Shock wave structure in a lattice gas

The motion and structure of shock and expansion waves in a simple particle system, a lattice gas and cellular automaton, are determined in an exact computation. Shock wave solutions, also exact, of a continuum description, a model Boltzmann equation, are compared with the lattice results. The comparison demonstrates that, as proved by Caprino et al. [“A derivation of the Broadwell equation,” Commun. Math. Phys. 135, 443 (1991)] only when the lattice processes are stochastic is the model Boltzmann description accurate. In the strongest shock wave, the velocity distribution function is the bimodal function proposed by Mott-Smith

Prediction of Advanced Fibrosis in Nonalcoholic Fatty Liver Disease: An Enhanced Model of BARD Score.

UNLABELLED: BACKGROUNDAIMS: The BARD score is a model to detect advanced liver fibrosis in nonalcoholic fatty liver disease (NAFLD) patients. The aims of this study were to identify additional factors and then to build an enhanced version of the BARD score. METHODS: One hundred seven patients with biopsy-proven NAFLD were enrolled retrospectively. Logistic regressions were performed to identify independent risk factors for advanced liver fibrosis (stage 3 or 4). An enhanced model of the BARD score (BARDI score) was built and evaluated with a receiver operating characteristic (ROC) curve. RESULTS: In multivariate analysis, age (odds ratio [OR], 0.89; p=0.04), aspartate aminotransferase/alanine aminotransferase ratio (OR, 1.73; p CONCLUSIONS: The BARDI score had an improved PPV over the BARD score and maintained an excellent NPV. Further study is warranted for its external validation and comparison with other models

Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation

BACKGROUND The Diamond-Forrester model was used extensively to predict obstructive coronary artery disease (CAD) but overestimates probability in current populations. Coronary artery calcium (CAC) is a useful marker of CAD, which is not routinely integrated with other features. We derived simple likelihood tables, integrating CAC with age, sex, and cardiac chest pain to predict obstructive CAD. METHODS AND RESULTS The training population included patients from 3 multinational sites (n=2055), with 2 sites for external testing (n=3321). We determined associations between age, sex, cardiac chest pain, and CAC with the presence of obstructive CAD, defined as any stenosis ≥50% on coronary computed tomography angiography. Prediction performance was assessed using area under the receiver-operating characteristic curves (AUCs) and compared with the CAD Consortium models with and without CAC, which require detailed calculations, and the updated Diamond-Forrester model. In external testing, the proposed likelihood tables had higher AUC (0.875 [95% CI, 0.862-0.889]) than the CAD Consortium clinical+CAC score (AUC, 0.868 [95% CI, 0.855-0.881]; P=0.030) and the updated Diamond-Forrester model (AUC, 0.679 [95% CI, 0.658-0.699]; P<0.001). The calibration for the likelihood tables was better than the CAD Consortium model (Brier score, 0.116 versus 0.121; P=0.005). CONCLUSIONS We have developed and externally validated simple likelihood tables to integrate CAC with age, sex, and cardiac chest pain, demonstrating improved prediction performance compared with other risk models. Our tool affords physicians with the opportunity to rapidly and easily integrate a small number of important features to estimate a patient's likelihood of obstructive CAD as an aid to clinical management

The accuracy of coronary CT angiography in patients with coronary calcium score above 1000 Agatston Units:Comparison with quantitative coronary angiography: Coronary CT Angiography in High Coronary Calcium

BACKGROUND: High amounts of coronary artery calcium (CAC) pose challenges in interpretation of coronary CT angiography (CCTA). The accuracy of stenosis assessment by CCTA in patients with very extensive CAC is uncertain. METHODS: Retrospective study was performed including patients who underwent clinically directed CCTA with CAC score >1000 and invasive coronary angiography within 90 days. Segmental stenosis on CCTA was graded by visual inspection with two-observer consensus using categories of 0%, 1–24%, 25–49%, 50–69%, 70–99%, 100% stenosis, or uninterpretable. Blinded quantitative coronary angiography (QCA) was performed on all segments with stenosis ≥25% by CCTA. The primary outcome was vessel-based agreement between CCTA and QCA, using significant stenosis defined by diameter stenosis ≥ 70%. Secondary analyses on a per-patient basis and inclusive of uninterpretable segments were performed. RESULTS: 726 segments with stenosis ≥25% in 346 vessels within 119 patients were analyzed. Median coronary calcium score was 1616 (1221–2118). CCTA identification of QCA-based stenosis resulted in a per-vessel sensitivity of 79%, specificity of 75%, positive predictive value (PPV) of 45%, negative predictive value (NPV) of 93%, and accuracy 76% (68 false positive and 15 false negative). Per-patient analysis had sensitivity 94%, specificity 55%, PPV 63%, NPV 92%, and accuracy 72% (30 false-positive and 3 false-negative). Inclusion of uninterpretable segments had variable effect on sensitivity and specificity, depending on whether they are considered as significant or non-significant stenosis. CONCLUSIONS: In patients with very extensive CAC (>1000 Agatston units), CCTA retained a negative predictive value > 90% to identify lack of significant stenosis on a per-vessel and per-patient level, but frequently overestimated stenosis

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P&lt;0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

Novel Approaches for the Use of Cardiac/Coronary Computed Tomography Angiography

Recent developments in the novel imaging technology of cardiac computed tomography (CT) not only permit detailed assessment of cardiac anatomy but also provide insight into cardiovascular physiology. Foremost, coronary CT angiography (CCTA) enables direct noninvasive examination of both coronary artery stenoses and atherosclerotic plaque characteristics. Calculation of computational fluid dynamics by cardiac CT allows the noninvasive estimation of fractional flow reserve, which increases the diagnostic accuracy for detection of hemodynamically significant coronary artery disease. In addition, a combination of myocardial CT perfusion and CCTA can provide simultaneous anatomical and functional assessment of coronary artery disease. Finally, detailed anatomical evaluation of atrial, ventricular, and valvular anatomy provides diagnostic information and guidance for procedural planning, such as for transcatheter aortic valve replacement. The clinical applications of cardiac CT will be extended with the development of these novel modalities

Concurrent smoking and alcohol consumers had higher triglyceride glucose indices than either only smokers or alcohol consumers: a cross-sectional study in Korea

Background The triglyceride glucose (TyG) index is a noninsulin-based marker for insulin resistance (IR) in general practice. Although smoking and heavy drinking have been regarded as major risk factors for various chronic diseases, there is limited evidence regarding the combined effects of smoking and alcohol consumption on IR. This study aimed to investigate the relationship between the TyG index and smoking and alcohol consumption using two Korean population-based datasets. Methods This study included 10,568 adults in the Korean National Health and Nutrition Examination Survey (KNHANES) and 9586 adults in the Korean Initiatives on Coronary Artery Calcification (KOICA) registry datasets. Multivariate logistic analysis was conducted to explore the relationship between smoking and alcohol consumption and the TyG index. To assess the predictive value of smoking and alcohol consumption on high TyG index, the area under the curve (AUC) were compared and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses were derived. Results The combined effect of smoking and alcohol consumption was an independent risk factor of a higher TyG index in the KNHANES (adjusted odds ratio: 4.33, P < .001) and KOICA (adjusted odds ratio: 1.94, P < .001) datasets. Adding smoking and alcohol consumption to the multivariate logistic models improved the model performance for the TyG index in the KNHANES (AUC: from 0.817 to 0.829, P < .001; NRI: 0.040, P < .001; IDI: 0.017, P < .001) and KOICA (AUC: from 0.822 to 0.826, P < .001; NRI: 0.025, P = .006; IDI: 0.005, P < .001) datasets. Conclusions Smoking and alcohol consumption were independently associated with the TyG index. Concurrent smokers and alcohol consumers were more likely to have a TyG index that was ≥8.8 and higher than the TyG indices of non-users and those who exclusively consumed alcohol or smoking tobacco.This work was supported by the Technology Innovation Program (20002781, A Platform for Prediction and Management of Health Risk Based on Personal Big Data and Lifelogging) funded by the Ministry of Trade, Industry and Energy (MOTIE, South Korea) to JW Lee, and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) Baek et al. Lipids in Health and Disease (2021) 20:49 Page 9 of 11 (NRF-2019R1A2C1010043) to H Lee. Additionally, this work was supported by Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (2019-31-1293), for autonomous digital companion framework and application to HJ Chan

Hepatocellular carcinoma incidence is decreasing in Korea but increasing in the very elderly

Background/Aims A comprehensive analysis of trends in the incidence of hepatocellular carcinoma (HCC) is important for planning public health initiatives. We aimed to analyze the trends in HCC incidence in South Korea over 10 years and to predict the incidence for the year 2028. Methods Data from patients with newly diagnosed HCC between 2008 and 2018 were obtained from Korean National Health Insurance Service database. Age-standardized incidence rates (ASRs) were calculated to compare HCC incidence. A poisson regression model was used to predict the future incidence of HCC. Results The average crude incidence rate (CR) was 22.4 per 100,000 person-years, and the average ASR was 17.6 per 100,000 person-years between 2008 and 2018. The CR (from 23.9 to 21.2 per 100,000 person-years) and ASR (from 21.9 to 14.3 per 100,000 person-years) of HCC incidence decreased during the past ten years in all age groups, except in the elderly. The ASR of patients aged ≥80 years increased significantly (from 70.0 to 160.2/100,000 person-years; average annual percent change, +9.00%; P<0.001). The estimated CR (17.9 per 100,000 person-years) and ASR (9.7 per 100,000 person-years) of HCC incidence in 2028 was declined, but the number of HCC patients aged ≥80 years in 2028 will be quadruple greater than the number of HCC patients in 2008 (from 521 to 2,055), comprising 21.3% of all HCC patients in 2028. Conclusions The ASRs of HCC in Korea have gradually declined over the past 10 years, but the number, CR, and ASR are increasing in patients aged ≥80 years