Réduction des artéfacts de tuteur coronarien au moyen d’un algorithme de reconstruction avec renforcement des bords : étude prospective transversale en tomodensitométrie 256 coupes

Les artéfacts métalliques entraînent un épaississement artéfactuel de la paroi des tuteurs en tomodensitométrie (TDM) avec réduction apparente de leur lumière. Cette étude transversale prospective, devis mesures répétées et observateurs avec méthode en aveugle, chez 24 patients consécutifs/71 tuteurs coronariens a pour objectif de comparer l’épaisseur de paroi des tuteurs en TDM après reconstruction par un algorithme avec renforcement des bords et un algorithme standard. Une angiographie coronarienne par TDM 256 coupes a été réalisée, avec reconstruction par algorithmes avec renforcement des bords et standard. L’épaisseur de paroi des tuteurs était mesurée par méthodes orthogonale (diamètres) et circonférentielle (circonférences). La qualité d’image des tuteurs était évaluée par échelle ordinale, et les données analysées par modèles linéaire mixte et régression logistique des cotes proportionnelles. L’épaisseur de paroi des tuteurs était inférieure avec l’algorithme avec renforcement des bords comparé à l’algorithme standard, avec les méthodes orthogonale (0,97±0,02 vs 1,09±0,03 mm, respectivement; p<0,001) et circonférentielle (1,13±0,02 vs 1,21±0,02 mm, respectivement; p<0,001). Le premier causait moins de surestimation par rapport à l’épaisseur nominale comparé au second, avec méthodes orthogonale (0,89±0,19 vs 1,00±0,26 mm, respectivement; p<0,001) et circonférentielle (1,06±0,26 vs 1,13±0,31 mm, respectivement; p=0,005) et diminuait de 6 % la surestimation. Les scores de qualité étaient meilleurs avec l’algorithme avec renforcement des bords (OR 3,71; IC 95% 2,33–5,92; p<0,001). En conclusion, la reconstruction des images avec l’algorithme avec renforcement des bords génère des parois de tuteurs plus minces, moins de surestimation, et de meilleurs scores de qualité d’image que l’algorithme standard.Metallic artifacts can result in an artificial thickening of the coronary stent wall which can significantly impair computed tomography (CT) imaging in patients with coronary stents. The purpose of this study is to assess the in vivo visualization of coronary stent wall and lumen with an edge-enhancing CT reconstruction kernel, as compared to a standard kernel. This is a prospective cross-sectional study of 24 consecutive patients with 71 coronary stents, using a repeated measure design and blinded observers, approved by the Local Institutional Review Board. 256-slice CT angiography was used, as well as standard and edge-enhancing reconstruction kernels. Stent wall thickness was measured with orthogonal and circumference methods, averaging wall thickness from stent diameter and circumference measurements, respectively. Stent image quality was assessed on an ordinal scale. Statistical analysis used linear and proportional odds models. Stent wall thickness was inferior using the edge-enhancing kernel compared to the standard kernel, either with the orthogonal (0.97±0.02 versus 1.09±0.03 mm, respectively; p<0.001) or circumference method (1.13±0.02 versus 1.21±0.02 mm, respectively; p<0.001). The edge-enhancing kernel generated less overestimation from nominal thickness compared to the standard kernel, both with orthogonal (0.89±0.19 versus 1.00±0.26 mm, respectively; p<0.001) and circumference (1.06±0.26 versus 1.13±0.31 mm, respectively; p=0.005) methods. The average decrease in stent wall thickness overestimation with an edge-enhancing kernel was 6%. Image quality scores were higher with the edge-enhancing kernel (odds ratio 3.71, 95% CI 2.33–5.92; p<0.001). In conclusion, the edge-enhancing CT reconstruction kernel generated thinner stent walls, less overestimation from nominal thickness, and better image quality scores than the standard kernel

Coronary stent artifact reduction with an edge-enhancing reconstruction kernel : a prospective cross-sectional study with 256-slice CT

Purpose Metallic artifacts can result in an artificial thickening of the coronary stent wall which can significantly impair computed tomography (CT) imaging in patients with coronary stents. The objective of this study is to assess in vivo visualization of coronary stent wall and lumen with an edge-enhancing CT reconstruction kernel, as compared to a standard kernel. Methods This is a prospective cross-sectional study involving the assessment of 71 coronary stents (24 patients), with blinded observers. After 256-slice CT angiography, image reconstruction was done with medium-smooth and edge-enhancing kernels. Stent wall thickness was measured with both orthogonal and circumference methods, averaging thickness from diameter and circumference measurements, respectively. Image quality was assessed quantitatively using objective parameters (noise, signal to noise (SNR) and contrast to noise (CNR) ratios), as well as visually using a 5-point Likert scale. Results Stent wall thickness was decreased with the edge-enhancing kernel in comparison to the standard kernel, either with the orthogonal (0.97 ± 0.02 versus 1.09 ± 0.03 mm, respectively; p<0.001) or the circumference method (1.13 ± 0.02 versus 1.21 ± 0.02 mm, respectively; p = 0.001). The edge-enhancing kernel generated less overestimation from nominal thickness compared to the standard kernel, both with the orthogonal (0.89 ± 0.19 versus 1.00 ± 0.26 mm, respectively; p<0.001) and the circumference (1.06 ± 0.26 versus 1.13 ± 0.31 mm, respectively; p = 0.005) methods. The edge-enhancing kernel was associated with lower SNR and CNR, as well as higher background noise (all p < 0.001), in comparison to the medium-smooth kernel. Stent visual scores were higher with the edge-enhancing kernel (p<0.001). Conclusion In vivo 256-slice CT assessment of coronary stents shows that the edge-enhancing CT reconstruction kernel generates thinner stent walls, less overestimation from nominal thickness, and better image quality scores than the standard kernel

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions

Deep learning of chest X‑rays can predict mechanical ventilation outcome in ICU‑admitted COVID‑19 patients

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model

Upregulated IL-32 expression and reduced gut short chain fatty acid caproic acid in people living with HIV with subclinical atherosclerosis

Despite the success of antiretroviral therapy (ART), people living with HIV (PLWH) are still at higher risk for cardiovascular diseases (CVDs) that are mediated by chronic inflammation. Identification of novel inflammatory mediators with the inherent potential to be used as CVD biomarkers and also as therapeutic targets is critically needed for better risk stratification and disease management in PLWH. Here, we investigated the expression and potential role of the multi-isoform proinflammatory cytokine IL-32 in subclinical atherosclerosis in PLWH (n=49 with subclinical atherosclerosis and n=30 without) and HIV- controls (n=25 with subclinical atherosclerosis and n=24 without). While expression of all tested IL-32 isoforms (α, β, γ, D, ϵ, and θ) was significantly higher in peripheral blood from PLWH compared to HIV- controls, IL-32D and IL-32θ isoforms were further upregulated in HIV+ individuals with coronary artery atherosclerosis compared to their counterparts without. Upregulation of these two isoforms was associated with increased plasma levels of IL-18 and IL-1β and downregulation of the atheroprotective protein TRAIL, which together composed a unique atherosclerotic inflammatory signature specific for PLWH compared to HIV- controls. Logistic regression analysis demonstrated that modulation of these inflammatory variables was independent of age, smoking, and statin treatment. Furthermore, our in vitro functional data linked IL-32 to macrophage activation and production of IL-18 and downregulation of TRAIL, a mechanism previously shown to be associated with impaired cholesterol metabolism and atherosclerosis. Finally, increased expression of IL-32 isoforms in PLWH with subclinical atherosclerosis was associated with altered gut microbiome (increased pathogenic bacteria; Rothia and Eggerthella species) and lower abundance of the gut metabolite short-chain fatty acid (SCFA) caproic acid, measured in fecal samples from the study participants. Importantly, caproic acid diminished the production of IL-32, IL-18, and IL-1β in human PBMCs in response to bacterial LPS stimulation. In conclusion, our studies identified an HIV-specific atherosclerotic inflammatory signature including specific IL-32 isoforms, which is regulated by the SCFA caproic acid and that may lead to new potential therapies to prevent CVD in ART-treated PLWH

CXCL13 as a Biomarker of Immune Activation During Early and Chronic HIV Infection

Background: CXCL13 is preferentially secreted by Follicular Helper T cells (TFH) to attract B cells to germinal centers. Plasma levels of CXCL13 have been reported to be elevated during chronic HIV-infection, however there is limited data on such elevation during early phases of infection and on the effect of ART. Moreover, the contribution of CXCL13 to disease progression and systemic immune activation have been partially defined. Herein, we assessed the relationship between plasma levels of CXCL13 and systemic immune activation.Methods: Study samples were collected in 114 people living with HIV (PLWH) who were in early (EHI) or chronic (CHI) HIV infection and 35 elite controllers (EC) compared to 17 uninfected controls (UC). A subgroup of 11 EHI who initiated ART and 14 who did not were followed prospectively. Plasma levels of CXCL13 were correlated with CD4 T cell count, CD4/CD8 ratio, plasma viral load (VL), markers of microbial translocation [LPS, sCD14, and (1→3)-β-D-Glucan], markers of B cell activation (total IgG, IgM, IgA, and IgG1-4), and inflammatory/activation markers like IL-6, IL-8, IL-1β, TNF-α, IDO-1 activity, and frequency of CD38+HLA-DR+ T cells on CD4+ and CD8+ T cells.Results: Plasma levels of CXCL13 were elevated in EHI (127.9 ± 64.9 pg/mL) and CHI (229.4 ± 28.5 pg/mL) compared to EC (71.3 ± 20.11 pg/mL), and UC (33.4 ± 14.9 pg/mL). Longitudinal analysis demonstrated that CXCL13 remains significantly elevated after 14 months without ART (p &lt; 0.001) and was reduced without normalization after 24 months on ART (p = 0.002). Correlations were observed with VL, CD4 T cell count, CD4/CD8 ratio, LPS, sCD14, (1→3)-β-D-Glucan, total IgG, TNF-α, Kynurenine/Tryptophan ratio, and frequency of CD38+HLA-DR+ CD4 and CD8 T cells. In addition, CMV+ PLWH presented with higher levels of plasma CXCL13 than CMV- PLWH (p = 0.005).Conclusion: Plasma CXCL13 levels increased with HIV disease progression. Early initiation of ART reduces plasma CXCL13 and B cell activation without normalization. CXCL13 represents a novel marker of systemic immune activation during early and chronic HIV infection and may be used to predict the development of non-AIDS events

Pandemic Influenza A (H1N1) 2009: Chest Radiographic Findings from 147 Proven Cases in the Montreal Area

AbstractObjectiveTo describe chest radiographic findings in patients with isolated and complicated acute novel influenza A (H1N1) virus infection.MethodsRetrospective study of 147 patients (64 men, mean age 41) with reverse-transcriptase polymerase chain reaction confirmed acute influenza A (H1N1) infection, who also had a chest radiograph <72 hours of viral specimen collection. Radiographs were analysed for acute findings. A correlation with bacterial cultures results was performed. The unpaired 2-sample equal-variance Student t test was applied to continuous variables and the Pearson χ2 test of association to discrete variables.ResultsIn 71% of cases, chest radiograph was normal. The presence of acute imaging findings was associated with older age (P < .05), increased number of comorbidities (most commonly, chronic obstructive pulmonary disease, diabetes, asthma) (P < .05), higher rate of hospitalization (P < .05) and intensive care unit admission, and increased mortality. Predominant acute radiographic finding in isolated influenza A (H1N1) was alveolar opacity (88%), either unifocal or multifocal, most often in the lower lobes. In the subgroup of patients with positive imaging findings and for whom nonviral microbiologic data was available, 62% had superimposed bacterial or fungal infection.ConclusionIn the majority of patients with acute influenza A (H1N1) infection, the chest radiograph is normal. Acute imaging findings are associated with older age, an increased number of comorbidities, and a higher rate of complications and mortality. The predominant radiographic finding of isolated primary influenza A (H1N1) infection is alveolar opacity. Superimposed bacterial infection is frequent and must be excluded in patients with abnormal imaging

Yield of CT pulmonary angiography in the diagnosis of acute pulmonary embolism: short report

Abstract Objective To assess computed tomography pulmonary angiography (CTPA) positive yield rate for pulmonary embolism (PE) in a Canadian academic tertiary center. Results This one-center retrospective cross-sectional study includes from 5565 (model 1) to 5296 (model 2) patients that were evaluated for suspected PE in 2015, among which 1331 (23.9% (model 1) to 25.1% (model 2)) underwent CTPA. Mean age of CTPA patients was 60.2 ± 16.6 years, of which 575 were males (43.2%). Two hundred eleven CTPA examinations were positive for PE, giving a CTPA positive yield rate of 15.9% (95% CI (13.93–17.87)). One hundred and thirteen (8.1%) CTPA were considered indeterminate, and eleven were considered nondiagnostic (0.8%). Among the 211 CTPA positive for PE, 67 (32%) were proximal emboli, 98 (47%) were segmental emboli and 44 (21%%) were subsegmental emboli. In conclusion, in this retrospective study done in a Canadian academic tertiary center, we report a positive rate of 15.9% for PE detection with CTPA, which is above the generally accepted lower threshold of 10% for the yield of CTPA