Symptomatic post COVID patients have impaired alveolar capillary membrane function and high VE/VCO2.

BACKGROUND: Post COVID-19 syndrome is characterized by several cardiorespiratory symptoms but the origin of patients' reported symptomatology is still unclear. METHODS: Consecutive post COVID-19 patients were included. Patients underwent full clinical evaluation, symptoms dedicated questionnaires, blood tests, echocardiography, thoracic computer tomography (CT), spirometry including alveolar capillary membrane diffusion (DM) and capillary volume (Vcap) assessment by combined carbon dioxide and nitric oxide lung diffusion (DLCO/DLNO) and cardiopulmonary exercise test. We measured surfactant derive protein B (immature form) as blood marker of alveolar cell function. RESULTS: We evaluated 204 consecutive post COVID-19 patients (56.5 ± 14.5 years, 89 females) 171 ± 85 days after the end of acute COVID-19 infection. We measured: forced expiratory volume (FEV1) 99 ± 17%pred, FVC 99 ± 17%pred, DLCO 82 ± 19%, DM 47.6 ± 14.8 mL/min/mmHg, Vcap 59 ± 17 mL, residual parenchymal damage at CT 7.2 ± 3.2% of lung tissue, peakVO2 84 ± 18%pred, VE/VCO2 slope 112 [102-123]%pred. Major reported symptoms were: dyspnea 45% of cases, tiredness 60% and fatigability 77%. Low FEV1, Vcap and high VE/VCO2 slope were associated with persistence of dyspnea. Tiredness was associated with high VE/VCO2 slope and low PeakVO2 and FEV1 while fatigability with high VE/VCO2 slope. SPB was fivefold higher in post COVID-19 than in normal subjects, but not associated to any of the referred symptoms. SPB was negatively associated to Vcap. CONCLUSIONS: In patients with post COVID-19, cardiorespiratory symptoms are linked to VE/VCO2 slope. In these patients the alveolar cells are dysregulated as shown by the very high SPB. The Vcap is low likely due to post COVID-19 pulmonary endothelial/vasculature damage but DLCO is only minimally impaired being DM preserved.

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.

Cardiac computed tomography: from anatomy to function.

Ischaemic heart disease (IHD) is one of the world's leading causes of morbidity and mortality. Likewise, the diagnosis and risk stratification of patients with coronary artery disease (CAD) have always been based on the detection of the presence and extent of ischaemia by physical or pharmacological stress tests with or without the aid of imaging methods (e.g. exercise stress, test, stress echocardiography, single-photon emission computed tomography, or stress cardiac magnetic resonance). These methods show high performance to assess obstructive CAD, whilst they do not show accurate power to detect non-obstructive CAD. The introduction into clinical practice of coronary computed tomography angiography, the only non-invasive method capable of analyzing the coronary anatomy, allowed to add a crucial piece in the puzzle of the assessment of patients with suspected or chronic IHD. The current review evaluates the technical aspects and clinical experience of coronary computed tomography in the evaluation of atherosclerotic burden with a special focus about the new emerging application such as functional relevance of CAD with fractional flow reserve computed tomography (CT)-derived (FFRct), stress CT perfusion, and imaging inflammatory makers discussing the strength and weakness of each approach.

Computed tomography predictors of structural valve degeneration in patients undergoing transcatheter aortic valve implantation with balloon-expandable prostheses.

OBJECTIVES: Computed tomography (CT) provides excellent anatomy assessment of the aortic annulus (AoA) and is utilized for pre-procedural planning of transcatheter aortic valve implantation (TAVI). We sought to investigate if geometrical characteristics of the AoA determined by CT may represent predictors of structural valve degeneration (SVD) in patients undergoing TAVI with balloon-expandable valves. METHODS: This is a retrospective study on 124 consecutive patients (mean age: 79 ± 7 years; female: 61%) undergoing balloon-expandable TAVI prospectively enrolled in a registry. AoA maximum diameter (Dmax), minimum diameter (Dmin), and area were assessed using pre-procedural CT. SVD was identified during follow-up with transthoracic echocardiography documenting structural prosthetic valve abnormalities with or without hemodynamic changes. RESULTS: The mean follow-up was 5.9 ± 1.7 years. SVD was found in 48 out of 124 patients (38%). AoA Dmax, Dmin, and area were significantly smaller in patients with SVD compared to patients without SVD (25.6 ± 2.2 mm vs. 27.1 ± 2.8 mm, p = 0.012; 20.5 ± 2.1 mm vs. 21.8 ± 2.1 mm, p = 0.001 and 419 ± 77 mm2 vs. 467 ± 88 mm2, p = 0.002, respectively). At univariable analysis, female sex, BSA, 23-mm prosthetic valve size, Dmax < 27.1 mm, and a Dmin < 19.9 mm were associated with SVD, whereas at multivariable analysis, only Dmin < 19.9 mm (OR = 2.873, 95% CI: 1.191-6.929, p = 0.019) and female sex (OR = 2.659, 95% CI: 1.095-6.458, p = 0.031) were independent predictors of SVD. CONCLUSIONS: Female sex and AoA Dmin < 19.9 mm are associated with SVD in patients undergoing TAVI with balloon-expandable valves. When implanting large prostheses in order to avoid paraprosthetic regurgitation, caution should be observed due to the risk of excessive stretching of the AoA Dmin, which may play a role in SVD. KEY POINTS: • Long-term durability is a concern for transcatheter aortic valve bioprosthesis. • CT provides an excellent assessment of the aortic annulus's geometrical characteristics for prosthesis sizing before transcatheter aortic valve implantation (TAVI). • Female sex and a small minimum aortic annulus diameter measured with CT are independent predictors of structural valve degeneration in patients undergoing TAVI with balloon-expandable valves.

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.

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.

Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.

OBJECTIVE: We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems. METHODS: A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death. RESULTS: The final population comprised 743 patients (mean age 65  ±â€¯ 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores. CONCLUSION: Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time. ADVANCES IN KNOWLEDGE: Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.

Intolerance to occlusion during carotid artery stenting with proximal protection: causes, mechanisms, treatment and prevention.

Periprocedural cerebral microembolization is the most important complication of carotid artery stenting. Among several variables that play a role to reduce this risk, brain protection (proximal vs. distal) plays a pivot role. Data are accumulating in favor of a better performance of proximal vs. distal especially in symptomatic patients and high-risk carotid plaques. A prerequisite for the technique to be safe and effective is the presence of a valid intracranial collateral circulation to compensate for the target vessel hemisphere avoiding patient intolerance. This complication may occur either soon after the common carotid balloon occlusion or slowly developing during the procedure peaking at the stent post-dilation step. While Willis' circle anatomic variants are the most frequent cause of acute intolerance, a mix of anatomic, hemodynamic and patient cerebral condition play a role for the late developing form. Prevention is the best treatment of intolerance through a pre- and procedural imaging with different techniques (CT angiography, NMR angiography, transcranial Doppler assessment, digital subtraction angiography and back pressure monitoring).

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks.

Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2, 68 unseen test cases, and 695 independent controls. Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of 0.89 ± 0.07 ; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of 0.89 ± 0.06 as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95-0.98). Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.

The Potential Role of Cardiac CT in the Evaluation of Patients With Known or Suspected Cardiomyopathy: From Traditional Indications to Novel Clinical Applications.

After 15 years from its advent in the clinical field, coronary computed tomography (CCTA) is now widely considered as the best first-step test in patients with low-to-moderate pre-test probability of coronary artery disease. Technological innovation was of pivotal importance for the extensive clinical and scientific interest in CCTA. Recently, the advent of last generation wide-coverage CT scans paved the way for new clinical applications of this technique beyond coronary arteries anatomy evaluation. More precisely, both biventricular volume and systolic function quantification and myocardial fibrosis identification appeared to be feasible with last generation CT. In the present review we would focus on potential applications of cardiac computed tomography (CCT), beyond CCTA, for a comprehensive assessment patients with newly diagnosed cardiomyopathy, from technical requirements to novel clinical applications.

Incidence and characterization of acute pulmonary embolism in patients with SARS-CoV-2 pneumonia: A multicenter Italian experience.

BACKGROUND AND AIMS: Several studies reported a high incidence of pulmonary embolism (PE) among patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, but detailed data about clinical characteristics, risk factors of these patients and prognostic role of PE are still lacking. We aim to evaluate the occurrence of pulmonary embolism among patients with SARS-CoV-2 infection, and to describe their risk factors, clinical characteristics, and in-hospital clinical outcomes. METHODS: This is a multicenter Italian study including 333 consecutive SARS-CoV-2 patients admitted to seven hospitals from February 22 to May 15, 2020. All the patients underwent computed tomography pulmonary angiography (CTPA) for PE detection. In particular, CTPA was performed in case of inadequate response to high-flow oxygen therapy (Fi02≥0.4 to maintain Sp02≥92%), elevated D-dimer (>0.5µg/mL), or echocardiographic signs of right ventricular dysfunction. Clinical, laboratory and radiological data were also analyzed. RESULTS: Among 333 patients with laboratory confirmed SARS-CoV-2 pneumonia and undergoing CTPA, PE was detected in 109 (33%) cases. At CTPA, subsegmental, segmental, lobar and central thrombi were detected in 31 (29%), 50 (46%), 20 (18%) and 8 (7%) cases, respectively. In-hospital death occurred in 29 (27%) patients in the PE-group and in 47 (21%) patients in the non-PE group (p = 0.25). Patients in PE-group had a low rate of traditional risk factors and deep vein thrombosis was detected in 29% of patients undergoing compression ultrasonography. In 71% of cases with documented PE, the thrombotic lesions were located in the correspondence of parenchymal consolidation areas. CONCLUSIONS: Despite a low rate of risk factors for venous thromboembolism, PE is present in about 1 out 3 patients with SARS-CoV-2 pneumonia undergoing CTPA for inadequate response to oxygen therapy, elevated D-dimer level, or echocardiographic signs of right ventricular dysfunction. In most of the cases, the thromboses were located distally in the pulmonary tree and were mainly confined within pneumonia areas.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks.

Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in Coronavirus disease 2019 (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (LSTM) networks. Utilizing the expert annotations, model training was performed using 5-fold cross-validation to segment ground-glass opacity and high opacity (including consolidation and pleural effusion). The performance of the method was evaluated on CT data sets from 197 patients with positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score coefficient of 0.876 ± 0.005; excellent correlations of 0.978 and 0.981 for ground-glass opacity and high opacity volumes. In the external validation set of 67 patients, there was dice score coefficient of 0.767 ± 0.009 as well as excellent correlations of 0.989 and 0.996 for ground-glass opacity and high opacity volumes. Computations for a CT scan comprising 120 slices were performed under 2 seconds on a personal computer equipped with NVIDIA Titan RTX graphics processing unit. Therefore, our deep learning-based method allows rapid fully-automated quantitative measurement of pneumonia burden from CT and may generate the big data with an accuracy similar to the expert readers.

Prevalence and prognosis of pericardial effusion in patients affected by pectus excavatum: A case-control study.

BACKGROUND: The presence of pectus excavatum(PEX) has been occasionally associated with pericardial effusion. Aim of the present study was to compare incidence and prognosis of pericardial effusion in a group of unselected patients with PEX vs a control group. METHODS: From a prospective registry of consecutive patients who underwent chest CT for cardiovascular disease, subjects with a radiological diagnosis of PEX were retrospectively identified (cases); from the same registry patients (controls) without rib cage abnormalities were randomly selected, until a 1:2 ratio was reached. The presence of pericardial effusion at CT was quantified. Follow-up was obtained for a composite end-point: cardiac tamponade, need for pericardiocentesis, need for cardiac surgery for relapsing pericardial effusion. RESULTS: A total of 43 patients with PEX (20 females) and a control group of 86 cases (31 females) without rib cage abnormalities were identified. Pericardial effusion evaluated at CT was significatively more prevalent in patients with PEX vs control group, 37.2% vs 13.9% (p < 0.001), respectively; four patients with PEX (9.3%) had at least moderate pericardial effusion vs no subjects among the controls (p = 0.004). PEX diagnosis was significantly associated to pericardial effusion at multi-variate analysis (OR95%CI 10.91[3.47-34.29], p < 0.001). At a mean follow-up of 6.5 ± 3.4 years no pericardial events were recorded. CONCLUSION: Our findings support the higher prevalence of pericardial effusion in patients with PEX when compared to a control group. The absence of adverse pericardial events at follow-up suggest the good prognosis of these effusions, that in the appropriate clinical setting might not be considered "idiopathic".

Epicardial adipose tissue is associated with extent of pneumonia and adverse outcomes in patients with COVID-19.

AIM: We sought to examine the association of epicardial adipose tissue (EAT) quantified on chest computed tomography (CT) with the extent of pneumonia and adverse outcomes in patients with coronavirus disease 2019 (COVID-19). METHODS: We performed a post-hoc analysis of a prospective international registry comprising 109 consecutive patients (age 64 ±â€¯16 years; 62% male) with laboratory-confirmed COVID-19 and noncontrast chest CT imaging. Using semi-automated software, we quantified the burden (%) of lung abnormalities associated with COVID-19 pneumonia. EAT volume (mL) and attenuation (Hounsfield units) were measured using deep learning software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. RESULTS: In multivariable linear regression analysis adjusted for patient comorbidities, the total burden of COVID-19 pneumonia was associated with EAT volume (ß = 10.6, p = 0.005) and EAT attenuation (ß = 5.2, p = 0.004). EAT volume correlated with serum levels of lactate dehydrogenase (r = 0.361, p = 0.001) and C-reactive protein (r = 0.450, p < 0.001). Clinical deterioration or death occurred in 23 (21.1%) patients at a median of 3 days (IQR 1-13 days) following the chest CT. In multivariable logistic regression analysis, EAT volume (OR 5.1 [95% CI 1.8-14.1] per doubling p = 0.011) and EAT attenuation (OR 3.4 [95% CI 1.5-7.5] per 5 Hounsfield unit increase, p = 0.003) were independent predictors of clinical deterioration or death, as was total pneumonia burden (OR 2.5, 95% CI 1.4-4.6, p = 0.002), chronic lung disease (OR 1.3 [95% CI 1.1-1.7], p = 0.011), and history of heart failure (OR 3.5 [95% 1.1-8.2], p = 0.037). CONCLUSIONS: EAT measures quantified from chest CT are independently associated with extent of pneumonia and adverse outcomes in patients with COVID-19, lending support to their use in clinical risk stratification.

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.

Quantitative Burden of COVID-19 Pneumonia on Chest CT Predicts Adverse Outcomes: A Post-Hoc Analysis of a Prospective International Registry.

PURPOSE: To examine the independent and incremental value of CT-derived quantitative burden and attenuation of COVID-19 pneumonia for the prediction of clinical deterioration or death. METHODS: This was a retrospective analysis of a prospective international registry of consecutive patients with laboratory-confirmed COVID-19 and chest CT imaging, admitted to four centers between January 10 and May 6, 2020. Total burden (expressed as a percentage) and mean attenuation of ground glass opacities (GGO) and consolidation were quantified from CT using semi-automated research software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. Logistic regression was performed to assess the predictive value of clinical and CT parameters for the primary outcome. RESULTS: The final population comprised 120 patients (mean age 64 ± 16 years, 78 men), of whom 39 (32.5%) experienced clinical deterioration or death. In multivariable regression of clinical and CT parameters, consolidation burden (odds ratio [OR], 3.4; 95% confidence interval [CI]: 1.7, 6.9 per doubling; P = .001) and increasing GGO attenuation (OR, 3.2; 95% CI: 1.3, 8.3 per standard deviation, P = .02) were independent predictors of deterioration or death; as was C-reactive protein (OR, 2.1; 95% CI: 1.3, 3.4 per doubling; P = .004), history of heart failure (OR 1.3; 95% CI: 1.1, 1.6, P = .01), and chronic lung disease (OR, 1.3; 95% CI: 1.0, 1.6; P = .02). Quantitative CT measures added incremental predictive value beyond a model with only clinical parameters (area under the curve, 0.93 vs 0.82, P = .006). The optimal prognostic cutoffs for burden of COVID-19 pneumonia as determined by Youden's index were consolidation of greater than or equal to 1.8% and GGO of greater than or equal to 13.5%. CONCLUSIONS: Quantitative burden of consolidation or GGO on chest CT independently predict clinical deterioration or death in patients with COVID-19 pneumonia. CT-derived measures have incremental prognostic value over and above clinical parameters, and may be useful for risk stratifying patients with COVID-19.

Iatrogenic Left Internal Mammary Artery Perforation Treated With a Covered Stent Via Transradial Approach.

Inadvertent perforation of the left internal mammary artery during a blind approach to the subclavian vein for pacemaker or central venous catheter insertion is an emergency that requires immediate treatment. Covered stent deployment is a quick and effective treatment, especially in patients with hemodynamic instability. The procedure may be safely performed by using the radial approach. (Level of Difficulty: Intermediate.).

Multimodality imaging of left atrium in patients with atrial fibrillation.

Atrial fibrillation (AF) is the most common arrhythmia worldwide associated with significant morbidity and mortality and represents a significant health care burden. Goals of AF treatment include prevention of cardioembolic stroke using anticoagulation and device therapy and restoration of sinus rhythm using antiarrhythmic drugs or catheter ablation techniques. A comprehensive assessment of cardiac chamber size and function is often started with echocardiography as a first line diagnostic imaging strategy. Recently, innovations in advanced imaging using cardiac magnetic resonance (CMR) and cardiac computed tomography (CCT) provide a detailed characterization of atrial anatomy and have been shown to accurately exclude thrombus and guide left atrial appendage (LAA) closure or catheter ablation (CA) of atrial fibrillation. Compared to echocardiography, CCT offers an uncompromised spatial resolution and a fast dataset acquisition, with the disadvantages of the need of iodine contrast agent and radiation exposure. CMR, conversely, can rely on very high temporal resolution, the unique feature of tissue characterization and the absence of radiation exposure. However, the main drawbacks of this diagnostic tool are long scan times and low availability. This review will illustrate the vital role of multimodality cardiac imaging in the accurate identification of left atrial, pulmonary vein and LAA size and function, discuss advanced imaging techniques to rule out thrombus and highlight novel CMR and CCT techniques to guide catheter ablation of AF and LAA occlusion.

Image Quality, Overall Evaluability, and Effective Radiation Dose of Coronary Computed Tomography Angiography With Prospective Electrocardiographic Triggering Plus Intracycle Motion Correction Algorithm in Patients With a Heart Rate Over 65 Beats Per Minute.

PURPOSE: Recently, a new intracycle motion correction algorithm (MCA) was introduced to reduce motion artifacts from heart rate (HR) in coronary computed tomography angiography (cCTA). The aim of the study was to evaluate the image quality, overall evaluability, and effective radiation dose (ED) of cCTA with prospective electrocardiographic (ECG) triggering plus MCA as compared with standard protocol with retrospective ECG triggering in patients with HR≥65 bpm. MATERIALS AND METHODS: One hundred consecutive patients (67±10 y) scheduled for cCTA with 65