Stability over time of immature platelet fraction and comparison between EDTA and citrated whole blood samples.

BACKGROUND: Immature platelets (IP) are the youngest circulating platelets, released from megakaryocytes, and demonstrating increased dimensions, significant RNA content, and enhanced activity. Immature platelet research focuses on a differential diagnostic help in patients with thrombocytopenia. The objectives of this study were to compare the variability of IP in citrate and EDTA samples, and to determine stability over time. METHODS: Fifty-six patients were included for comparison between EDTA and citrate whole blood sample collection. Among the patients, 28 had thrombocytopenia (platelet count < 150G/L). Platelet measurement impedancemetry and fluorimetry were performed with Sysmex XN-9000. The immature platelet fraction (IPF) and absolute immature platelet count (A-IPC) were determined with a fluorescent method. RESULTS: The mean value of platelet count with fluorescence was, in EDTA sample, 215 ± 171 and, in citrate sample, 153 ± 118 G/L. No significant difference was observed between IPF between EDTA and citrate (7.74 ± 6.68% vs. 8.45 ± 7.37%, p = 0.69), respectively. With the Bland-Altman analysis, the mean difference in the EDTA sample, between 1 and 24 h, was 8.06 ± 6.96% and 8.73 ± 7.12% for IPF, whereas in the citrate sample, between 1 and 6 h, it was 8.60 ± 7.29% and 7.54 ± 6.97%, for IPF. Comparing 1 h EDTA sample with 6 h citrate sample, the variance ratio was 0.974 (95% CI: 0.864-1.084) in IPF. CONCLUSIONS: We confirmed the potential to conduct IP measurements up to 24 h in the EDTA sample and IPF measurements in the citrate sample for up to 6 h. These results may be useful for the use of IPF, which is a promising parameter whose interest in clinical practice and standardization is not yet well defined.

Automatic Detection of Pancreatic Lesions and Main Pancreatic Duct Dilatation on Portal Venous CT Scans Using Deep Learning.

OBJECTIVES: This study proposes and evaluates a deep learning method to detect pancreatic neoplasms and to identify main pancreatic duct (MPD) dilatation on portal venous computed tomography scans. MATERIALS AND METHODS: A total of 2890 portal venous computed tomography scans from 9 institutions were acquired, among which 2185 had a pancreatic neoplasm and 705 were healthy controls. Each scan was reviewed by one in a group of 9 radiologists. Physicians contoured the pancreas, pancreatic lesions if present, and the MPD if visible. They also assessed tumor type and MPD dilatation. Data were split into a training and independent testing set of 2134 and 756 cases, respectively.A method to detect pancreatic lesions and MPD dilatation was built in 3 steps. First, a segmentation network was trained in a 5-fold cross-validation manner. Second, outputs of this network were postprocessed to extract imaging features: a normalized lesion risk, the predicted lesion diameter, and the MPD diameter in the head, body, and tail of the pancreas. Third, 2 logistic regression models were calibrated to predict lesion presence and MPD dilatation, respectively. Performance was assessed on the independent test cohort using receiver operating characteristic analysis. The method was also evaluated on subgroups defined based on lesion types and characteristics. RESULTS: The area under the curve of the model detecting lesion presence in a patient was 0.98 (95% confidence interval [CI], 0.97-0.99). A sensitivity of 0.94 (469 of 493; 95% CI, 0.92-0.97) was reported. Similar values were obtained in patients with small (less than 2 cm) and isodense lesions with a sensitivity of 0.94 (115 of 123; 95% CI, 0.87-0.98) and 0.95 (53 of 56, 95% CI, 0.87-1.0), respectively. The model sensitivity was also comparable across lesion types with values of 0.94 (95% CI, 0.91-0.97), 1.0 (95% CI, 0.98-1.0), 0.96 (95% CI, 0.97-1.0) for pancreatic ductal adenocarcinoma, neuroendocrine tumor, and intraductal papillary neoplasm, respectively. Regarding MPD dilatation detection, the model had an area under the curve of 0.97 (95% CI, 0.96-0.98). CONCLUSIONS: The proposed approach showed high quantitative performance to identify patients with pancreatic neoplasms and to detect MPD dilatation on an independent test cohort. Performance was robust across subgroups of patients with different lesion characteristics and types. Results confirmed the interest to combine a direct lesion detection approach with secondary features such as the MPD diameter, thus indicating a promising avenue for the detection of pancreatic cancer at early stages.

Efficacy and safety of convalescent plasma to treat hospitalised COVID-19 patients with or without underlying immunodeficiency: a randomized clinical trial

Efficacy of COVID-19 convalescent plasma (CCP) in COVID-19 pneumonia is uncertain. The CORIPLASM study was an open-label, Bayesian randomised clinical trial evaluating the efficacy of CCP in patients with moderate COVID-19, including immunocompromised patients. Patients hospitalised with COVID-19 and less than 9 days since symptoms onset were assigned to receive 4 units of plasma over 2 days ({approx} 840 ml)(CCP) or usual care alone (UC). Primary outcomes were the proportion of patients with a WHO-Clinical Progression Score (CPS) [≥]6 on the 10-point scale on day (d) 4 and survival without ventilation or additional immunomodulatory treatment by d14. A total of 120 patients were recruited and assigned to CCP (n=60) or UC (n=60), including 22 (CCP) and 27 (UC) immunocompromised patients. Thirteen (22%) patients with CCP had a WHO-CPS [≥]6 at d4 versus 8 (13%) with UC, adjusted odds ratio (aOR) 1.88 [95%CI 0.71 to 5.24]. By d14, 19 (31.6%) patients with CCP and 20 (33.3%) patients with UC had ventilation, additional immunomodulatory treatment or had died. Cumulative incidence of death was 3 (5%) with CCP and 8 (13%) with UC at d14 (aHR 0.40 [95%CI 0{middle dot}10 -1{middle dot}53]), and 7 (12%) with CCP and 12 (20%) with UC at d28 (aHR 0.51 [95%CI 0.20-1.32]). Subgroup analysis indicated that CCP might be associated with a lower mortality in immunocompromised patients (HR 0.37 [95%CI 0.14-0.97]). CCP treatment did not improve early outcomes in patients with moderate COVID-19 but was associated with reduced mortality in the subgroup of immunocompromised patients.

From Dose Reduction to Contrast Maximization: Can Deep Learning Amplify the Impact of Contrast Media on Brain Magnetic Resonance Image Quality? A Reader Study.

OBJECTIVES: The aim of this study was to evaluate a deep learning method designed to increase the contrast-to-noise ratio in contrast-enhanced gradient echo T1-weighted brain magnetic resonance imaging (MRI) acquisitions. The processed images are quantitatively evaluated in terms of lesion detection performance. MATERIALS AND METHODS: A total of 250 multiparametric brain MRIs, acquired between November 2019 and March 2021 at Gustave Roussy Cancer Campus (Villejuif, France), were considered for inclusion in this retrospective monocentric study. Independent training (107 cases; age, 55 ± 14 years; 58 women) and test (79 cases; age, 59 ± 14 years; 41 women) samples were defined. Patients had glioma, brain metastasis, meningioma, or no enhancing lesion. Gradient echo and turbo spin echo with variable flip angles postcontrast T1 sequences were acquired in all cases. For the cases that formed the training sample, "low-dose" postcontrast gradient echo T1 images using 0.025 mmol/kg injections of contrast agent were also acquired. A deep neural network was trained to synthetically enhance the low-dose T1 acquisitions, taking standard-dose T1 MRI as reference. Once trained, the contrast enhancement network was used to process the test gradient echo T1 images. A read was then performed by 2 experienced neuroradiologists to evaluate the original and processed T1 MRI sequences in terms of contrast enhancement and lesion detection performance, taking the turbo spin echo sequences as reference. RESULTS: The processed images were superior to the original gradient echo and reference turbo spin echo T1 sequences in terms of contrast-to-noise ratio (44.5 vs 9.1 and 16.8; P < 0.001), lesion-to-brain ratio (1.66 vs 1.31 and 1.44; P < 0.001), and contrast enhancement percentage (112.4% vs 85.6% and 92.2%; P < 0.001) for cases with enhancing lesions. The overall image quality of processed T1 was preferred by both readers (graded 3.4/4 on average vs 2.7/4; P < 0.001). Finally, the proposed processing improved the average sensitivity of gradient echo T1 MRI from 88% to 96% for lesions larger than 10 mm ( P = 0.008), whereas no difference was found in terms of the false detection rate (0.02 per case in both cases; P > 0.99). The same effect was observed when considering all lesions larger than 5 mm: sensitivity increased from 70% to 85% ( P < 0.001), whereas false detection rates remained similar (0.04 vs 0.06 per case; P = 0.48). With all lesions included regardless of their size, sensitivities were 59% and 75% for original and processed T1 images, respectively ( P < 0.001), and the corresponding false detection rates were 0.05 and 0.14 per case, respectively ( P = 0.06). CONCLUSION: The proposed deep learning method successfully amplified the beneficial effects of contrast agent injection on gradient echo T1 image quality, contrast level, and lesion detection performance. In particular, the sensitivity of the MRI sequence was improved by up to 16%, whereas the false detection rate remained similar.

Can Deep Learning Replace Gadolinium in Neuro-Oncology?: A Reader Study.

MATERIALS AND METHODS: This monocentric retrospective study leveraged 200 multiparametric brain MRIs acquired between November 2019 and February 2020 at Gustave Roussy Cancer Campus (Villejuif, France). A total of 145 patients were included: 107 formed the training sample (55 ± 14 years, 58 women) and 38 the separate test sample (62 ± 12 years, 22 women). Patients had glioma, brain metastases, meningioma, or no enhancing lesion. T1, T2-FLAIR, diffusion-weighted imaging, low-dose, and standard-dose postcontrast T1 sequences were acquired. A deep network was trained to process the precontrast and low-dose sequences to predict "virtual" surrogate images for contrast-enhanced T1. Once trained, the deep learning method was evaluated on the test sample. The discrepancies between the predicted virtual images and the standard-dose MRIs were qualitatively and quantitatively evaluated using both automated voxel-wise metrics and a reader study, where 2 radiologists graded image qualities and marked all visible enhancing lesions. RESULTS: The automated analysis of the test brain MRIs computed a structural similarity index of 87.1% ± 4.8% between the predicted virtual sequences and the reference contrast-enhanced T1 MRIs, a peak signal-to-noise ratio of 31.6 ± 2.0 dB, and an area under the curve of 96.4% ± 3.1%. At Youden's operating point, the voxel-wise sensitivity (SE) and specificity were 96.4% and 94.8%, respectively. The reader study found that virtual images were preferred to standard-dose MRI in terms of image quality (P = 0.008). A total of 91 reference lesions were identified in the 38 test T1 sequences enhanced with full dose of contrast agent. On average across readers, the brain lesion SE of the virtual images was 83% for lesions larger than 10 mm (n = 42), and the associated false detection rate was 0.08 lesion/patient. The corresponding positive predictive value of detected lesions was 92%, and the F1 score was 88%. Lesion detection performance, however, dropped when smaller lesions were included: average SE was 67% for lesions larger than 5 mm (n = 74), and 56% with all lesions included regardless of their size. The false detection rate remained below 0.50 lesion/patient in all cases, and the positive predictive value remained above 73%. The composite F1 score was 63% at worst. CONCLUSIONS: The proposed deep learning method for virtual contrast-enhanced T1 brain MRI prediction showed very high quantitative performance when evaluated with standard voxel-wise metrics. The reader study demonstrated that, for lesions larger than 10 mm, good detection performance could be maintained despite a 4-fold division in contrast agent usage, unveiling a promising avenue for reducing the gadolinium exposure of returning patients. Small lesions proved, however, difficult to handle for the deep network, showing that full-dose injections remain essential for accurate first-line diagnosis in neuro-oncology.

Novel threshold value of midnight serum cortisol for diagnosis of hypercortisolism using the Roche Cortisol II assay.

BACKGROUND: The diagnosis of hypercortisolism requires multiple biochemical investigations, due to variations in cortisol production during the 24-hour circadian cycle. Midnight serum cortisol is difficult to interpret since the threshold value is dependent on the analytical method used and is often not provided by the manufacturer. Second-generation assays are more specific than first-generation assays and may have lower threshold values. OBJECTIVES: The aim of this study was to determine a novel threshold value of midnight serum cortisol for the biochemical diagnosis of hypercortisolism, using the Roche Cobas Cortisol® second-generation assay. METHODS: This study was performed in adult patients hospitalized in the endocrinology unit of a university hospital. Patients had a complete assessment of their 24-hour cortisol cycle, i.e., a serum cortisol test every four hours and at least two first-line tests: late night salivary cortisol, dexamethasone suppression test and/or 24-hour urinary free cortisol. First-line tests were used to identify patients with hypercortisolism. Serum samples were analyzed by second-generation electrochemiluminescence immunoassays (ECLIA) from Roche Cobas Cortisol®. RESULTS: Midnight serum cortisol samples were obtained from 175 hospitalized patients. The novel threshold value obtained was 157 nmol/L with a sensitivity of 82.9% (68.6 to 94.3%) and a specificity of 90.0% (85.0 to 95.0%). CONCLUSION: Our study confirms that the threshold value of midnight serum cortisol is not comparable between first- and second-generation Roche Cobas Cortisol® assays and that the threshold value is lower with the second-generation assay.

Immune escape of SARS-CoV-2 Omicron variant from mRNA vaccination-elicited RBD-specific memory B cells.

Memory B cells (MBCs) represent a second layer of immune protection against SARS-CoV-2. Whether MBCs elicited by mRNA vaccines can recognize the Omicron variant is of major concern. We used bio-layer interferometry to assess the affinity against the receptor-binding-domain (RBD) of Omicron spike of 313 naturally expressed monoclonal IgG that were previously tested for affinity and neutralization against VOC prior to Omicron. We report here that Omicron evades recognition from a larger fraction of these antibodies than any of the previous VOCs. Additionally, whereas 30% of these antibodies retained high affinity against Omicron-RBD, our analysis suggest that Omicron specifically evades antibodies displaying potent neutralizing activity against the D614G and Beta variant viruses. Further studies are warranted to understand the consequences of a lower memory B cell potency on the overall protection associated with current vaccines.

Memory B cells control SARS-CoV-2 variants upon mRNA vaccination of naive and COVID-19 recovered individuals.

How a previous SARS-CoV-2 infection may amplify and model the memory B cell (MBC) response elicited by mRNA vaccines was addressed by a comparative longitudinal study of two cohorts, naive individuals and disease-recovered patients, up to 2 months after vaccination. The quality of the memory response was assessed by analysis of the VDJ repertoire, affinity and neutralization against variants of concerns (VOC), using unbiased cultures of 2452 MBCs. Upon boost, the MBC pool of recovered patients selectively expanded, further matured and harbored potent neutralizers against VOC. Maturation of the MBC response in naive individuals was much less pronounced. Nevertheless, and as opposed to their weaker neutralizing serum response, half of their RBD-specific MBCs displayed high affinity towards multiple VOC and one-third retained neutralizing potency against B.1.351. Thus, repeated vaccine challenges could reduce these differences by recall of affinity-matured MBCs and allow naive vaccinees to cope efficiently with VOC.

Development and Implementation of a scalable and versatile test for COVID-19 diagnostics in rural communities

Rapid and widespread testing of severe acute respiratory coronavirus 2 (SARS-CoV-2) is essential for an effective public health response aimed at containing and mitigating the coronavirus disease 2019 (COVID-19) pandemic. Successful health policy implementation relies on early identification of infected individuals and extensive contact tracing.However, rural communities, where resources for testing are sparse or simply absent, face distinctive challenges to achieving this success. Accordingly, we report the development of an academic, public land grant University laboratory-based detection assay for the identification of SARS-CoV-2 in samples from various clinical specimens that can be readily deployed in areas where access to testing is limited. The test, which is a quantitative reverse transcription polymerase chain reaction (RT-qPCR)-based procedure, was validated on samples provided by the state laboratory and submitted for FDA Emergency Use Authorization. Our test exhibits comparable sensitivity and exceeds specificity and inclusivity values compared to other molecular assays. Additionally, this test can be re-configured to meet supply chain shortages, modified for scale up demands, and is amenable to several clinical specimens. Test development also involved 3D engineering critical supplies and formulating a stable collection media that allowed samples to be transported for hours over a dispersed rural region without the need for a cold-chain. These two elements that were critical when shortages impacted testing and when personnel needed to reach areas that were geographically isolated from the testing center. Overall, using a robust, easy-to-adapt methodology, we show that an academic laboratory can supplement COVID-19 testing needs and help local health departments assess and manage outbreaks. This additional testing capacity is particularly germane for smaller cities and rural regions that would otherwise be unable to meet the testing demand.

CHD2-Related CNS Pathologies.

Epileptic encephalopathies (EE) are severe epilepsy syndromes characterized by multiple seizure types, developmental delay and even regression. This class of disorders are increasingly being identified as resulting from de novo genetic mutations including many identified mutations in the family of chromodomain helicase DNA binding (CHD) proteins. In particular, several de novo pathogenic mutations have been identified in the gene encoding chromodomain helicase DNA binding protein 2 (CHD2), a member of the sucrose nonfermenting (SNF-2) protein family of epigenetic regulators. These mutations in the CHD2 gene are causative of early onset epileptic encephalopathy, abnormal brain function, and intellectual disability. Our understanding of the mechanisms by which modification or loss of CHD2 cause this condition remains poorly understood. Here, we review what is known and still to be elucidated as regards the structure and function of CHD2 and how its dysregulation leads to a highly variable range of phenotypic presentations.

No evidence of clinical efficacy of hydroxychloroquine in patients hospitalized for COVID-19 infection with oxygen requirement: results of a study using routinely collected data to emulate a target trial

BackgroundTreatments are urgently needed to prevent respiratory failure and deaths from coronavirus disease 2019 (COVID-19). Hydroxychloroquine (HCQ) has received worldwide attention because of positive results from small studies. MethodsWe used data collected from routine care of all adults in 4 French hospitals with documented SARS-CoV-2 pneumonia and requiring oxygen [≥] 2 L/min to emulate a target trial aimed at assessing the effectiveness of HCQ at 600 mg/day. The composite primary endpoint was transfer to intensive care unit (ICU) within 7 days from inclusion and/or death from any cause. Analyses were adjusted for confounding factors by inverse probability of treatment weighting. ResultsThis study included 181 patients with SARS-CoV-2 pneumonia; 84 received HCQ within 48 hours of admission (HCQ group) and 97 did not (no-HCQ group). Initial severity was well balanced between the groups. In the weighted analysis, 20.2% patients in the HCQ group were transferred to the ICU or died within 7 days vs 22.1% in the no-HCQ group (16 vs 21 events, relative risk [RR] 0.91, 95% CI 0.47-1.80). In the HCQ group, 2.8% of the patients died within 7 days vs 4.6% in the no-HCQ group (3 vs 4 events, RR 0.61, 95% CI 0.13-2.89), and 27.4% and 24.1%, respectively, developed acute respiratory distress syndrome within 7 days (24 vs 23 events, RR 1.14, 95% CI 0.65-2.00). Eight patients receiving HCQ (9.5%) experienced electrocardiogram modifications requiring HCQ discontinuation. InterpretationThese results do not support the use of HCQ in patients hospitalised for documented SARS-CoV-2-positive hypoxic pneumonia.

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

Safety and feasibility of endovascular aortic aneurysm repair as day surgery.

OBJECTIVE: The adoption of endovascular aneurysm repair (EVAR) during the past two decades has led to significantly shorter length of stay as well as lower hospital resource use. Currently, most patients are admitted to the hospital after EVAR; however, there are no standard observation periods, and timing of discharge is based on clinical judgment. The aim of this study was to confirm the safety and feasibility of performing EVAR as outpatient surgery. METHODS: We developed criteria to identify patients for potential same-day discharge (infrarenal aneurysm, low perioperative risk, to be accompanied for first 24 hours). We then implemented a prospective trial that observed patients planned for same-day discharge and compared them with a historical control group (patients who had undergone EVAR during the previous 2 years and met same-day discharge criteria). Basic demographic and operative data as well as length of stay, inpatient and perioperative complications, emergency department visits, readmissions, reinterventions, and deaths were collected. The primary outcome was the 30-day complication rate, and the study was powered to assess noninferiority. RESULTS: Prospectively, we assessed 266 patients and planned 110 (41%) for outpatient EVAR (62% of historical controls met outpatient criteria). Demographic characteristics were similar between planned outpatients and historical controls. In planned outpatients, hospital stay was significantly shorter (0.7 ± 2.6 days vs 2.5 ± 6.9 days; P < .01), and 79% were discharged the same day of surgery. The 30-day follow-up was available for all study patients and 94% of control patients; there were no differences in complication (11% vs 9%), readmission (2% vs 4%), reintervention (4% vs 4%), or mortality (1% vs 1%) rates, but study patients had significantly more emergency department visits (15% vs 6%; P < .05). Unsuccessful same-day discharge was associated with longer operative times, increased blood loss, and use of general anesthesia. CONCLUSIONS: In selected patients undergoing elective EVAR, same-day discharge is feasible without increasing complication rates. Health resource utilization remains a challenge in transitioning to an outpatient model.

Low-dimensional representation of cardiac motion using Barycentric Subspaces: A new group-wise paradigm for estimation, analysis, and reconstruction.

One major challenge when trying to build low-dimensional representation of the cardiac motion is its natural circular pattern during a cycle, therefore making the mean image a poor descriptor of the whole sequence. Therefore, traditional approaches for the analysis of the cardiac deformation use one specific frame of the sequence - the end-diastolic (ED) frame - as a reference to study the whole motion. Consequently, this methodology is biased by this empirical choice. Moreover, the ED image might be a poor reference when looking at large deformation for example at the end-systolic (ES) frame. In this paper, we propose a novel approach to study cardiac motion in 4D image sequences using low-dimensional subspace analysis. Instead of building subspaces relying on a mean value we use a novel type of subspaces called Barycentric Subspaces which are implicitly defined as the weighted Karcher means of k+1 reference images instead of being defined with respect to one reference image. In the first part of this article, we introduce the methodological framework and the algorithms used to manipulate images within these new subspaces: how to compute the projection of a given image on the Barycentric Subspace with its coordinates, and the opposite operation of computing an image from a set of references and coordinates. Then we show how this framework can be applied to cardiac motion problems and lead to significant improvements over the single reference method. Firstly, by computing the low-dimensional representation of two populations we show that the parameters extracted correspond to relevant cardiac motion features leading to an efficient representation and discrimination of both groups. Secondly, in motion estimation, we use the projection on this low-dimensional subspace as an additional prior on the regularization in cardiac motion tracking, efficiently reducing the error of the registration between the ED and ES by almost 30%. We also derive a symmetric and transitive formulation of the registration that can be used both for frame-to-frame and frame-to-reference registration. Finally, we look at the reconstruction of the images using our proposed low-dimensional representation and show that this multi-references method using Barycentric Subspaces performs better than traditional approaches based on a single reference.

Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1.

A National Survey on Teaching and Assessing Technical Proficiency in Vascular Surgery in Canada.

BACKGROUND: This survey aims to explore trainees' perspectives on how Canadian vascular surgery training programs are using simulation in teaching and assessing technical skills through a cross-sectional national survey. METHODS: A 10-min online questionnaire was sent to Program Directors of Canada's Royal College of Physicians and Surgeons' of Canada approved training programs in vascular surgery. This survey was distributed among residents and fellows who were studying in the 2013-2014 academic year. RESULTS: Twenty-eight (58%) of the 48 Canadian vascular surgery trainees completed the survey. A total of 68% of the respondents were part of the 0 + 5 integrated vascular surgery training program. The use of simulation in the assessment of technical skills at the beginning of training was reported by only 3 (11%) respondents, whereas 43% reported that simulation was used in their programs in the assessment of technical skills at some time during their training. Training programs most often provided simulation as a method of teaching and learning endovascular abdominal aortic or thoracic aneurysm repair (64%). Furthermore, 96% of trainees reported the most common resource to learn and enhance technical skills was dialog with vascular surgery staff. CONCLUSIONS: Surveyed vascular surgery trainees in Canada report that simulation is rarely used as a tool to assess baseline technical skills at the beginning of training. Less than half of surveyed trainees in vascular surgery programs in Canada report that simulation is being used for skills acquisition. Currently, in Canadian training programs, simulation is most commonly used to teach endovascular skills.

Heparin-induced thrombocytopenia causing graft thrombosis and bowel ischemia postendovascular aneurysm repair.

Heparin-induced thrombocytopenia (HIT) is an immune-mediated thrombocytopenia resulting from prior heparin exposure. It can be associated with limb- or life-threatening thrombotic events. Patients undergoing any vascular procedures including endovascular procedures that require heparin administration are at risk. There is very little reported in the literature with regards to thrombosis associated with HIT after endovascular aortic aneurysm repair. All reported cases of HIT thrombosis presented as acute arterial lower limb ischemia or deep vein thrombosis. In this report, we present a case of HIT complicated by stent graft thrombosis and bowel ischemia.

Age-related differences according to the associative deficit and the environmental support hypotheses: an application of the formal charm associative memory model.

UNLABELLED: BACKGROUND/STUDY CONTEXT: According to both the associative deficit hypothesis (ADH; Naveh-Benjamin, 2000 , Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 1170-1187) and the environmental support hypothesis (ESH; Craik, 1983 , Philosophical Transactions of the Royal Society of London Series B, 302, 354-359), memory decline with aging can be seen as an impairment of the self-initiated associative memory processes such that supportive encoding and/or retrieval can reduce age-related differences. A formalization of relationships between the ADH and ESH was investigated using the distributed memory model "CHARM" (Composite Holographic Associative Recall-Recognition Model; Metcalfe, 1982 , Psychological Review, 89, 627-661; Metcalfe, 1991 , Psychological Review, 98, 529-543). METHODS: Empirical data were collected in young and elderly participants on cued recall and recognition tests according to both the level of processing (LOP: phonetic vs. semantic tasks) and the self-generated cueing (elaboration effect: provided vs. self-generated cue) manipulation. These data were compared with those from CHARM simulations that were designed to evaluate the impact of deteriorated associative processes (i.e., ADH) and the role of LOP and elaboration effects (i.e., ESH) in memory performance. RESULTS: The simulated data were largely consistent with the empirical data, showing that the impairment of associative processes in the CHARM model was accompanied by an increased need for environmental support at encoding (interaction between age, LOP, and elaboration) to reduce memory decline in cued recall tasks, which is somewhat observed in the recognition scores. CONCLUSION: The overall results from CHARM simulations are in accordance with both the ADH and ESH hypotheses and provide discussion on the formal connections between these two main aging explanations.

Midterm results following endovascular repair of blunt thoracic aortic injuries.

OBJECTIVE: Previous studies have focused on early outcomes of thoracic endovascular repair (TEVAR) of blunt thoracic aortic injuries (BTAIs). Late results remain ill-defined. The purpose of this study is to review the midterm results of our experience with endovascular repair of BTAIs. METHODS: A retrospective analysis was performed reviewing all endovascular repairs of BTAIs from 2002 to present. Preoperative, operative, and postoperative variables were recorded. Clinical end points included aortic-related mortality, stroke and paraplegia, hospital length of stay, procedure-related complications, endoleaks, and reinterventions. Computed tomography data sets were postprocessed for assessing integrity of stent grafts and late complications. RESULTS: A total of 24 cases of BTAIs treated with TEVAR were identified. Thoracic endovascular repair was successful in treating BTAIs in all patients and there were no instances of procedure-related death, stroke, or paraplegia. One access complication occurred, requiring an iliofemoral bypass. Actuarial survival estimates and freedom from reintervention at 5 years were 88.7% and 95.8%, respectively. No late endoleaks, stent fractures, or device migration were identified. One patient required a secondary intervention 1 year following the initial repair to treat a pseudocoarctation syndrome caused by a diaphragm at the distal half of the stented aorta. This was treated successfully with repeated endografting. CONCLUSIONS: Thoracic endovascular repair for BTAIs can be performed safely with low periprocedural mortality and morbidity. Midterm follow-up data presented in this report further support the therapeutic role of endoluminal approach for treating BTAIs in anatomically suitable patients.

Paraplegia prevention branches: a new adjunct for preventing or treating spinal cord injury after endovascular repair of thoracoabdominal aneurysms.

In this report, we describe a technique that could potentially be used for both prevention and treatment of spinal cord ischemia (SCI) in endovascular repair of thoracoabdominal aneurysms. This technique involves using a specially designed endograft with side branches (paraplegia prevention branches [PPBs]), which are left patent to perfuse the aneurysmal sac and any associated lumbar or intercostal arteries in the early postoperative period. The use of PPBs with this technique is feasible and allows for a temporary controlled endoleak that may be useful for preventing or reversing spinal cord injury. This technique may be considered as an adjunct to the more standard perioperative physiological manipulations such as permissive hypertension and spinal fluid drainage.