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fotografías, gráficas, ilustraciones, tablas El Hospital Universitario Nacional de Colombia (HUN), junto a la Universidad Nacional de Colombia, se han posicionado como un centro de referencia asistencial y académica, con proyección a formar clínicas o centros de excelencia; en la búsqueda de este objetivo, es indispensable el desarrollo de estándares clínicos basados en la evidencia (ECBE), que son documentos realizados con la mejor evidencia médica disponible y que permiten una adecuada y estandarizada atención de los pacientes. La enfermedad cardiovascular representa la principal causa de muerte a nivel mundial, y una de las principales en Colombia; la detección temprana de la enfermedad aumenta las oportunidades de tratamiento oportuno, disminuyendo la morbimortalidad y los costos económicos y humanos asociados a esta. En 2019 se atendieron 281 personas con IAM el Hospital Universitario Nacional de Colombia (HUN), para un total de 1742 días de estancia hospitalaria, razón por la cual el IAM se considera una enfermedad de importancia en el hospital. Este ECBE busca Identificar las indicaciones para el diagnóstico y tratamiento de los pacientes con IAM atendidos en el HUN, para facilitar la atención de los pacientes. (Texto tomado de la fuente) The National University Hospital of Colombia (HUN), together with the National University of Colombia, have positioned themselves as a care and academic reference center, with a projection to form clinics or centers of excellence; In pursuit of this objective, the development of evidence-based clinical standards (ECBE) is essential, which are documents made with the best available medical evidence and that allow adequate and standardized patient care. Cardiovascular disease represents the leading cause of death worldwide and one of the main ones in Colombia; early detection of the disease increases the opportunities for timely treatment, reducing morbidity and mortality and the economic and human costs associated with it. In 2019, 281 people with AMI were treated at the National University Hospital of Colombia (HUN) for a total of 1742 days of hospital stay, which is why AMI is considered a significant disease in the hospital. This ECBE seeks to identify the indications for the diagnosis and treatment of patients with AMI treated at the HUN, to facilitate patient care. Especialidades Médicas Especialista en Medicina Interna

Motivation Machine learning (ML)-based stroke risk stratification systems have typically focused on conventional risk factors (CRF) (AtheroRisk-conventional). Besides CRF, carotid ultrasound image phenotypes (CUSIP) have shown to be powerful phenotypes risk stratification. This is the first ML study of its kind that integrates CUSIP and CRF for risk stratification (AtheroRisk-integrated) and compares against AtheroRisk-conventional. Methods Two types of ML-based setups called (i) AtheroRisk-integrated and (ii) AtheroRisk-conventional were developed using random forest (RF) classifiers. AtheroRisk-conventional uses a feature set of 13 CRF such as age, gender, hemoglobin A1c, fasting blood sugar, low-density lipoprotein, and high-density lipoprotein (HDL) cholesterol, total cholesterol (TC), a ratio of TC and HDL, hypertension, smoking, family history, triglyceride, and ultrasound-based carotid plaque score. AtheroRisk-integrated system uses the feature set of 38 features with a combination of 13 CRF and 25 CUSIP features (6 types of current CUSIP, 6 types of 10-year CUSIP, 12 types of quadratic CUSIP (harmonics), and age-adjusted grayscale median). Logistic regression approach was used to select the significant features on which the RF classifier was trained. The performance of both ML systems was evaluated by area-under-the-curve (AUC) statistics computed using a leave-one-out cross-validation protocol. Results Left and right common carotid arteries of 202 Japanese patients were retrospectively examined to obtain 404 ultrasound scans. RF classifier showed higher improvement in AUC (~57%) for leave-one-out cross-validation protocol. Using RF classifier, AUC statistics for AtheroRisk-integrated system was higher (AUC = 0.99,p-value

Introduction: Cardiovascular disease (CVD) is the leading cause of death in the US. Asians are the fastest-growing immigrant group in the US and are disproportionately affected by CVD risk factors, compared to Whites. Studies on CVD risk aggregate Asians into a homogenous group. It is unclear how Asian immigrants (Indian subcontinent, Southeast Asia, Asia) differ with respect to CVD risks and in comparison, to Whites. Hypothesis: We hypothesize that there will be significant heterogeneity in CVD risk factors among Asian immigrant subgroups as compared to Non-Hispanics Whites. Methods: A cross-sectional comparative analysis of the 2010 to 2018 National Health Interview Survey was conducted among 508,941 participants. A generalized linear model using Poisson distribution was fitted to compare the prevalence of self-reported hypertension, overweight/obesity, diabetes, high cholesterol, physical inactivity, and smoking among Asian immigrants to Whites, adjusting for known confounders. Results: In this sample, 33,973 were Asian immigrants (26% Indian subcontinent; 45% Southeast Asia; 29% Asia) and 474,968 Whites; Indian subcontinent were the youngest (39 years; SE: 0.36). Compared to Whites, the Indian subcontinent had the highest prevalence of overweight/obesity (PR:1.22; 95% CI:1.19-1.25) and Southeast Asian had highest in high cholesterol (PR:1.16; 95% CI:1.10-1.23). Both, the Indian subcontinent (PR:1.69; 95% CI:1.49 - 1.93) and Southeast Asian (PR:1.38; 95%:1.26-1.52), reported a higher prevalence of diabetes. All Asian subgroups were more likely to be physically inactive and less likely to smoke than Whites (Table). Conclusion: Significant heterogeneity in CVD risk factors among Asian immigrants was observed compared to Whites. Providers caring for Asians should consider health behaviors and cultural beliefs influencing health outcomes and provide tailored and culturally informed care to improve the cardiovascular health of diverse populations.

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 (CVRC(ML)) and compares against statistically derived CVRC (CVRC(Stat)) 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 CVRC(Stat) (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 CVRC(Stat), the patients were again risk-stratified using CVRC(ML) 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 CVRC(Stat) calculators were: AECRS2.0 (AUC 0.83, P

ackground: 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

Background and Purpose Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)- based plaque tissue classification and characterization system. Methods We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra. Results After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%–10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p < 0.0001). It took tCNN > ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization.

The cardiovascular and stroke mortality rates in Arkansas are among the highest in the USA. The annual trends of stroke and cardiovascular mortality are barely correlated to smoking cessation, while the prevalence of risk factors such as obesity, cholesterol and hypertension increased over the 1979–2007 period. The study determined the effect of chronic exposure to PM2.5 on non-accidental, cardiovascular, stroke and lung cancer mortality in Arkansas over the 2000–2010 period using the World Health Organization’s log-linear health impact model. County chronic exposures to PM2.5 were computed by averaging spatially-resolved gridded concentrations using PM2.5 observations. A spatial uniformity was observed for PM2.5 mass levels indicating that chronic exposures were comparable throughout the state. The reduction of PM2.5 mass levels by 3.0 μg/m3 between 2000 and 2010 explained a significant fraction of the declining mortality. The effect was more pronounced in southern and eastern rural Arkansas as compared to the rest of the state. This study provides evidence that the implementation of air pollution regulations has measurable effects on mortality even in regions with high prevalence of major risk factors such as obesity and smoking. These outcomes are noteworthy as efforts to modify the major risk factors require longer realization times.

BACKGROUND: Myocardial infarction (MI) as a term for a heart attack happens due to reduced blood flow to heart myocardium and lack of oxygen supply caused by plaques inthe interior walls of coronary arteries. With respect to the importance of MI etiology, we aimed to study the relationship of MI and blood examination variables.METHODS: This study was conducted in Mazandaran Heart Center as a hospital-based case-control Comprising 894 participants including 465 cases and 429 controls, individually matched by sex and age. Considered blood markers were analyzed using routine laboratory methods and equipment.RESULTS: Of all participants, 64.3% of the cases and 51.0% of the controls were males with a mean age of 61.2 (±13.8) in cases and 62.4 (±14.) in controls. We could not find any differences between cases and controls for total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and alkaline-phosphatase (ALP) (P>0.05). However, levels of creatine-kinase-muscle/brain (CK-MB) (P