemotherapy (15-25%) or experience relapse (25-50%) after an initial response. The reasons underlying this poor response to therapy are unknown. Thus, there is an urgent need to develop improved predictive models for PCNSL. In this study, we investigated whether radiomics features can improve outcome prediction in patients with PCNSL. A total of 80 patients diagnosed with PCNSL were enrolled. A patient sub-group, with complete Magnetic Resonance Imaging (MRI) series, were selected for the stratification analysis. Following radiomics feature extraction and selection, different Machine Learning (ML) models were tested for OS and Progression-free Survival (PFS) prediction. To assess the stability of the selected features, images from 23 patients scanned at three different time points were used to compute the Interclass Correlation Coefficient (ICC) and to evaluate the reproducibility of each feature for both original and normalized images. Features extracted from Z-score normalized images were significantly more stable than those extracted from non-normalized images with an improvement of about 38% on average (p-value < 10-12). The area under the ROC curve (AUC) showed that radiomics-based prediction overcame prediction based on current clinical prognostic factors with an improvement of 23% for OS and 50% for PFS, respectively. These results indicate that radiomics features extracted from normalized MR images can improve prognosis stratification of PCNSL patients and pave the way for further study on its potential role to drive treatment choice.

Assessing the added value of apparent diffusion coefficient, cerebral blood volume, and radiomic magnetic resonance features for differentiation of pseudoprogression versus true tumor progression in patients with glioblastoma.

Background: Pseudoprogression (PsPD) is a major diagnostic challenge in the follow-up of patients with glioblastoma (GB) after chemoradiotherapy (CRT). Conventional imaging signs and parameters derived from diffusion and perfusion-MRI have yet to prove their reliability in clinical practice for an accurate differential diagnosis. Here, we tested these parameters and combined them with radiomic features (RFs), clinical data, and MGMT promoter methylation status using machine- and deep-learning (DL) models to distinguish PsPD from Progressive disease. Methods: In a single-center analysis, 105 patients with GB who developed a suspected imaging PsPD in the first 7 months after standard CRT were identified retrospectively. Imaging data included standard MRI anatomical sequences, apparent diffusion coefficient (ADC), and normalized relative cerebral blood volume (nrCBV) maps. Median values (ADC, nrCBV) and RFs (all sequences) were calculated from DL-based tumor segmentations. Generalized linear models with LASSO feature-selection and DL models were built integrating clinical data, MGMT methylation status, median ADC and nrCBV values and RFs. Results: A model based on clinical data and MGMT methylation status yielded an areas under the receiver operating characteristic curve (AUC) = 0.69 (95% CI 0.55-0.83) for detecting PsPD, and the addition of median ADC and nrCBV values resulted in a nonsignificant increase in performance (AUC = 0.71, 95% CI 0.57-0.85, P = .416). Combining clinical/MGMT information with RFs derived from ADC, nrCBV, and from all available sequences both resulted in significantly (both P < .005) lower model performances, with AUC = 0.52 (0.38-0.66) and AUC = 0.54 (0.40-0.68), respectively. DL imaging models resulted in AUCs ≤ 0.56. Conclusion: Currently available imaging biomarkers could not reliably differentiate PsPD from true tumor progression in patients with glioblastoma; larger collaborative efforts are needed to build more reliable models.

Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.

PURPOSE: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation. METHODS: In this paper, we tailored the 3D ResNet to predict the OS of patients with PCNSL. To overcome the limitation of data sparsity, we introduced data augmentation and transfer learning, and we evaluated the results using r stratified k-fold cross-validation. To explain the results of our model, gradient-weighted class activation mapping was applied. RESULTS: We obtained the best performance (the standard error) on post-contrast T1-weighted (T1Gd)-area under curve [Formula: see text], accuracy [Formula: see text], precision [Formula: see text], recall [Formula: see text] and F1-score [Formula: see text], while compared with ML-based models on clinical data and radiomics data, respectively, further confirming the stability of our model. Also, we observed that PCNSL is a whole-brain disease and in the cases where the OS is less than 1 year, it is more difficult to distinguish the tumor boundary from the normal part of the brain, which is consistent with the clinical outcome. CONCLUSIONS: All these findings indicate that T1Gd can improve prognosis predictions of patients with PCNSL. To the best of our knowledge, this is the first time to use DL to explain model patterns in OS classification of patients with PCNSL. Future work would involve collecting more data of patients with PCNSL, or additional retrospective studies on different patient populations with rare diseases, to further promote the clinical role of our model.

Myosteatosis Significantly Predicts Persistent Dyspnea and Mobility Problems in COVID-19 Survivors.

Background: Persistent symptoms including dyspnea and functional impairment are common in COVID-19 survivors. Poor muscle quality (myosteatosis) associates with poor short-term outcomes in COVID-19 patients. The aim of this observational study was to assess the relationship between myosteatosis diagnosed during acute COVID-19 and patient-reported outcomes at 6 months after discharge. Methods: Myosteatosis was diagnosed based on CT-derived skeletal muscle radiation attenuation (SM-RA) measured during hospitalization in 97 COVID-19 survivors who had available anthropometric and clinical data upon admission and at the 6-month follow-up after discharge. Dyspnea in daily activities was assessed using the modified Medical Research Council (mMRC) scale for dyspnea. Health-related quality of life was measured using the European quality of life questionnaire three-level version (EQ-5D-3L). Results: Characteristics of patients with (lowest sex- and age-specific tertile of SM-RA) or without myosteatosis during acute COVID-19 were similar. At 6 months, patients with myosteatosis had greater rates of obesity (48.4 vs. 27.7%, p = 0.046), abdominal obesity (80.0 vs. 47.6%, p = 0.003), dyspnea (32.3 vs. 12.5%, p = 0.021) and mobility problems (32.3 vs. 12.5%, p = 0.004). Myosteatosis diagnosed during acute COVID-19 was the only significant predictor of persistent dyspnea (OR 3.19 [95% C.I. 1.04; 9.87], p = 0.043) and mobility problems (OR 3.70 [95% C.I. 1.25; 10.95], p = 0.018) at 6 months at logistic regression adjusted for sex, age, and BMI. Conclusion: Myosteatosis diagnosed during acute COVID-19 significantly predicts persistent dyspnea and mobility problems at 6 months after hospital discharge independent of age, sex, and body mass. Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [NCT04318366].

Continuous sensing of IFNα by hepatic endothelial cells shapes a vascular antimetastatic barrier.

Hepatic metastases are a poor prognostic factor of colorectal carcinoma (CRC) and new strategies to reduce the risk of liver CRC colonization are highly needed. Herein, we used mouse models of hepatic metastatization to demonstrate that the continuous infusion of therapeutic doses of interferon-alpha (IFNα) controls CRC invasion by acting on hepatic endothelial cells (HECs). Mechanistically, IFNα promoted the development of a vascular antimetastatic niche characterized by liver sinusoidal endothelial cells (LSECs) defenestration extracellular matrix and glycocalyx deposition, thus strengthening the liver vascular barrier impairing CRC trans-sinusoidal migration, without requiring a direct action on tumor cells, hepatic stellate cells, hepatocytes, or liver dendritic cells (DCs), Kupffer cells (KCs) and liver capsular macrophages (LCMs). Moreover, IFNα endowed LSECs with efficient cross-priming potential that, along with the early intravascular tumor burden reduction, supported the generation of antitumor CD8+ T cells and ultimately led to the establishment of a protective long-term memory T cell response. These findings provide a rationale for the use of continuous IFNα therapy in perioperative settings to reduce CRC metastatic spreading to the liver.

Colorectal cancer remains one of the most widespread and deadly cancers worldwide. Poor health outcomes are usually linked to diseased cells spreading from the intestine to create new tumors in the liver or other parts of the body. Treatment involves surgically removing the initial tumors in the bowel, but patient survival could be improved if, in parallel, their immune system was 'boosted' to destroy cancer cells before they can form other tumors. Interferon alpha is a small protein which helps to coordinate how the immune system recognizes and deactivates foreign agents and cancerous cells. It has recently been trialed as a colorectal cancer treatment to prevent tumors from spreading to the liver, but only with limited success. This partly because interferon-alpha is usually administered in high and pulsed doses, which cause severe side effects through the body. Instead, Tran, Ferreira, Alvarez-Moya et al. aimed to investigate whether continuously delivering lower amounts of the drug could be a better approach. This strategy was tested on mice in which colorectal cancer cells had been implanted into the wall of the large intestine. Continuous administration minimized the risk of the implanted cancer cells spreading to the liver while also creating fewer side effects. The team was able to identify an optimum delivery strategy by varying how much interferon-alpha the animals received and when. Further experiments also revealed a new mechanism by which interferon-alpha prevented the spread of colorectal cancer. Upon receiving continuous doses of the drug, a group of liver cells started to generate a physical barrier which stopped cancer cells from being able to invade the organ. The treatment also promoted long-term immune responses that targeted diseased cells while being safe for healthy tissues. If confirmed in clinical trials, these results suggest that colorectal patients undergoing tumor removal surgery may benefit from also receiving interferon-alpha through continuous delivery.

The hidden interplay between sex and COVID-19 mortality: the role of cardiovascular calcification.

Recent clinical and demographical studies on COVID-19 patients have demonstrated that men experience worse outcomes than women. However, in most cases, the data were not stratified according to gender, limiting the understanding of the real impact of gender on outcomes. This study aimed to evaluate the disaggregated in-hospital outcomes and explore the possible interactions between gender and cardiovascular calcifications. Data was derived from the sCORE-COVID-19 registry, an Italian multicentre registry that enrolled COVID-19 patients who had undergone a chest computer tomography scan on admission. A total of 1683 hospitalized patients (mean age 67±14 years) were included. Men had a higher prevalence of cardiovascular comorbidities, a higher pneumonia extension, more coronary calcifications (63% vs.50.9%, p<0.001), and a higher coronary calcium score (391±847 vs. 171±479 mm3, p<0.001). Men experienced a significantly higher mortality rate (24.4% vs. 17%, p=0.001), but the death event tended to occur earlier in women (15±7 vs. 8±7 days, p= 0.07). Non-survivors had a higher coronary, thoracic aorta, and aortic valve calcium score. Female sex, a known independent predictor of a favorable outcome in SARS-CoV2 infection, was not protective in women with a coronary calcification volume greater than 100 mm3. There were significant differences in cardiovascular comorbidities and vascular calcifications between men and women with SARS-CoV2 pneumonia. The differences in outcomes can be at least partially explained by the different cardiovascular profiles. However, women with poor outcomes had the same coronary calcific burden as men. The presumed favorable female sex bias in COVID-19 must therefore be reviewed in the context of comorbidities, especially cardiovascular ones.

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

BACKGROUND: Coronavirus 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. OBJECTIVES: The aim was to evaluate the association of coronary artery calcium and total thoracic calcium on in-hospital mortality in COVID-19 patients. METHODS: 1093 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. RESULTS: Non-survivors compared to survivors had higher coronary artery [Agatston (467.76 â€‹± â€‹570.92 vs 206.80 â€‹± â€‹424.13 â€‹mm2, p â€‹< â€‹0.001); Volume (487.79 â€‹± â€‹565.34 vs 207.77 â€‹± â€‹406.81, p â€‹< â€‹0.001)], aortic valve [Volume (322.45 â€‹± â€‹390.90 vs 98.27 â€‹± â€‹250.74 mm2, p â€‹< â€‹0.001; Agatston 337.38 â€‹± â€‹414.97 vs 111.70 â€‹± â€‹282.15, p â€‹< â€‹0.001)] and thoracic aorta [Volume (3786.71 â€‹± â€‹4225.57 vs 1487.63 â€‹± â€‹2973.19 mm2, p â€‹< â€‹0.001); Agatston (4688.82 â€‹± â€‹5363.72 vs 1834.90 â€‹± â€‹3761.25, p â€‹< â€‹0.001)] calcium values. Coronary artery calcium (HR 1.308; 95% CI, 1.046-1.637, p â€‹= â€‹0.019) and total thoracic calcium (HR 1.975; 95% CI, 1.200-3.251, p â€‹= â€‹0.007) resulted to be independent predictors of in-hospital mortality. CONCLUSION: Coronary, aortic valve and thoracic aortic calcium assessment on admission non-gated CT permits to stratify the COVID-19 patients in-hospital mortality risk.

Impact of clinical and subclinical coronary artery disease as assessed by coronary artery calcium in COVID-19.

BACKGROUND AND AIMS: The potential impact of coronary atherosclerosis, as detected by coronary artery calcium, on clinical outcomes in COVID-19 patients remains unsettled. We aimed to evaluate the prognostic impact of clinical and subclinical coronary artery disease (CAD), as assessed by coronary artery calcium score (CAC), in a large, unselected population of hospitalized COVID-19 patients undergoing non-gated chest computed tomography (CT) for clinical practice. METHODS: SARS-CoV 2 positive patients from the multicenter (16 Italian hospitals), retrospective observational SCORE COVID-19 (calcium score for COVID-19 Risk Evaluation) registry were stratified in three groups: (a) "clinical CAD" (prior revascularization history), (b) "subclinical CAD" (CAC >0), (c) "No CAD" (CAC = 0). Primary endpoint was in-hospital mortality and the secondary endpoint was a composite of myocardial infarction and cerebrovascular accident (MI/CVA). RESULTS: Amongst 1625 patients (male 67.2%, median age 69 [interquartile range 58-77] years), 31%, 57.8% and 11.1% had no, subclinical and clinical CAD, respectively. Increasing rates of in-hospital mortality (11.3% vs. 27.3% vs. 39.8%, p < 0.001) and MI/CVA events (2.3% vs. 3.8% vs. 11.9%, p < 0.001) were observed for patients with no CAD vs. subclinical CAD vs clinical CAD, respectively. The association with in-hospital mortality was independent of in-study outcome predictors (age, peripheral artery disease, active cancer, hemoglobin, C-reactive protein, LDH, aerated lung volume): subclinical CAD vs. No CAD: adjusted hazard ratio (adj-HR) 2.86 (95% confidence interval [CI] 1.14-7.17, p=0.025); clinical CAD vs. No CAD: adj-HR 3.74 (95% CI 1.21-11.60, p=0.022). Among patients with subclinical CAD, increasing CAC burden was associated with higher rates of in-hospital mortality (20.5% vs. 27.9% vs. 38.7% for patients with CAC score thresholds≤100, 101-400 and > 400, respectively, p < 0.001). The adj-HR per 50 points increase in CAC score 1.007 (95%CI 1.001-1.013, p=0.016). Cardiovascular risk factors were not independent predictors of in-hospital mortality when CAD presence and extent were taken into account. CONCLUSIONS: The presence and extent of CAD are associated with in-hospital mortality and MI/CVA among hospitalized patients with COVID-19 disease and they appear to be a better prognostic gauge as compared to a clinical cardiovascular risk assessment.

Leakage Risk Stratification After Laparoscopic Sleeve Gastrectomy (LSG): Is There a Role for Routine Postoperative CT Scan?

PURPOSE: Leakage of the gastric remnant after laparoscopic sleeve gastrectomy (LSG) represents an unpredictable, dreadful occurrence. Our aim was to assess whether routine postoperative CT scan is an effective tool for early prediction of leakage after LSG. MATERIALS AND METHODS: From a prospectively acquired database, all consecutive patients who underwent LSG between January 2015 and December 2018 were identified; within this database, all patients who were evaluated with at least one contrast-enhanced CT scan within 48 h from surgery were enrolled in this retrospective study. The selected CT findings included twisting of the gastric remnant, perigastric air bubbles, and hematoma; the antral segment proximal from the pylorus to the first staple firing was also analyzed in terms of distance (StP, stapler to pylorus distance) and linearity (LI, linearity index). RESULTS: After exclusions, 250 patients were included; 10 patients suffered from gastric leakage. Patients with perigastric hematoma and/or twisting of the distal part of the gastric remnant on routine postoperative CT scan were found to be more likely to develop leakage after LSG (p = 0.005 and p < 0.001, respectively). The mean StP was 45 ± 19.1 mm; the mean LI was 1.54 ± 0.4. Patients with subsequent development of leakage had significantly lower StP (26.7 ± 12.5 mm vs. 45.9 ± 18.9 mm; p = 0.001) and LI values (1.16 ± 0.11 vs. 1.55 ± 0.39; p = 0.002). CONCLUSION: Routine postoperative CT scan after LSG permits early stratification of leakage risk, thus providing an actual aid for patients' management.