COVID-19: A qualitative chest CT model to identify severe form of the disease.

PURPOSE: The purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form. MATERIALS AND METHODS: A total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68±14 (SD) years (range: 24-96years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built: one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method. RESULTS: Central involvement of lung parenchyma (P<0.001), area of consolidation (P<0.008), air bronchogram sign (P<0.001), bronchiectasis (P<0.001), traction bronchiectasis (P<0.011), pleural effusion (P<0.026), large involvement of either one of the upper lobes or of the middle lobe (P<0.001) and total CT score≥15 (P<0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR)=2.473], central involvement [OR=2.760], pleural effusion [OR=2.699]) and the semi-quantitative model (total CT score≥15 [OR=3.342], central involvement [OR=2.344], pleural effusion [OR=2.754]) with AUC of 0.722 (95% CI: 0.638-0.806) vs. 0.739 (95% CI: 0.656-0.823), respectively (P=0.209). CONCLUSION: We have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19.

X-ray Phase Contrast osteo-articular imaging: a pilot study on cadaveric human hands.

X-ray Phase Contrast Imaging (PCI) is an emerging modality whose availability in clinics for mammography and lung imaging is expected to materialize within the coming years. In this study, we evaluate the PCI Computed Tomography (PCI-CT) performances with respect to current conventional imaging modalities in the context of osteo-articular disorders diagnosis. X-ray PCI-CT was performed on 3 cadaveric human hands and wrists using a synchrotron beam. Conventional CT, MRI and Ultrasound were also performed on these three samples using routine procedures as well as research protocols. Six radiologists and rheumatologists independently evaluated qualitatively and semi quantitatively the 3D images' quality. Medical interpretations were also made from the images. PCI-CT allows the simultaneous visualization of both the high absorbing and the softer tissues. The 6 reader evaluations characterized PCI-CT as a visualization tool with improved performances for all tissue types (significant p-values), which provides sharper outlines and clearer internal structures than images obtained using conventional modalities. The PCI-CT images contain overall more information, especially at smaller scales with for instance more visible micro-calcifications in our chondrocalcinosis case. Despite a reduced number of samples used, this pilot study highlights the possible medical benefits of PCI for osteo-articular disorders evaluation. Although PCI-CT is not yet available in hospitals, the improved visualization capabilities demonstrated so far and the enhanced tissue measurement quality let suggest strong diagnosis benefits for rheumatology in case of a widespread application of PCI.

Cohort Creation and Visualization Using Graph Model in the PREDIMED Health Data Warehouse.

Grenoble Alpes University Hospital (CHUGA) is currently deploying a health data warehouse called PREDIMED [1], a platform designed to integrate and analyze for research, education and institutional management the data of patients treated at CHUGA. PREDIMED contains healthcare data, administrative data and, potentially, data from external databases. PREDIMED is hosted by the CHUGA Information Systems Department and benefits from its strict security rules. CHUGA's institutional project PREDIMED aims to collaborate with similar projects in France and worldwide. In this paper, we present how the data model defined to implement PREDIMED at CHUGA is useful for medical experts to interactively build a cohort of patients and to visualize this cohort.

Image-free cup navigation inaccuracy: a two-study approach.

The present study tested the accuracy of an image-free navigation system used for total hip arthroplasty (THA). Two parallel, prospective studies were performed: one on real patients and the other on pelvic phantoms. We used a comparison between the intra-operative cup orientation, as displayed by the navigation system, and the post-operative cup position, as measured on CT data. The mean intrinsic overall error (+/- standard deviation) found in the phantom study was 2.6 +/- 1.1 degrees (range: 1.5-4.4 degrees ) for cup abduction, and 0.9 +/- 0.7 degrees (range: 0-2.5 degrees ) for cup anteversion. The system was less accurate in the clinical operative setting. The evaluation model was able to identify, and more importantly quantify, the clinically induced error. Ameliorating this would improve the clinical accuracy of the system.

Severity assessment of acute pulmonary embolism: role of CT angiography.

Helical CT has gained wide acceptance in the noninvasive diagnosis of acute pulmonary embolism (APE) and has therefore largely replaced conventional pulmonary angiography as well as ventilation perfusion scan in the work-up of patients suspected of nonsevere pulmonary embolism (PE). Massive PE is life-threatening; its occurrence may require aggressive treatment such as thrombolysis or embolectomy. Identification of patients suffering from major thromboembolic events based solely on clinical grounds may, however, be difficult. Acute right heart failure is the principal cause of circulatory collapse and death for patients with massive PE, and rapid and specific diagnosis and therapy are required in such patients. Bedside echocardiography, a commonly performed first-line examination, demonstrates signs of cor pulmonale, if present, and can identify large central thrombi. However, echocardiography has limitations. In this review, our goal is to discuss the potential role of CT in assessing patients with severe APE. CT evaluation is based on the direct quantification of pulmonary arterial bed obstruction using various scores and the evaluation of morphological heart changes indicating acute cor pulmonale.