An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias

Catheter ablation is currently the only curative treatment for scar-related ventricular tachycardias (VTs). However, not only are ablation procedures long, with relatively high risk, but success rates are punitively low, with frequent VT recurrence. Personalized in-silico approaches have the opportunity to address these limitations. However, state-of-the-art reaction diffusion (R-D) simulations of VT induction and subsequent circuits used for in-silico ablation target identification require long execution times, along with vast computational resources, which are incompatible with the clinical workflow. Here, we present the Virtual Induction and Treatment of Arrhythmias (VITA), a novel, rapid and fully automated computational approach that uses reaction-Eikonal methodology to induce VT and identify subsequent ablation targets. The rationale for VITA is based on finding isosurfaces associated with an activation wavefront that splits in the ventricles due to the presence of an isolated isthmus of conduction within the scar; once identified, each isthmus may be assessed for their vulnerability to sustain a reentrant circuit, and the corresponding exit site automatically identified for potential ablation targeting. VITA was tested on a virtual cohort of 7 post-infarcted porcine hearts and the results compared to R-D simulations. Using only a standard desktop machine, VITA could detect all scar-related VTs, simulating activation time maps and ECGs (for clinical comparison) as well as computing ablation targets in 48 minutes. The comparable VTs probed by the R-D simulations took 68.5 hours on 256 cores of high-performance computing infrastructure. The set of lesions computed by VITA was shown to render the ventricular model VT-free. VITA could be used in near real-time as a complementary modality aiding in clinical decision-making in the treatment of post-infarction VTs.     

The presentation,etiologies, pathophysiology,and treatment of pulmonary renal syndrome: A review of the literature

Pulmonary renal syndrome (PRS) is a constellation of different disorders that cause both rapidly progressive glomerulonephritis and diffuse alveolar hemorrhage. While antineutrophil cytoplasmic antibody associated vasculitis and anti-glomerular basement membrane disease are the predominant causes of PRS, numerous other mechanisms have been shown to cause this syndrome, including thrombotic microangiopathies, drug exposures, and infections, among others. This syndrome has high morbidity and mortality, and early diagnosis and treatment is imperative to improve outcomes. Treatment generally involves glucocorticoids and immunosuppressive agents, but treatment targeted to the underlying disorder can improve outcomes and mitigate side effects. Familiarity with the wide range of possible causes of PRS can aid the clinician in workup, diagnosis and early initiation of treatment. This review provides a summary of the clinical presentation, etiologies, pathophysiology, and treatment of PRS.     

CEUS Versus MRI in Evaluation of the Effect of Microwave Ablation of Breast Cancer

Percutaneous microwave ablation (MWA) is a new minimally invasive technique for breast cancer treatment. The aim of this research was to compare the differences in performance between contrast-enhanced ultrasound (CEUS) and magnetic resonance imaging (MRI) with respect to the curative effect of MWA in the treatment of breast cancer. Between 2015 and 2019, 26 patients with breast cancer underwent ultrasound-guided MWA. All patients underwent both CEUS and MRI within 3 d after ablation. If either of the two imaging modalities revealed suspicious enhancement of the ablation zone and the ultrasound-guided biopsy confirmed residual tumor in the suspicious area, supplementary MWA was applied. The diagnostic efficacy of CEUS and MRI within 3 d after ablation was evaluated based on a >6-mo follow-up of 26 patients. Two cases were diagnosed with residual tumors by ultrasound that were missed by MRI. Three cases were diagnosed with residual tumors by MRI that were missed by CEUS. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of CEUS in the diagnosis of complete ablation were 100%, 40%, 87.5%, 100% and 88.5%, respectively. The sensitivity, specificity, PPV, NPV and accuracy of MRI in the diagnosis of complete ablation were 100%, 60%, 91.3%, 100% and 92.3%, respectively. Within 3 d, both CEUS and MRI can efficiently assess the efficacy of MWA of breast cancer.     

Peripheral Blood Allogeneic Stem Cell Mobilization: Can We Predict a Suboptimal Mobilization?

Allogeneic peripheral blood stem cells mobilization is now the basis of most stem cell transplants. In a very limited number of cases, mobilization is suboptimal leading to further collection procedures, to suboptimal cell doses infusion with delayed engraftment time, increased risks of transplant procedure and of related costs. To date we have no recognized and shared criteria for early estimating the probability of poor mobilization in healthy donors. We then analyzed allogeneic peripheral blood stem cell donations performed at the Fondazione Policlinico Universitario A.Gemelli IRCCS Hospital from January 2013 to December 2021 in order to identify premobilization factors associated with successful mobilization. The following data were collected: age, gender, weight, complete blood cell count at baseline, G-CSF dose, number of collection procedures, CD34+ cell count in peripheral blood on the first day of collection, CD34+ cell dose per kg body weight of recipient. Mobilization efficacy was defined according to the number of CD34+ cells in peripheral blood on day +5 of G-CSF administration. We classified donors as sub-optimal mobilizers or good mobilizers according to the achievement of the 50 CD34+ cell/μL threshold. We observed 30 suboptimal mobilizations in 158 allogeneic peripheral blood stem cell donations. Age and baseline white blood cell count were factors significantly associated with negative or positive impact on mobilization, respectively. We did not find significant differences in mobilization based on gender or G-CSF dose. Using cut-off values of 43 years and 5.5×10⁹/L WBC count, we built a suboptimal mobilization score: donors who reach 2, 1 or 0 points have a 46%, 16% or 4% probability of suboptimal mobilization, respectively. Our model explains 26% of the variability of mobilization confirming that most of the mobilization magnitude depends on genetically determined factors; however, suboptimal mobilization score is a simple tool providing an early assessment of mobilization efficacy before G-CSF administration begins in order to support allogeneic stem cells selection, mobilization and collection. Through a systematic review, we looked for confirmation of our findings. According to the published articles, all the variables we included in our model are confirmed to be strongly related to the success of mobilization. We believe that score system approach could be applied in clinical practice to assess the risk of mobilization failure at baseline allowing for a priori intervention.     

A multimodal deep learning model for cardiac resynchronisation therapy response prediction

We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the ‘nnU-Net’ segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At test time, this framework can be used with 2D echocardiography data only, whilst taking advantage of the implicit relationship between CMR and echocardiography features learnt from the model. We evaluate our pipeline on a cohort of 50 CRT patients for whom paired echocardiography/CMR data were available, and results show that the proposed multimodal classifier results in a statistically significant improvement in accuracy compared to the baseline approach that uses only 2D echocardiography data. The combination of multimodal data enables CRT response to be predicted with 77.38% accuracy (83.33% sensitivity and 71.43% specificity), which is comparable with the current state-of-the-art in machine learning-based CRT response prediction. Our work represents the first multimodal deep learning approach for CRT response prediction.     

Pain,Anxiety, and Quality of Life of COVID-19 Survivors with Myofascial Pain Syndrome: A cross sectional study

BackgroundPeople who have survived COVID-19 may develop chronic pain.AimsTo investigate the difference in pain level, anxiety, functional status, and quality of life in COVID-19 survivors with myofascial pain syndrome (MPS) in the trapezius muscle compared with MPS patients without COVID-19.DesignCross-sectional observational study.SettingsPhysical medicine and rehabilitation outpatient clinics of a single tertiary-care hospital.Participants/SubjectsEighty patients (40 patients with MPS and 40 patients with MPS + COVID) who were diagnosed with chronic MPS in the trapezius muscle were evaluated.MethodsPain level of the patients was evaluated using the visual analogue scale (VAS), the functional status with the Neck Pain and Disability scale, the psychosocial effects of the pain with the Beck Anxiety Inventory, and the quality of life with the Nottingham Health Profile tests, and the two groups (MPS and MPS + COVID) were compared.ResultsA significant difference was observed between the groups in terms of pain, anxiety, and disability (p < .001). MPS + COVID group showed significantly greater pain intensity on VAS and higher mean total scores on Nottingham Health Profile, Beck Anxiety Inventory, all Nottingham Health Profile subdomains (pain, emotional reactions, sleep, social isolation, physical mobility, energy) compared with the MPS group (p < .001).ConclusionsAfter recovering from COVID-19, patients with MPS showed increased pain, anxiety, disability, and decreased quality of life.     

Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised: the ground truth is only known for the slide, not for every single tile. In classical weakly-supervised analysis pipelines, all tiles inherit the slide label while in multiple-instance learning (MIL), only bags of tiles inherit the label. However, it is still unclear how these widely used but markedly different approaches perform relative to each other.We implemented and systematically compared six methods in six clinically relevant end-to-end prediction tasks using data from N=2980 patients for training with rigorous external validation. We tested three classical weakly-supervised approaches with convolutional neural networks and vision transformers (ViT) and three MIL-based approaches with and without an additional attention module. Our results empirically demonstrate that histological tumor subtyping of renal cell carcinoma is an easy task in which all approaches achieve an area under the receiver operating curve (AUROC) of above 0.9. In contrast, we report significant performance differences for clinically relevant tasks of mutation prediction in colorectal, gastric, and bladder cancer. In these mutation prediction tasks, classical weakly-supervised workflows outperformed MIL-based weakly-supervised methods for mutation prediction, which is surprising given their simplicity. This shows that new end-to-end image analysis pipelines in computational pathology should be compared to classical weakly-supervised methods. Also, these findings motivate the development of new methods which combine the elegant assumptions of MIL with the empirically observed higher performance of classical weakly-supervised approaches. We make all source codes publicly available at https://github.com/KatherLab/HIA, allowing easy application of all methods to any similar task.     

Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale

Accurate 3D modelling of cardiac chambers is essential for clinical assessment of cardiac volume and function, including structural, and motion analysis. Furthermore, to study the correlation between cardiac morphology and other patient information within a large population, it is necessary to automatically generate cardiac mesh models of each subject within the population. In this study, we introduce MCSI-Net (Multi-Cue Shape Inference Network), where we embed a statistical shape model inside a convolutional neural network and leverage both phenotypic and demographic information from the cohort to infer subject-specific reconstructions of all four cardiac chambers in 3D. In this way, we leverage the ability of the network to learn the appearance of cardiac chambers in cine cardiac magnetic resonance (CMR) images, and generate plausible 3D cardiac shapes, by constraining the prediction using a shape prior, in the form of the statistical modes of shape variation learned a priori from a subset of the population. This, in turn, enables the network to generalise to samples across the entire population. To the best of our knowledge, this is the first work that uses such an approach for patient-specific cardiac shape generation. MCSI-Net is capable of producing accurate 3D shapes using just a fraction (about 23% to 46%) of the available image data, which is of significant importance to the community as it supports the acceleration of CMR scan acquisitions. Cardiac MR images from the UK Biobank were used to train and validate the proposed method. We also present the results from analysing 40,000 subjects of the UK Biobank at 50 time-frames, totalling two million image volumes. Our model can generate more globally consistent heart shape than that of manual annotations in the presence of inter-slice motion and shows strong agreement with the reference ranges for cardiac structure and function across cardiac ventricles and atria.     

Applying a Bookmarking Approach to Setting Clinically Relevant Interpretive Standards for the Spinal Cord Injury–Functional Index/Capacity Basic Mobility and Self-Care Item Bank Scores

ObjectivesTo develop clinically relevant interpretive standards for the Spinal Cord Injury–Functional Index/Capacity (SCI-FI/C) Basic Mobility and Self-Care item bank scores.DesignModified “bookmarking” standard-setting methodology, including 2 stakeholder consensus meetings with individuals with spinal cord injury (SCI) and SCI clinicians, respectively, and a final, combined (consumers and clinicians) “convergence” meeting.SettingTwo SCI Model System centers in the United States.ParticipantsFourteen adults who work with individuals with traumatic SCI and 14 clinicians who work with individuals with SCI.Main Outcome MeasuresPlacement of bookmarks between vignettes based on SCI-FI Basic Mobility and Self-Care T scores. Bookmarks were placed between vignettes representing “No Problems,” “Mild Problems,” “Moderate Problems,” and “Severe Problems” for each item bank.ResultsEach consensus group resulted in a single set of scoring cut points for the SCI-FI/C Basic Mobility and Self-Care item banks. The cut points were similar but not identical between the consumer and clinician groups, necessitating a final convergence meeting. For SCI-FI/C Basic Mobility, the convergence group agreed on cut scores of 61.25 (no problems/mild problems), 51.25 (mild problems/moderate problems), and 41.25 (moderate problems/severe problems). For SCI-FI/C Self-Care, the convergence group agreed on cut scores of 56.25 (no/mild), 51.25 (mild/moderate), and 38.75 (moderate/severe).ConclusionsThe results of this study provide straightforward interpretive guidelines for SCI researchers and clinicians using the SCI-FI/C Basic Mobility and Self-Care instruments. These results are appropriate for the full bank, computer adaptive test, and short-form versions of the SCI-FI/C Basic Mobility and Self-Care item banks.     

Brain Activation of Unpleasant Emotions Increases Catastrophizing in Patients with Chronic Pain

BackgroundCatastrophic thinking among patients with chronic pain impairs their quality of life and increases anxiety levels. Further, severe pain causes high emotional brain sensitivity and unpleasant feelings. However, the effects of emotional changes on catastrophic thinking in patients with chronic pain remain unclear.AimsWe hypothesised that emotional brain activity during mild pain stimuli would affect catastrophic thinking in these patients. We aimed to examine the relationship between unpleasant emotional brain activation and catastrophic thinking due to pain stimuli in patients with chronic pain.DesignThis was a prospective observational study.ParticipantsWe included patients with chronic pain and healthy individuals.MethodsThe impact of emotional brain activity on catastrophic thinking was evaluated, specifically, the skin conductance response and oxygenated haemoglobin levels using near-infrared spectroscopy. After receiving three different pain stimuli, the participants were evaluated using the Numeric Rating Scale, Pain Catastrophising Scale, and McGill Pain Questionnaire.ResultsThere were 28 patients in the chronic pain group and 33 patients in the healthy group. There was no between-group difference in oxygenated haemoglobin levels during pain stimulation. The chronic pain group showed a higher Pain Catastrophising Scale score and skin conductance response than the healthy group (p < .05). In the chronic pain group, oxygenated haemoglobin levels after pain stimuli were significantly associated with the Pain Catastrophising Scale score and skin conductance response (p < .05).ConclusionsBrain activity of unpleasant emotions may influence catastrophic thinking in patients with chronic pain.     

Exercise Counteracts the Cardiotoxicity of Psychosocial Stress

Physical inactivity and psychosocial stress are prevalent in residents of the United States. The purpose of this article is to review the interaction between these 2 conditions and examine the effects of exercise on stress and cardiovascular (CV) health. A query of scientific references between 1974 to 2018 was performed using the PubMed search engine accessing the MEDLINE database using the search terms psychosocial stress, CV disease (CVD), physical activity, exercise, cardiac rehabilitation, and team sports. Psychosocial stress is a strong independent risk factor for adverse CV events. Conversely, people who experience CV events subsequently have drastically elevated rates of new-onset mental health disorders, including depression and anxiety. Psychosocial stress and CVD often trigger self-reinforcing feedback loops that can worsen mental health and cardiac prognosis. Exercise predictably improves CV health and prognosis and also is effective at lowering levels of psychosocial stress. Group exercise in particular seems to provide social support while at the same time boosting fitness levels and, thus, may be the single most important intervention for patients with concomitant CVD and emotional stress. Collaborative physical activity, such as group exercise, team sports, interactive physical play, and cardiac rehabilitation programs, have the potential to improve mental health and CV prognosis.     

Nebulized colistin as the adjunctive treatment for ventilator-associated pneumonia: A systematic review and meta-analysis

PurposeNebulized colistin (NC) is a potential therapy for ventilator-associated pneumonia (VAP); however, the clinical efficacy and safety of NC remain unclear. This study investigated whether NC is an effective therapy for patients with VAP.Materials and methodsWe performed a search in Web of Science, PubMed, Embase, and the Cochrane Library to retrieve randomized controlled trials (RCTs) and observational studies published at any time until February 6, 2023. The primary outcome was clinical response. Secondary outcomes included microbiological eradication, overall mortality, length of mechanical ventilation (MV), length of intensive care unit stay (ICU-LOS), nephrotoxicity, neurotoxicity, and bronchospasm.ResultsSeven observational studies and three RCTs were included. Despite exhibiting a higher microbiological eradication rate (OR,2.21; 95%CI, 1.25–3.92) and the same nephrotoxicity risk (OR,0.86; 95%CI, 0.60–1.23), NC was not significantly different in clinical response (OR,1.39; 95%CI, 0.87–2.20), overall mortality (OR,0.74; 95%CI, 0.50–1.12), MV length (mean difference (MD),-2.5; 95%CI, −5.20–0.19), and the ICU-LOS (MD,-1.91; 95%CI, −6.66–2.84) than by the intravenous antibiotic. Besides, the risk of bronchospasm raised significantly (OR, 5.19; 95%CI, 1.05–25.52) among NC.ConclusionNC was associated with better microbiological outcomes but did not result in any remarkable changes in the prognosis of patients with VAP.     

A Target Antigen–Based Approach to the Classification of Membranous Nephropathy

ObjectiveTo describe the clinical and pathological phenotype of membranous nephropathy (MN) associated with M-type-phospholipase–A₂-receptor (PLA₂R), thrombospondin-type-1-domain-containing-7A (THSD7A), semaphorin 3B (SEMA3B), neural-epidermal-growth-factor-like-1-protein (NELL-1), protocadherin 7 (PCDH7), exostosin 1/exostosin 2 (EXT1/EXT2) and neural cell adhesion molecule 1 (NCAM-1) as target antigens.MethodsA retrospective cohort of 270 adult patients with biopsy-proven MN diagnosed between January 2015 and April 2020 was classified as PLA₂R-, THSD7A-, SEMA3B-, NELL-1–, PCDH7-, EXT1/EXT2-, NCAM-1–associated or septuple-negative MN using serologic tests, immunostaining, and/or mass spectrometry. Clinical, biochemical, pathologic, and follow-up data were systematically abstracted from the medical records, including disease activity of conditions traditionally associated with MN and occurring within 5 years of MN diagnosis.ResultsPatients with PLA₂R-associated MN were predominantly middle-aged white men without associated disease. The presence of associated disease did not affect the clinical and pathologic characteristics of PLA₂R-associated MN, suggesting that they were coincidental rather than causally linked. THSD7A-, NELL-1–, PCDH7-, and NCAM-1–associated MN were rare and SEMA3B-associated MN was not discovered in our cohort. EXT1/EXT2-associated MN was primarily diagnosed in younger women with active systemic autoimmunity. A significant proportion of septuple-negative patients had associated malignancy or systemic autoimmunity.ConclusionThe widely used distinction between primary and secondary MN has limitations. We propose a refined terminology that combines the target antigen and associated disease to better classify MN and guide clinical decision making.