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.     

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.     

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.     

Preoperative Evaluation and Management of Patients With Select Chronic Gastrointestinal,Liver, and Renal Diseases

Patients with chronic gastrointestinal, hepatic, and renal disease are frequently encountered in clinical practice. This is due in part to the rising prevalence of risk factors associated with these conditions. These patients are increasingly being considered for surgical intervention and are at higher risk for multiple perioperative complications. Many are able to safely undergo surgery but require unique considerations to ensure optimal perioperative care. In this review, we highlight relevant perioperative physiology and outline our approach to the evaluation and management of patients with select chronic gastrointestinal, hepatic, and renal diseases. A comprehensive preoperative evaluation with a multidisciplinary approach is often beneficial, and specialist involvement should be considered. Intraoperative and postoperative plans should be individualized based on the unique medical and surgical characteristics of each patient.     

Incidence and Risk of Various Types of Arterial Thromboembolism in Patients With Cancer

ObjectiveTo describe the temporal association and identify risk factors between cancer diagnosis and various types of arterial thromboembolism (ATE).Patients and MethodsWe inquired an aggregated electronic health record database (Explorys, IBM Corp., Armonk, New York) and identified patients with cancer from January 1999 to October 2019, with various types of ATE, including myocardial infarction, acute ischemic stroke, acute limb ischemia, acute mesenteric ischemia, acute renal infarction, and retinal artery occlusion. We investigated the temporal relationship between cancer diagnosis and ATE events by examining the incidence ratio (IR) of ATE before and after diagnosis of cancer.ResultsWe identified 305,384 patients with cancer and ATE. The 30-day interval IR of total ATE was elevated shortly before and after cancer diagnosis, which was consistent among different ATE and cancer types. The incidence was highest within a 330-day window (90 days before and 240 days after cancer diagnosis), and IR peaked at 13.9 (95% confidence interval [CI], 13.6 to 14.2) in the first 30 days following diagnosis of cancer. Compared with patients with cancer who never developed ATE, patients with ATE had more cardiovascular risk factors at baseline. Patients with brain cancer, lung cancer, colorectal cancer, and pancreatic cancer had the highest risk of developing ATE, whereas ATE type was anatomically associated with cancer type.ConclusionIn this observational study of an aggregated US patient population, those with newly diagnosed cancer had increased risk of ATE events. This risk was most elevated in a 330-day window around cancer diagnosis and was consistent across different types of ATE and cancer.     

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.     

Longitudinal Anthropometric Measures and Risk of New-Onset Atrial Fibrillation Among Community-Dwelling Men and Women

ObjectiveTo assess the sex-specific evolution of various anthropometric measures and the association of their longitudinal trajectories with new-onset atrial fibrillation (AF).MethodsAmong 5266 men and 7218 women free of AF at baseline from the prospective population-based Rotterdam Study, each anthropometric measure was measured 1 to 5 times from 1989 to 2014. Anthropometric measures were standardized to obtain hazard ratios per 1 SD increase to enable comparison. Joint models were used to assess the longitudinal association between anthropometric measures and incident AF. Use of the joint models is a preferred method for simultaneous analyses of repeated measurements and survival data for conferring less biased estimates.ResultsMean (SD) age was 63.9 (8.9) years for men and 64.9 (9.8) years for women. Median follow-up time was 10.5 years. Longitudinal evolution of weight, height, waist circumference, hip circumference, and body mass index was associated with an increased risk of new-onset AF in both men and women. In joint models, larger height in men (hazard ratio [95% credible interval] per 1 SD, 1.27 [1.17 to 1.38]) and weight in women (1.24 [1.16 to 1.34]) showed the largest associations with AF. In joint models, waist to hip ratio was significantly associated with incident AF only in women (1.10 [1.03 to 1.18]).ConclusionConsidering the entire longitudinal trajectories in joint models, anthropometric measures were positively associated with an increased risk for new-onset AF among men and women in the general population. Increase in measure of central obesity showed a stronger association with increased risk of AF onset among women compared with men.     

Clinical Characteristics and Outcomes of Patients With Primary Plasma Cell Leukemia in the Era of Novel Agent Therapy

ObjectiveTo evaluate the clinical outcomes of patients with primary plasma cell leukemia (pPCL) defined by 5% or greater clonal circulating plasma cells on peripheral blood smear and treated with novel agent induction therapies.Patients and MethodsA cohort of 68 patients with pPCL diagnosed at the Mayo Clinic in Rochester, Minnesota, from January 1, 2000, to December 31, 2019, and treated with novel agent induction therapies was evaluated.ResultsThe median follow-up was 46 (95% CI, 41 to 90) months. The median bone marrow plasma cell content was 85% (range, 10% to 100%) and median clonal circulaitng plasma cell percentage on the peripheral blood smear was 26% (range, 5% to 93%). There was a preponderance of t(11;14) primary cytogenetic abnormality in this cohort. The median time to next therapy (TTNT) and overall survival (OS) for all patients with pPCL patients in this cohort was 13 (95% CI, 9 to 17) and 23 (95% CI, 19 to 38) months, respectively. However, when stratified by cytogenetic risk, the median TTNT and OS were 16 and 51 months for standard risk vs 9 and 19 months for high risk (P=.01 for OS).ConclusionPrimary plasma cell leukemia remains an aggressive disease with poor prognosis despite novel agent–based therapies. Some patients have better than expected survival and this phenomenon may be influenced by the absence of high-risk cytogenetics. Newer treatment regimens are needed to improve the prognosis of this devastating disease.     

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.     

Construction and Application of Chain Management Information System for Cancer Pain

BackgroundWith the rapid development of information technology, hospital information systems (HISs) have been deeply applied in the medical field and have shown broad application prospects. There are still some noninteroperable clinical information systems that pose an obstacle to the effective coordination of care, such as cancer pain management.AimTo construct a chain management information system for cancer pain and explore its clinical application effect.MethodsA quasiexperimental study was conducted in the inpatient department of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine. A total of 259 patients were nonrandomly divided into two groups: the experimental group (after the system was applied, n = 123) and the control group (before the system was applied, n = 136). The cancer pain management evaluation form score, patient satisfaction with pain control, pain score at admission and discharge, and the worst pain score during hospitalization were compared between the two groups.ResultsCompared with the control group, the score of the cancer pain management evaluation form was significantly higher (p < .05). There were no statistically significant differences in worst pain intensity, pain score at admission and discharge, and patients’ satisfaction with pain control between the two groups.ConclusionsThe cancer pain chain management information system can enable nurses to evaluate and record pain in a more standardized way, but it has no significant effect on the pain intensity of cancer patients.     

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.     

Lower Extremity Arterial Disease as a Predictor of Incident Atrial Fibrillation and Cardiovascular Events

ObjectiveTo evaluate the relationship between peripheral arterial disease (PAD) and incident atrial fibrillation (AF) and its clinical and pathophysiologic implications on ischemic stroke and all-cause mortality.Patients and MethodsWe identified all adult patients in the Mayo Clinic Health System without a previous diagnosis of AF undergoing ankle-brachial index (ABI) testing for any indication from January 1, 1996, to June 30, 2018. Retrospective extraction of ABI data and baseline echocardiographic data was performed. The primary outcome of interest was incident AF. The secondary outcomes of interest were incident ischemic stroke and all-cause mortality.ResultsA total of 33,734 patients were included in the study. After adjusting for demographic and comorbidity variables, compared with patients who had normal ABI (1.0 to 1.39), there was an increased risk of incident AF in patients with low ABI (<1.0) (adjusted hazard ratio, 1.14; 95% CI, 1.06 to 1.22) and elevated ABI (≥1.4) (adjusted hazard ratio, 1.18; 95% CI, 1.06 to 1.31). The risk was greater in patients with increasing severity of PAD. Patients with abnormal ABIs had an increased risk of ischemic stroke and all-cause mortality. We found that patients with PAD and incident AF have certain baseline echocardiographic abnormalities.ConclusionIn this large cohort of ambulatory patients undergoing ABI measurement, patients with PAD were at increased risk for incident AF, ischemic stroke, and mortality. In these high-risk patients with abnormal ABI, particularly severe PAD and cardiac structural abnormalities, routine monitoring for AF and management of cardiovascular risk factors may be warranted.     

An Electronic Health Record–Compatible Model to Predict Personalized Treatment Effects From the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real-World Data

ObjectiveTo develop an electronic health record (EHR)-based risk tool that provides point-of-care estimates of diabetes risk to support targeting interventions to patients most likely to benefit.Patients and MethodsA risk prediction model was developed and validated in a large observational database of patients with an index visit date between January 1, 2012, and December 31, 2016, with treatment effect estimates from risk-based reanalysis of clinical trial data. The risk model development cohort included 1.1 million patients with prediabetes from the OptumLabs Data Warehouse (OLDW); the validation cohort included a distinct sample of 1.1 million patients in OLDW. The randomly assigned clinical trial cohort included 3081 people from the Diabetes Prevention Program (DPP) study.ResultsEleven variables reliably obtainable from the EHR were used to predict diabetes risk. This model validated well in the OLDW (C statistic = 0.76; observed 3-year diabetes rate was 1.8% (95% confidence interval [CI], 1.7 to 1.9) in the lowest-risk quarter and 19.6% (19.4 to 19.8) in the highest-risk quarter). In the DPP, the hazard ratio (HR) for lifestyle modification was constant across all levels of risk (HR, 0.43; 95% CI, 0.35 to 0.53), whereas the HR for metformin was highly risk dependent (HR, 1.1; 95% CI, 0.61 to 2.0 in the lowest-risk quarter vs HR, 0.45; 95% CI, 0.35 to 0.59 in the highest-risk quarter). Fifty-three percent of the benefits of population-wide dissemination of the DPP lifestyle modification and 73% of the benefits of population-wide metformin therapy can be obtained by targeting the highest-risk quarter of patients.ConclusionThe Tufts–Predictive Analytics and Comparative Effectiveness DPP Risk model is an EHR-compatible tool that might support targeted diabetes prevention to more efficiently realize the benefits of the DPP interventions.     

Artificial Intelligence Application in Graves Disease: Atrial Fibrillation,Heart Failure and Menstrual Changes

ObjectiveTo study the utility of artificial intelligence (AI)–enabled electrocardiograms (ECGs) in patients with Graves disease (GD) in identifying patients at high risk of atrial fibrillation (AF) and heart failure with reduced ejection fraction (HFrEF), and to study whether AI-ECG can reflect hormonal changes and the resulting menstrual changes in GD.Patients and MethodsPatients diagnosed with GD between January 1, 2009, and December 31, 2019, were included. We considered AF diagnosed at 30 days or fewer before or any time after GD and de novo HFrEF not explained by ischemia, valve disorder, or other cardiomyopathy at/after GD diagnosis. Electrocardiograms at/after index condition were excluded. A subset analysis included females younger than 45 years of age to study the association between ECG-derived female probability and menstrual changes (shorter, lighter, or newly irregular cycles).ResultsAmong 430 patients (mean age, 50±17 years; 337 (78.4%) female), independent risk factors for AF included ECG probability of AF (hazard ratio [HR], 1.5; 95% CI, 1.2 to 1.6 per 10%; P<.001), older age (HR, 1.05; 95% CI, 1.03 to 1.07 per year; P<.001), and overt hyperthyroidism (HR, 3.9; 95% CI, 1.2 to 12.7; P=.03). The C-statistic was 0.85 for the combined model. Among 495 patients (mean age, 52±17 years; 374 (75.6%) female), independent risk factors for HFrEF were ECG probability of low ejection fraction (HR, 1.4; 95% CI, 1.1 to 1.6 per 10%; P=.001) and presence of AF (HR, 8.3; 95% CI, 2.2 to 30.9; P=.002), and a C-statistic of 0.89 for the combined model. Lastly, of 72 females younger than 45 years, 30 had menstrual changes at time of GD and had a significantly lower AI ECG–derived female probability [median 77.3; (IQR 57.9 to 94.4)% vs. median 97.7 (IQR 92.4 to 99.5)%, P<.001].ConclusionAI-enabled ECG identifies patients at risk for GD-related AF and HFrEF and was associated with menstrual changes in women with GD.     

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.