Budd-Chiari Syndrome.

Budd-Chiari syndrome (BCS) is a rare disease with an incidence of 0.1 to 10 per million inhabitants a year caused by impaired venous outflow from the liver mostly at the level of hepatic veins and inferior vena cava. Etiological factors include hypercoagulable conditions, myeloprolipherative diseases, anatomical variability of the inferior vena cava, and environmental conditions. Survival rates in treated patients range from 42 to 100% depending on the etiology and the presence of risk factors including parameters of Child-Pugh score, sodium and creatinine plasma levels, and the choice of treatment. Without treatment, 90% of patients die within 3 years, mostly due to complications of liver cirrhosis. BCS can be classified according to etiology (primary, secondary), clinical course (acute, chronic, acute or chronic lesion), and morphology (truncal, radicular, and venooclusive type). The diagnosis is established by demonstrating obstruction of the venous outflow and structural changes of the liver, portal venous system, or a secondary pathology by ultrasound, computed tomography, or magnetic resonance. Laboratory and hematological tests are an integral part of the comprehensive workup and are invaluable in recognizing hematological and coagulation disorders that may be identified in up to 75% of patients with BCS. The recommended therapeutic approach to BCS is based on a stepwise algorithm beginning with medical treatment (a consensus of expert opinion recommends anticoagulation in all patients), endovascular treatment to restore vessel patency (angioplasty, stenting, and local thrombolysis), placement of transjugular portosystemic shunt (TIPS), and orthotopic liver transplantation as a last resort rescue treatment.

A Scoping Review of Military Combat Casualty Data on Submassive, Massive, and Supermassive Transfusions.

INTRODUCTION: Blood transfusions are common during combat casualty care, aiming to address the loss of blood volume that often accompanies severe battlefield injuries. This scoping review delves into the existing military combat casualty data to analyze the efficacy, challenges, and advances in the use of massive and super-massive transfusions in the management of critically injured warfighters. MATERIALS AND METHODS: We performed a scoping review of combat-related literature published between 2006 and 2023 pertaining to massive transfusions used during combat deployments. We utilized PubMed to identify relevant studies and utilized the PRISMA-ScR Checklist to conduct the review. RESULTS: We identified 53 studies that met the inclusion criteria with the majority being retrospective studies from registries used by the United States, British, French, and Dutch Militaries. Most of the studies focused on transfusion ratios, the movement of blood transfusions to more forward locations, implementation of massive transfusions with different fibrinogen-to-red blood cell ratios, the addition of recombinant factor VII, and the use of predictive models for transfusion. Lastly, we identified reports of improved survival for casualties with the rapid implementation of various blood products (warm fresh whole blood, cold-stored low titer group O blood, freeze-dried plasma, and component therapy) and literature relating to pediatric casualties and submassive transfusions. Notable findings include the establishment of hemodynamic and cell blood count parameters as predictors of the requirement for massive transfusions and the association of higher fibrinogen-to-red blood cell ratios with decreased mortality. CONCLUSIONS: We identified 53 studies focused on blood transfusions from the Global War on Terrorism conflicts. The majority were related to transfusion ratios and the movement of blood transfusions to more forward locations. We highlight key lessons learned on the battlefield that have been translated into scientific developments and changes in civilian trauma methods.

WEB vs coiling in ruptured aneurysms: A propensity score matched comparison of safety and efficacy.

BACKGROUND: We aim to compare the safety and efficacy of WEB with coiling for acutely ruptured aneurysms.METHODS: All consecutive ruptured aneurysms with width suitable for WEB (2-10 mm) treated over 5 years (1/1/2015 to 31/12/2019) were included. We recorded WFNS, Fisher grade, patient demographics and aneurysm characteristics (size, location, D/W and aspect ratio, lobulation). Primary endpoints were mRS status at 3 months, aneurysm occlusion on latest available imaging follow-up, retreatment rate and procedural complications. We applied propensity score matching using aneurysm morphology (size, D/N ratio, ASPECT ratio and lobulation) to optimise matching for WEB versus coil comparison and minimise the effects of confounding. RESULT: A total of 493 patients were identified, 97 treated with the WEB device. 1:1 propensity score matching was used to establish a matched group of 97 patients treated with coiling. The WEB arm showed 3% procedural complication rate, with no haemorrhagic complications and use of adjunctive device in 4%. Satisfactory occlusion on follow-up (mean 14 months) was 79%, with 19% retreatment rate. The coil arm had 8% complication rate, with use of an adjunctive device in 52% of cases (balloon 44%, stent 8%). Satisfactory occlusion on follow-up (mean 22 months) was 90%, with 8% retreatment rate. CONCLUSION: Treatment of ruptured wide-necked bifurcation aneurysms with WEB has a lower complication rate than coiling with high rate of satisfactory occlusion. However, there was a higher retreatment rate when compared with patients treated with coiling. An adjunct device (balloon or stent), was used in over 50% of aneurysms in the coiling group.

Synthetic PPG Signal Generation to Improve Coronary Artery Disease Classification: Study With Physical Model of Cardiovascular System.

This paper presents a novel approach of generating synthetic Photoplethysmogram (PPG) data using a physical model of the cardiovascular system to improve classifier performance with a combination of synthetic and real data. The physical model is an in-silico cardiac computational model, consisting of a four-chambered heart with electrophysiology, hemodynamic, and blood pressure auto-regulation functionality. Starting with a small number of measured PPG data, the cardiac model is used to synthesize healthy as well as PPG time-series pertaining to coronary artery disease (CAD) by varying pathophysiological parameters. A Variational Autoencoder (VAE) structure is proposed to derive a statistical feature space for CAD classification. Results are presented in two perspectives namely, (i) using artificially reduced real disease data and (ii) using all the real disease data. In both cases, by augmenting with the synthetic data for training, the performance (sensitivity, specificity) of the classifier changes from (i) (0.65, 1) to (1, 0.9) and (ii) (1, 0.95) to (1, 1). The proposed hybrid approach of combining physical modelling and statistical feature space selection generates realistic PPG data with pathophysiological interpretation and can outperform a baseline Generative Adversarial Network (GAN) architecture with a relatively small amount of real data for training. This proposed method could aid as a substitution technique for handling the problem of bulk data required for training machine learning algorithms for cardiac health-care applications.

Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy.

Wearable cardioverter defibrillator (WCD) is a life saving, wearable, noninvasive therapeutic device that prevents fatal ventricular arrhythmic propagation that leads to sudden cardiac death (SCD). WCD are frequently prescribed to patients deemed to be at high arrhythmic risk but the underlying pathology is potentially reversible or to those who are awaiting an implantable cardioverter-defibrillator. WCD is programmed to detect appropriate arrhythmic events and generate high energy shock capable of depolarizing the myocardium and thus re-initiating the sinus rhythm. WCD guidelines dictate very high reliability and accuracy to deliver timely and optimal therapy. Computational model-based process validation can verify device performance and benchmark the device setting to suit personalized requirements. In this article, we present a computational pipeline for WCD validation, both in terms of shock classification and shock optimization. For classification, we propose a convolutional neural network-"Long Short Term Memory network (LSTM) full form" (Convolutional neural network- Long short term memory network (CNN-LSTM)) based deep neural architecture for classifying shockable rhythms like Ventricular Fibrillation (VF), Ventricular Tachycardia (VT) vs. other kinds of non-shockable rhythms. The proposed architecture has been evaluated on two open access ECG databases and the classification accuracy achieved is in adherence to American Heart Association standards for WCD. The computational model developed to study optimal electrotherapy response is an in-silico cardiac model integrating cardiac hemodynamics functionality and a 3D volume conductor model encompassing biophysical simulation to compute the effect of shock voltage on myocardial potential distribution. Defibrillation efficacy is simulated for different shocking electrode configurations to assess the best defibrillator outcome with minimal myocardial damage. While the biophysical simulation provides the field distribution through Finite Element Modeling during defibrillation, the hemodynamic module captures the changes in left ventricle functionality during an arrhythmic event. The developed computational model, apart from acting as a device validation test-bed, can also be used for the design and development of personalized WCD vests depending on subject-specific anatomy and pathology.

Detection of atrial fibrillation and other abnormal rhythms from ECG using a multi-layer classifier architecture.

OBJECTIVE: Atrial fibrillation (AF) and other types of abnormal heart rhythm are related to multiple fatal cardiovascular diseases that affect the quality of human life. Hence the development of an automated robust method that can reliably detect AF, in addition to other non-sinus and sinus rhythms, would be a valuable addition to medicine. The present study focuses on developing an algorithm for the classification of short, single-lead electrocardiogram (ECG) recordings into normal, AF, other abnormal rhythms and noisy classes. APPROACH: The proposed classification framework presents a two-layer, three-node architecture comprising binary classifiers. PQRST markers are detected on each ECG recording, followed by noise removal using a spectrogram power based novel adaptive thresholding scheme. Next, a feature pool comprising time, frequency, morphological and statistical domain ECG features is extracted for the classification task. At each node of the classification framework, suitable feature subsets, identified through feature ranking and dimension reduction, are selected for use. Adaptive boosting is selected as the classifier for the present case. The training data comprises 8528 ECG recordings provided under the PhysioNet 2017 Challenge. F1 scores averaged across the three non-noisy classes are taken as the performance metric. MAIN RESULT: The final five-fold cross-validation score achieved by the proposed framework on the training data has high accuracy with low variance (0.8254 [Formula: see text] 0.0043). SIGNIFICANCE: Further, the proposed algorithm has achieved joint first place in the PhysioNet/Computing in Cardiology Challenge 2017 with a score of 0.83 computed on a hidden test dataset.

Growing number of emergency cranial CTs in patients with head injury not justified by their clinical need.

BACKGROUND: Computed tomography (CT) is widely available in most hospitals, usually 24 h a day, which results in an expansion of its indications, sometimes beyond medically justifiable extent. AIM: To evaluate trends in emergency cranial CTs in a general university hospital during the last 15 years. METHODS: We conducted a database search for emergency cranial CTs between January 2000 and December 2015 that were performed in patients after head injury on weekends and bank holidays and between 8 P.M. and 6 A.M. on workdays. The numbers were compared with demographic data, the number of hospital beds, and total number of CT examinations. RESULTS: The annual number of emergency cranial CTs increased 5.5 times from 124 to 679 with a sharp increase since 2013. This trend showed a negative correlation with the number of hospital beds (r = -0.88, p = 0.0001), the proportion of important findings on cranial CT (r = -0.74, p = 0.0010), or the proportion of patients indicated for cranial CT by NICE 2014 criteria (r = -0.90, p < 0.0001) and positive correlation with the proportion of inebriated patients (r = 0.94, p < 0.0001), and their average GCS score (r = 0.92, p < 0.0001). Compared to the number of emergency cranial CTs, the slope of regression lines for all trends was significantly different (p < 0.001) apart from the number of inebriated patients (p = 0.062). CONCLUSIONS: The increase in the emergency cranial CTs cannot be entirely justified by their clinical need. We assume that this is the result of an absent support of adherence to the guidelines in the legislation together with a medicolegally unpredictable environment.

Novel features from autocorrelation and spectrum to classify Phonocardiogram quality.

Phonocardiogram (PCG) or auscultation via a stethoscope forms the basis of preliminary medical screening. But PCG recorded in an uncontrolled environment is inherently noisy. In this paper we have derived novel features from the spectral domain and autocorrelation waveforms. These are used to identify the quality of a PCG recording and accepting only diagnosable quality recordings for further analysis. These features proved to be robust irrespective of variations in devices and in data collection protocols employed to ensure consistent data quality. A freely available, large, diverse, medical-grade PCG dataset was used for creating the training models. Results show that the proposed methodology yields an accuracy score of ~75% on our in-house PCG dataset, collected using a low-cost smartphone-based digital stethoscope.

A robust dataset-agnostic heart disease classifier from Phonocardiogram.

Automatic classification of normal and abnormal heart sounds is a popular area of research. However, building a robust algorithm unaffected by signal quality and patient demography is a challenge. In this paper we have analysed a wide list of Phonocardiogram (PCG) features in time and frequency domain along with morphological and statistical features to construct a robust and discriminative feature set for dataset-agnostic classification of normal and cardiac patients. The large and open access database, made available in Physionet 2016 challenge was used for feature selection, internal validation and creation of training models. A second dataset of 41 PCG segments, collected using our in-house smart phone based digital stethoscope from an Indian hospital was used for performance evaluation. Our proposed methodology yielded sensitivity and specificity scores of 0.76 and 0.75 respectively on the test dataset in classifying cardiovascular diseases. The methodology also outperformed three popular prior art approaches, when applied on the same dataset.

Ultra-low-dose CT Imaging of the Thorax: Decreasing the Radiation Dose by One Order of Magnitude.

Computed tomography (CT) is an indispensable tool for imaging of the thorax and there is virtually no alternative without associated radiation burden. The authors demonstrate ultra-low-dose CT of the thorax in three interesting cases. In an 18-y-old girl with rheumatoid arthritis, CT of the thorax identified alveolitis in the posterior costophrenic angles (radiation dose = 0.2 mSv). Its resolution was demonstrated on a follow-up scan (4.2 mSv) performed elsewhere. In an 11-y-old girl, CT (0.1 mSv) showed changes of the right collar bone consistent with chronic recurrent multifocal osteomyelitis. CT (0.1 mSv) of a 9-y-old girl with mucopolysaccharidosis revealed altogether three hamartomas, peribronchial infiltrate, and spine deformity. In some indications, the radiation dose from CT of the thorax can approach that of several plain radiographs. This may help the pediatrician in deciding whether "gentle" ultra-low-dose CT instead of observation or follow-up radiographs will alleviate the uncertainty of the diagnosis with little harm to the child.

Intercondylar Route of Prosthetic Infragenicular Femoropopliteal Bypass Has Better Primary, Assisted, and Secondary Patency but Not Limb Salvage Rate Compared to the Medial Route.

Aim. To compare the differences between medial and intercondylar infragenicular femoropopliteal prosthetic bypasses in terms of their midterm patency and limb salvage rates. Methods. Ninety-three consecutive patients with peripheral arterial disease who underwent a simple distal femoropopliteal bypass using a reinforced polytetrafluorethylene graft were included in this retrospective study. The bypass was constructed in the intercondylar route in 52 of the patients (group A) and in 41 in the medial route (group B). Results. Median observation time of the patients was 12.7 (IQR 4.6-18.5) months. There were 22 and 24 interventional or surgical procedures (angioplasty, stenting, thrombolysis, thrombectomy, or correction of the anastomosis) performed to restore patency of the reconstruction in groups A and B, respectively (p = 0.14). The 20-month primary, assisted, and secondary patency rates and limb salvage rates were 57%, 57%, 81%, and 80% in group A compared to 21%, 23%, 55%, and 82% in group B (p = 0.0012, 0.0052, 0.022, and 0.44, resp.). Conclusion. Despite better primary, assisted, and secondary patency rates in patients with a prosthetic infragenicular femoropopliteal bypass embedded in the intercondylar fossa compared to patients with the medial approach, there is no benefit in terms of the limb salvage rate and the number of interventions required to maintain patency of the reconstruction.

Estimating blood pressure using Windkessel model on Photoplethysmogram.

Simple and non-invasive methods to estimate vital signs are very important for preventive healthcare. In this paper, we present a methodology to estimate Blood Pressure (BP) using Photoplethysmography (PPG). Instead of directly relating systolic and diastolic BP values with PPG features, our proposed methodology initially maps PPG features with some person specific intermediate latent parameters and later derives BP values from them. The 2-Element Windkessel model has been considered in the current context to estimate total peripheral resistance and arterial compliance of a person using PPG features, followed by linear regression for simulating arterial blood pressure. Experimental results, performed on a standard hospital dataset yielded absolute errors of 0.78±13.1 mmHg and 0.59 ± 10.23 mmHg for systolic and diastolic BP values respectively. Results also indicate that the methodology is more robust than the standard methodologies that directly estimate BP values from PPG signal.

On the stability of lung parenchymal lesions with applications to early pneumothorax diagnosis.

Spontaneous pneumothorax, a prevalent medical challenge in most trauma cases, is a form of sudden lung collapse closely associated with risk factors such as lung cancer and emphysema. Our work seeks to explore and quantify the currently unknown pathological factors underlying lesion rupture in pneumothorax through biomechanical modeling. We hypothesized that lesion instability is closely associated with elastodynamic strain of the pleural membrane from pulsatile air flow and collagen-elastin dynamics. Based on the principles of continuum mechanics and fluid-structure interaction, our proposed model coupled isotropic tissue deformation with pressure from pulsatile air motion and the pleural fluid. Next, we derived mathematical instability criteria for our ordinary differential equation system and then translated these mathematical instabilities to physically relevant structural instabilities via the incorporation of a finite energy limiter. The introduction of novel biomechanical descriptions for collagen-elastin dynamics allowed us to demonstrate that changes in the protein structure can lead to a transition from stable to unstable domains in the material parameter space for a general lesion. This result allowed us to create a novel streamlined algorithm for detecting material instabilities in transient lung CT scan data via analyzing deformations in a local tissue boundary.