Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients.

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.

Relationship between Calcium Score and Conventional Risk Factors in the Diagnosis of Atherosclerosis.

Background: The coronary artery calcium score has been established as a highly specific feature of coronary atherosclerosis. The present study aimed to assess the possible association of coronary artery risk factors involving atherosclerosis with the coronary artery calcification (CAC) scores using coronary computed tomographic angiography (CCTA). Methods: The present cross-sectional study, performed on 252 patients in need of CCTA during April 2019 and September 2019 at Farshchian hospital in Hamadan, Iran. The demographic information and risk factors were acquired from the files of patients. Furthermore, the CACs of patients were calculated and expressed as the Agatston score. Based on the Agatston scale, participants were divided into 4 CAC scores: zero (CAC = 0), mild (CAC = 1-99), moderate (CAC = 100-399), and severe (CAC ≥400). The association between possible coronary artery disease (CAD) risk variables and the CAC score was investigated using multinomial logistic regression. Results: Of 252 participants, approximately 40% of studied patients had a positive CAC score (CAC > 0). CAC significantly shifts toward higher scores in smokers, patients with diabetes, hypertension, and older patients. Mild (CAC = 1-99) and moderate CAC (100-399) were significantly associated with diabetes (odds ratio [OR], 3.26; 95% CI, 1.48-7.17) and (OR, 12; 95% CI, 4.40-32.71) for mild and moderate CAC, respectively. However, the strongest predictor for severe CAC was diabetes (OR, 7.72; 95% CI, 2.10-28.35). Conclusion: Coronary artery calcium scoring is a marker for risk factors associated with atherosclerosis. In this study, more than half of patients in CAC screening had CAC = 0. The strongest predictor of severe CAC>0 was smoking and diabetes. Regarding this association between health condition and CAC, determining the CAC can prevent major coronary heart disease events in these patients.

The role of the socio-economic environment on medical outcomes after ST-segment elevation myocardial infarction.

BACKGROUND: According to the World Health Organization, coronary artery disease (CAD), including ST-segment elevation myocardial infarction (STEMI), is the most common cause of death worldwide as well as in Europe and Austria. There is valid data on the impact of conventional risk factors on the medical outcomes for STEMI patients. However, only few studies examine the role of the socio-economic environment for medical outcomes. The main task of this study is to investigate if the socio-economic environment of patients who underwent percutaneous coronary intervention (PCI) after STEMI has an impact on the distribution of risk factors and medical outcomes. METHODS: The study focuses on the population of the City of Vienna, Austria, and includes 870 STEMI patients, who underwent PCI at the General Hospital of Vienna (AKH Wien) between 2008 and 2012. The following data were collected: conventional risk factors (hypertension, hyperlipidemia, diabetes, overweight, smoking, family history and vascular disease) and socio-economic indicators of the patient's residential district (number of residents, income pre-tax, residents per general practitioner, residents per internal specialist, compulsory education only, academic degree and rate of unemployment). Cox regressions were performed to evaluate the impact of socio-economic environment and conventional risk factors on survival. RESULTS: Most of the conventional risk factors show a significant difference between deceased and surviving patients. The study revealed significant differences across districts in relation to the socio-economic background of STEMI patients. Surprisingly, medical outcomes, as measured by the survival of patients, are significantly related to a patient's district of residence (p-Value = 0.028) but not in a systematic way as far as the socio-economic environment of these districts is concerned. CONCLUSIONS: The study provides intuitive evidence for a hitherto understudied Central European context on the link between socio-economic environment and conventional risk factors at population level and the link between conventional risk factors and survival both at the population at the individual level. While this is in line with previous evidence and suggestive of the incorporation of measures of socio-economic status (SES) into policy & guidelines toward the management of CAD, more data on the SES - STEMI nexus are needed at individual level.

Ethnic Disparities in CKD in the Netherlands: The Healthy Life in an Urban Setting (HELIUS) Study.

BACKGROUND: Evidence suggesting important ethnic differences in chronic kidney disease (CKD) prevalence comes mainly from the United States, and data among various ethnic groups in Europe are lacking. We therefore assessed differences in CKD in 6 ethnic groups living in the Netherlands and explored to what extent the observed differences could be accounted for by differences in conventional cardiovascular risk factors (smoking, physical activity, obesity, hypertension, diabetes, and hypercholesterolemia). STUDY DESIGN: Cross-sectional analysis of baseline data from the Healthy Life in an Urban Setting (HELIUS) cohort study. SETTING & PARTICIPANTS: A random sample of 12,888 adults (2,129 Dutch, 2,273 South Asian Surinamese, 2,159 African Surinamese, 1,853 Ghanaians, 2,255 Turks, and 2,219 Moroccans) aged 18 to 70 years living in Amsterdam, the Netherlands. PREDICTORS: Ethnicity. OUTCOMES & MEASUREMENTS: CKD status was defined using the 2012 KDIGO (Kidney Disease: Improving Global Outcomes) severity of CKD classification. CKD was defined as albumin-creatinine ratio ≥ 3mg/mmol (category ≥ A2) or glomerular filtration rate < 60mL/min/1.73m(2) (category ≥ G3). Comparisons among groups were made using prevalence ratios (PRs). RESULTS: The age-standardized prevalence of CKD was higher in all ethnic minority groups, ranging from 4.6% (95% CI, 3.8%-5.5%) in African Surinamese to 8.0% (95% CI, 6.7%-9.4%) in Turks, compared with 3.0% (95% CI, 2.3%-3.7%) in Dutch. Adjustment for conventional risk factors reduced the PR substantially, but ethnic differences remained for all ethnic minority groups except African Surinamese, with the PR ranging from 1.48 (95% CI, 1.12-1.97) in Ghanaians to 1.75 (95% CI, 1.33-2.30) in Turks compared with Dutch. Similar findings were found when CKD was stratified into a moderately increased and a combined high/very high risk group. Among the combined high/very high CKD risk group, conventional risk factors accounted for most of the ethnic differences in CKD except for South Asian Surinamese (PR, 2.60; 95% CI, 1.26-5.34) and Moroccans (PR, 2.33; 95% CI, 1.05-5.18). LIMITATIONS: Cross-sectional design. CONCLUSIONS: These findings suggest ethnic inequalities in CKD for most groups even after adjustment for conventional risk factors. These findings highlight the need for further research to identify other potential factors contributing to the ethnic inequalities in CKD.

Young hearts at risk: Unveiling novel factors in myocardial infarction susceptibility and prevention.

The increasing incidence of acute myocardial infarction (AMI) among the young population represents a significant and emerging health concern, contributing substantially to both mortality and morbidity. Unlike myocardial infarctions occurring in older individuals, traditional risk factors such as diabetes and hypertension exhibit a weaker association in the younger demographic. Consequently, there is a pressing need for a deeper understanding of novel risk factors that contribute to AMI in young patients. In this review, we explore distinct risk factor profiles associated with young-onset AMI in comparison to older patients. Special attention is given to novel risk factors, examining their susceptibility factors and exploring preventive measures. The comprehensive risk profile of extremely young South Asians who develop early coronary arterial disease is not yet fully understood. There are many novel evolving risk factors associated with young AMI which need intervention to reduce morbidity and mortality. It has been seen that established inflammatory markers like lipoprotein (a), dyslipidaemia, long COVID, and new emerging risk factors like air pollution (micro- and nanoplastics), periodontitis, acute stress, energy drinks, misuse of recreational drugs may increase risk and influence treatment, and outcomes of AMI in this young population. Screening of emerging novel risk markers and their optimization is important in preventing young patients with AMI. The role of conventional risk factors should not be overlooked and should be treated aggressively. Sex and geographic-specific base approaches are required to reduce risk factors and prevent AMI in young. More prospective studies are needed to evaluate the increasing incidence of young AMI and its associated novel risk factors.

Lipid Association of India 2023 update on cardiovascular risk assessment and lipid management in Indian patients: Consensus statement IV.

OBJECTIVE: In 2016, the Lipid Association of India (LAI) developed a cardiovascular risk assessment algorithm and defined low-density lipoprotein cholesterol (LDL-C) goals for prevention of atherosclerotic cardiovascular disease (ASCVD) in Indians. The recent refinements in the role of various risk factors and subclinical atherosclerosis in prediction of ASCVD risk necessitated updating the risk algorithm and treatment goals. METHODS: The LAI core committee held twenty-one meetings and webinars from June 2022 to July 2023 with experts across India and critically reviewed the latest evidence regarding the strategies for ASCVD risk prediction and the benefits and modalities for intensive lipid lowering. Based on the expert consensus and extensive review of published data, consensus statement IV was commissioned. RESULTS: The young age of onset and a more aggressive nature of ASCVD in Indians necessitates emphasis on lifetime ASCVD risk instead of the conventional 10-year risk. It also demands early institution of aggressive preventive measures to protect the young population prior to development of ASCVD events. Wide availability and low cost of statins in India enable implementation of effective LDL-C-lowering therapy in individuals at high risk of ASCVD. Subjects with any evidence of subclinical atherosclerosis are likely to benefit the most from early aggressive interventions. CONCLUSIONS: This document presents the updated risk stratification and treatment algorithm and describes the rationale for each modification. The intent of these updated recommendations is to modernize management of dyslipidemia in Indian patients with the goal of reducing the epidemic of ASCVD among Indians in Asia and worldwide.

Prevalence and Comparision of Conventional Risk Factors of Cardiovascular Disease Between Clinical and Nonclinical Staff.

BACKGROUND: Cardiovascular disease is the leading cause of death around the globe. A number of studies have shown that hospital staff are vulnerable to cardiovascular disease due to a certain risk of shift duty. It is important to identify cardiovascular risk factors among hospital staff. The aim of this study is, to assess the prevalence of conventional risk factors of cardiovascular disease among hospital staff. METHODS: A quantitative cross-sectional study was conducted among staff working at a Shahid Gangalal National Heart Center, a tertiary cardiac center in Nepal. Simple and multiple linear regression analyses were used to examine the association between independent variables and cardiovascular diseases. Statistical analysis was done using SPSS software version 20. RESULTS: A total of 250 hospital staff participated in this study. Among them, 137 were clinical staff and 113 were non-clinical staff. The mean age of clinical staff and the non-clinical staff was 33.69 ± 7.02 years and 38.7 ± 10.58 years respectively with a total of 66.8% females. Prevalence of hypertension, diabetes mellitus, and dyslipidaemia was less in clinical staff compared to non-clinical staff. The mean systolic, diastolic BP was high in non-clinical staff ( P-value 0.001), moreover mean HDL-C was low (1.2 ± 0.2 mmol). BMI was significantly low in clinical staff. [standardized ß= -0.24; 95% CI: -2.90, -0.88]. CONCLUSIONS: The prevalence of cardiovascular risk factors were high in non-clinical staff compared to clinical staff.

Racial Differences and Contributory Cardiovascular and Non-Cardiovascular Risk Factors Towards Chronic Kidney Disease Progression.

Background: The prevalence of advanced chronic kidney disease (CKD) is higher in Black than in White Americans. We evaluated CKD progression in Black and White participants and the contribution of biological risk factors. We included the study of lung function (measured by forced vital capacity [FVC]), which is part of the emerging notion of interorgan cross-talk with the kidneys to racial differences in CKD progression. Methods: This longitudinal study included 2175 Black and 2207 White adult Coronary Artery Risk Development in Young Adults (CARDIA) participants. Estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR) were measured at study year 10 (age 27-41y) and every five years for 20 years. The outcome was CKD progression through no CKD, low, moderate, high, or very high-risk categories based on eGFR and UACR in combination. The association between race and CKD progression as well as the contribution of risk factors to racial differences were assessed in multivariable-adjusted Cox models. Results: Black participants had higher CKD transition probabilities than White participants and more prevalent risk factors during the 20-year period studied. Hazard ratios for CKD transition for Black (vs White participants) were 1.38 from No CKD into ≥ low risk, 2.25 from ≤ low risk into ≥ moderate risk, and 4.49 from ≤ moderate risk into ≥ high risk. Racial differences in CKD progression from No CKD into ≥ low risk were primarily explained by FVC (54.8%), hypertension (30.9%), and obesity (20.8%). In contrast, racial differences were less explained in more severe transitions. Conclusion: Black participants had a higher risk of CKD progression, and this discrepancy may be partly explained by FVC and conventional risk factors.

Non-Conventional Risk Factors: "Fact" or "Fake" in Cardiovascular Disease Prevention?

Cardiovascular diseases (CVDs), such as arterial hypertension, myocardial infarction, stroke, heart failure, atrial fibrillation, etc., still represent the main cause of morbidity and mortality worldwide. They significantly modify the patients' quality of life with a tremendous economic impact. It is well established that cardiovascular risk factors increase the probability of fatal and non-fatal cardiac events. These risk factors are classified into modifiable (smoking, arterial hypertension, hypercholesterolemia, low HDL cholesterol, diabetes, excessive alcohol consumption, high-fat and high-calorie diet, reduced physical activity) and non-modifiable (sex, age, family history, of previous cardiovascular disease). Hence, CVD prevention is based on early identification and management of modifiable risk factors whose impact on the CV outcome is now performed by the use of CV risk assessment models, such as the Framingham Risk Score, Pooled Cohort Equations, or the SCORE2. However, in recent years, emerging, non-traditional factors (metabolic and non-metabolic) seem to significantly affect this assessment. In this article, we aim at defining these emerging factors and describe the potential mechanisms by which they might contribute to the development of CVD.

Unconventional Cardiovascular Risk Factors and Systematic Coronary Risk Estimation (SCORE) in the Lebanese Rural Population: The Forgotten Factors.

Purpose: To evaluate the correlation between unconventional risk factors and the Systematic Coronary Risk Estimation (SCORE), and estimate the prevalence of conventional and unconventional cardiovascular (CV) risk factors in the rural Lebanese population in order to assess their CV risk. Methods: This is a retrospective descriptive study conducted between November 2017 and June 2019 among the Lebanese rural population. The risk factors were analyzed from the files of the patients who presented for the CV disease screening days organized by a non governmental organization. The CV risk estimation tool is the SCORE. The classification of socio-economic level ranges from zero (low level) to 3 (high level). Results: A total of 433 patients were included. The prevalence of hypertension, diabetes, dyslipidemia, smoking, and metabolic syndrome was 45.1%, 31.2%, 39.2%, 50% and 42.9% respectively. Only 13.6% of hypertensive patients and 6.7% of diabetics were controlled. A total of 0 or 1 point for the classification of socio-economic status was found in 62.6% of cases. A family history of CV diseases was present in 87.3% of participants. The SCORE was correlated with diabetes and metabolic syndrome (p = 0.000), without being correlated to socio-economic status (HR = -0.104; p = 0.059) or to family history (p = 0.834). Conclusion: The socio-economic status and the family history of CV disease must be evaluated in addition to the classical risk calculation of the SCORE to better pinpoint the actual risk of the targeted population. The risk factors are prevalent but poorly controlled, hence the need for a national effort to ensure better care for the rural Lebanese population.

Comparison of Genetic Susceptibility to Coronary Heart Disease in the Hungarian Populations: Risk Prediction Models for Coronary Heart Disease.

Background and Aim: It was evaluated whether the integration of genetic risk scores (GRS-unweighted, wGRS-weighted) into conventional risk factor (CRF) models for coronary heart disease or acute myocardial infarction (CHD/AMI) could improve the predictive ability of the models. Methods: Subjects and data collected in a previous survey were used to perform regression and ROC curve analyses as well as to examine the role of genetic components. Thirty SNPs were selected, and genotype and phenotype data were available for 558 participants (general: N = 279 and Roma: N = 279). Results: The mean GRS (27.27 ± 3.43 vs. 26.68 ± 3.51, p = 0.046) and wGRS (3.52 ± 0.68 vs. 3.33 ± 0.62, p = 0.001) were significantly higher in the general population. The addition of the wGRS to the CRF model yielded the strongest improvement in discrimination among Roma (from 0.8616 to 0.8674), while the addition of GRS to the CRF model yielded the strongest improvement in discrimination in the general population (from 0.8149 to 0.8160). In addition to that, the Roma individuals were likely to develop CHD/AMI at a younger age than subjects in the general population. Conclusions: The combination of the CRFs and genetic components improved the model's performance and predicted AMI/CHD better than CRFs alone.

Coronary Artery Stenosis Severity in Patients With Different Coronary Artery Calcium Scores on Coronary Computed Tomography Angiography.

Background In this study, we aimed to determine coronary artery stenosis severity in patients with different coronary artery calcium (CAC) scores. Methodology A total of 145 patients were included in the study. All patients were given beta-blockers 12 hours and two hours before the test to keep their heart rate between 55 and 65 beats per minute. Computed tomography angiography was done from the pulmonary hilum up to the base of the heart and the patients were asked to hold their breath. The CAC score and stenosis were assessed. Results The mean age of the patients was 41.35 ± 4.95 years. In total, 112 (77.24%) patients were male and 33 (22.76%) were female. Regarding the frequency of the CAC score, a score of 0-9 was observed in 43 (29.66%) patients, 10-99 was observed in 55 (37.93%) patients, and 100-400 was observed in 47 (32.41%) patients. The CAC score was 0-9 in 86.4% of patients having normal coronary arteries. Two (5.2%) patients with a CAC score of 100-400 had mild coronary artery stenosis, 11 (32.3%) patients had moderate coronary artery disease, and 33 (66.0%) patients had severe coronary artery disease (p < 0.00001). Conclusions There is a strong association between CAC scores and the severity of coronary artery stenosis. A CAC score of zero is associated with a very low risk of having coronary artery stenosis.

Prognostic Modelling Studies of Coronary Heart Disease-A Systematic Review of Conventional and Genetic Risk Factor Studies.

This study aims to provide an overview of multivariable prognostic modelling studies developed for coronary heart disease (CHD) in the general population and to explore the optimal prognostic model by comparing the models' performance. A systematic review was performed using Embase, PubMed, Cochrane, Web of Science, and Scopus databases until 30 November 2019. In this work, only prognostic studies describing conventional risk factors alone or a combination of conventional and genomic risk factors, being developmental and/or validation prognostic studies of a multivariable model, were included. A total of 4021 records were screened by titles and abstracts, and 72 articles were eligible. All the relevant studies were checked by comparing the discrimination, reclassification, and calibration measures. Most of the models were developed in the United States and Canada and targeted the general population. The models included a set of similar predictors, such as age, sex, smoking, cholesterol level, blood pressure, BMI, and diabetes mellitus. In this study, many articles were identified and screened for consistency and reliability using CHARM and GRIPS statements. However, the usefulness of most prognostic models was not demonstrated; only a limited number of these models supported clinical evidence. Unfortunately, substantial heterogeneity was recognized in the definition and outcome of CHD events. The inclusion of genetic risk scores in addition to conventional risk factors might help in predicting the incidence of CHDs; however, the generalizability of the existing prognostic models remains open. Validation studies for the existing developmental models are needed to ensure generalizability, improve the research quality, and increase the transparency of the study.

Independent and interactive effects of kidney stone formation and conventional risk factors for chronic kidney disease: a follow-up study of Japanese men.

PURPOSE: To assess the impact of interactions between kidney stone formation and conventional risk factors on incident chronic kidney disease (CKD). METHODS: A total of 11,402 subjects (men 30-69 years of age, Japanese) without CKD at baseline were observed over an average period of 4 years. Cox proportional hazards regression models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) to determine the association between incident CKD, kidney stone formation, and conventional risk factors (diabetes mellitus, hypertension, overweight/obesity, dyslipidemia, and hyperuricemia/gout). We also examined the interactions of renal stones and the conventional risk factors for CKD. RESULTS: In total, 2301 men (20.2%) developed incident CKD during the follow-up period. After multivariable adjustment, kidney stones were found to increase the risk of incident CKD (HR 1.16; 95% CI 1.03-1.32). Kidney stone formers with hypertension, dyslipidemia, or hyperuricemia/gout presented a greater risk for incident CKD than those who had either kidney stones or other risk factors. However, no significant interactions between kidney stones and other risk factors were found to increase CKD risk. On the other hand, a negative interactive effect between kidney stones and overweight/obesity was observed, leading to reversed risk of incident CKD in coexistence of both factors. CONCLUSION: Kidney stones were linked to a higher risk for the development of CKD. However, no positive interactive effects were identified between renal stones and conventional risk factors on the risk of incident CKD.

Sex Differences in Modifiable Risk Factors and Severity of Coronary Artery Disease.

Background It is still unknown whether traditional risk factors may have a sex-specific impact on coronary artery disease (CAD) burden. Methods and Results We identified 14 793 patients who underwent coronary angiography for acute coronary syndromes in the ISACS-TC (International Survey of Acute Coronary Syndromes in Transitional Countries; Clini​calTr​ials.gov, NCT01218776) registry from 2010 to 2019. The main outcome measure was the association between traditional risk factors and severity of CAD and its relationship with 30-day mortality. Relative risk (RR) ratios and 95% CIs were calculated from the ratio of the absolute risks of women versus men using inverse probability of weighting. Estimates were compared by test of interaction on the log scale. Severity of CAD was categorized as obstructive (≥50% stenosis) versus nonobstructive CAD. The RR ratio for obstructive CAD in women versus men among people without diabetes mellitus was 0.49 (95% CI, 0.41-0.60) and among those with diabetes mellitus was 0.89 (95% CI, 0.62-1.29), with an interaction by diabetes mellitus status of P =0.002. Exposure to smoking shifted the RR ratios from 0.50 (95% CI, 0.41-0.61) in nonsmokers to 0.75 (95% CI, 0.54-1.03) in current smokers, with an interaction by smoking status of P=0.018. There were no significant sex-related interactions with hypercholesterolemia and hypertension. Women with obstructive CAD had higher 30-day mortality rates than men (RR, 1.75; 95% CI, 1.48-2.07). No sex differences in mortality were observed in patients with nonobstructive CAD. Conclusions Obstructive CAD in women signifies a higher risk for mortality compared with men. Current smoking and diabetes mellitus disproportionally increase the risk of obstructive CAD in women. Achieving the goal of improving cardiovascular health in women still requires intensive efforts toward further implementation of lifestyle and treatment interventions. Registration URL: https://www.clini​caltr​ials.gov; Unique identifier: NCT01218776.

Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors.

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<0.001) compared to AtheroRisk-conventional (AUC = 0.63,p-value<0.001). CONCLUSION: The AtheroRisk-integrated ML system outperforms the AtheroRisk-conventional ML system using RF classifier.

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<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<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.

Risk prediction-Homocysteine in Coronary Heart Disease.

Majority of patients who experience a Coronary Heart disease event have one or more of the conventional risk factors for atherosclerosis and so do many people who have not yet experienced such an event. Thus predictive models based on conventional risk factors have lower than the desired accuracy, providing a stimulus to search for new factors to predict accurately the risk of CHD. In this regard newer risk factors like homocysteine, Lp(a), insulin resistances are the important ones and are called as 'novel risk factors'. The study was undertaken to find the prediction of CHD risk by homocysteine in comparison with other conventional risk factors. The data obtained suggests a very high sensitivity, specificity and accuracy with above 90% positive prediction value for homocysteine in CHD patients when compared to commonest conventional risk factors.

Genetic risk analysis of coronary artery disease in Pakistani subjects using a genetic risk score of 21 variants.

BACKGROUND AND AIMS: Conventional coronary artery disease (CAD) risk factors like age, gender, blood lipids, hypertension and smoking have been the basis of CAD risk prediction algorithms, but provide only modest discrimination. Genetic risk score (GRS) may provide improved discrimination over and above conventional risk factors. Here we analyzed the genetic risk of CAD in subjects from Pakistan, using a GRS of 21 variants in 18 genes and examined whether the GRS is associated with blood lipid levels. METHODS: 625 (405 cases and 220 controls) subjects were genotyped for variants, NOS3 rs1799983, SMAD3 rs17228212, APOB rs1042031, LPA rs3798220, LPA rs10455872, SORT1 rs646776, APOE rs429358, GLUL rs10911021, FTO rs9939609, MIA3 rs17465637, CDKN2Ars10757274, DAB2IP rs7025486, CXCL12 rs1746048, ACE rs4341, APOA5 rs662799, CETP rs708272, MRAS rs9818870, LPL rs328, LPL rs1801177, PCSK9 rs11591147 and APOE rs7412 by TaqMan and KASPar allele discrimination techniques. RESULTS: Individually, the single SNPs were not associated with CAD except APOB rs1042031 and FTO rs993969 (p = 0.01 and 0.009 respectively). However, the combined GRS of 21 SNPs was significantly higher in cases than controls (19.37 ± 2.56 vs. 18.47 ± 2.45, p = 2.9 × 10-5), and compared to the bottom quintile, CAD risk in the top quintile of the GRS was 2.96 (95% CI 1.71-5.13). Atherogenic blood lipids showed significant positive association with GRS. CONCLUSIONS: The GRS was quantitatively associated with CAD risk and showed association with blood lipid levels, suggesting that the mechanism of these variants is likely to be, in part at least, through creating an atherogenic lipid profile in subjects carrying high numbers of risk alleles.

Prevalence of conventional risk factors and lipid profiles in patients with acute coronary syndrome and significant coronary disease.

BACKGROUND: Among patients with coronary artery disease (CAD), 80%-90% present at least one conventional risk factor. On the other hand, lipid profile modification after a cardiovascular event related to acute coronary syndrome (ACS) has been recognized. The prevalence of conventional risk factors and the lipid profile at the time of admission in patients with ACS and significant CAD (stenosis ≥50%) determined through coronary angiography is not well described. METHODS: We studied 3,447 patients with a diagnosis of ACS and significant CAD with stenosis ≥50%, as shown o n angiography. We recorded the presence of conventional risk factors, including smoking, hypertension, dyslipidemia, and diabetes. In addition, we analyzed the lipid profiles within the first 24 hours of admission. We analyzed the studied population and compared findings according to sex. RESULTS: Most patients (81.7%) were male. ST-elevation myocardial infarction was present in 51.3% of patients, and non-ST-elevation acute coronary syndrome was present in 48.7%. The most frequent risk factor was smoking, which was present in 68% of patients, followed by hypertension (57.8%), dyslipidemia (47.5%), and diabetes (37.7%). In women, the most frequent risk factors were hypertension, diabetes, and dyslipidemia, whereas in men, smoking was the most frequent. We identified at least one risk factor in 95.7% of all patients, two or three risk factors in 62%, and four risk factors in 8.6% of patients. The lipid profile analysis revealed that 85.1% of patients had some type of dyslipidemia, and the most frequent was low levels of high-density lipoprotein cholesterol (68.6% of cases). CONCLUSION: We found at least one conventional risk factor in 95.7% of patients with ACS and significant CAD. The lipid profile analysis revealed that two thirds of cases had low high-density lipoprotein cholesterol levels.