Left ventricular T2 distribution in Duchenne Muscular Dystrophy

<p>Abstract</p> <p>Background</p> <p>Although previous studies have helped define the natural history of Duchenne Muscular Dystrophy (DMD)-associated cardiomyopathy, the myocardial pathobiology associated with functional impairment in DMD is not yet known.</p> <p>The objective of this study was to assess the distribution of transverse relaxation time (T2) in the left ventricle (LV) of DMD patients, and to determine the association of myocardial T2 heterogeneity to the severity of cardiac dysfunction. DMD patients (n = 26) and normal control subjects (n = 13) were studied by Cardiovascular Magnetic Resonance (CMR). DMD subject data was stratified based on subject age and LV Ejection Fraction (EF) into the following groups: A (<12 years old, n = 12); B (≥12 years old, EF ≤ 55%, n = 8) and C (≥12 years old, EF = 55%, n = 6). Controls were also stratified by age into Groups N1 (<12 years, n = 6) and N2 (>12 years, n = 5). LV mid-slice circumferential myocardial strain (ε_cc) was calculated using tagged CMR imaging. T2 maps of the LV were generated for all subjects using a black blood dual spin echo method at two echo times. The Full Width at Half Maximum (<it>FWHM</it>) was calculated from a histogram of LV T2 distribution constructed for each subject.</p> <p>Results</p> <p>In DMD subject groups, <it>FWHM </it>of the T2 histogram rose progressively with age and decreasing EF (Group A <it>FWHM</it>= 25.3 ± 3.8 ms; Group B <it>FWHM</it>= 30.9 ± 5.3 ms; Group C <it>FWHM</it>= 33.0 ± 6.4 ms). Further, <it>FWHM </it>was significantly higher in those with reduced circumferential strain (|ε_cc| ≤ 12%) (Group B, and C) than those with |ε_cc| > 12% (Group A). Group A <it>FWHM </it>was not different from the two normal groups (N1 <it>FWHM </it>= 25.3 ± 3.5 ms; N2 <it>FWHM</it>= 24.0 ± 7.3 ms).</p> <p>Conclusion</p> <p>Reduced EF and ε_cccorrelates well with increased T2 heterogeneity quantified by <it>FWHM</it>, indicating that subclinical functional impairments could be associated with pre-existing abnormalities in tissue structure in young DMD patients.</p

Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models

Background: Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRCML) and compares against statistically derived CVRC (CVRCStat) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors). Methods: The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRCStat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRCStat, the patients were again risk-stratified using CVRCML based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events. Results: An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRCStat calculators were: AECRS2.0 (AUC 0.83, P&lt;0.001), QRISK3 (AUC 0.72, P&lt;0.001), WHO (AUC 0.70, P&lt;0.001), ASCVD (AUC 0.67, P&lt;0.001), FRScardio (AUC 0.67, P&lt;0.01), FRSstroke (AUC 0.64, P&lt;0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P&lt;0.001), NIPPON (AUC 0.63, P&lt;0.001), PROCAM (AUC 0.59, P&lt;0.001), RRS (AUC 0.57, P&lt;0.001), UKPDS60 (AUC 0.53, P&lt;0.001), and SCORE (AUC 0.45, P&lt;0.001), while the AUC for the CVRCML with integrated risk factors (AUC 0.88, P&lt;0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRCML with integrated risk factors was 92.52% which was higher compared all the other CVRCStat. Conclusions: ML-based CVD/stroke risk calculator provided a higher predictive ability of 10-year CVD/ stroke compared to the 13 different types of statistically derived risk calculators including integrated model AECRS 2.0

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system.Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set.Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC=0.80, P&lt;0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of similar to 18% against AtheroRisk-Conventional ML (AUC=0.68, P&lt;0.0001, 95% CI: 0.64 to 0.72).Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment

Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on “Integrated Vascular Age” instead of “Chronological Age”: A multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts

Background: Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA. Methods: The system follows a four-step process: (I) VA using cIMT based using linear-regression (LR) model and its coefficients; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)based model with gender adjustment; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area-under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin. Results: The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1,287 ultrasound scans. The 10-year FRS using IVA reported higher AUC (AUC =0.78) compared with 10-year FRS using CA (AUC =0.66) by ~18%

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

COVLIAS 1.0: Lung segmentation in COVID-19 computed tomography scans using hybrid deep learning artificial intelligence models

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi-or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value &lt; 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet &gt; VGG-SegNet &gt; NIH &gt; SegNet. The HDL runs in &lt;1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings

The Clinical Significance of Interleukin-6 in Heart Failure:Results from the BIOSTAT-CHF Study

Aims: Inflammation is a central process in the pathophysiology of heart failure (HF), but trials targeting tumour necrosis factor (TNF)‐α were largely unsuccessful. Interleukin (IL)‐6 is an important inflammatory mediator and might constitute a potential pharmacologic target in HF. However, little is known regarding the association between IL‐6 and clinical characteristics, outcomes and other inflammatory biomarkers in HF. We thus aimed to identify and characterize these associations. Methods and results: Interleukin‐6 was measured in 2329 patients [89.4% with a left ventricular ejection fraction (LVEF) ≤ 40%] of the BIOSTAT‐CHF cohort. The primary outcome was all‐cause mortality and HF hospitalization during 2 years, with all‐cause, cardiovascular (CV), and non‐CV death as secondary outcomes. Approximately half (56%) of all included patients had plasma IL‐6 values greater than the previously determined 95th percentile of normal values at baseline. Elevated N‐terminal pro‐brain natriuretic peptide, procalcitonin and hepcidin, younger age, TNF‐α/IL‐1‐related biomarkers, or having iron deficiency, atrial fibrillation and LVEF &gt; 40% independently predicted elevated IL‐6 levels. IL‐6 independently predicted the primary outcome [HR (95% confidence interval) per doubling: 1.16 (1.11–1.21), P &lt; 0.001], all‐cause mortality [1.22 (1.16–1.29), P &lt; 0.001] and CV as well as non‐CV mortality [1.16 (1.09–1.24), P &lt; 0.001; 1.31 (1.18–1.45), P &lt; 0.001], but did not improve discrimination in previously published risk models. Conclusions: In a large, heterogeneous cohort of HF patients, elevated IL‐6 levels were found in more than 50% of patients and were associated with iron deficiency, reduced LVEF, atrial fibrillation and poorer clinical outcomes. These findings warrant further investigation of IL‐6 as a potential therapeutic target in specific HF subpopulations