Diagnosis Of Coronary Heart Disease Research Articles

Improved Cascade-RCNN for automatic detection of coronary artery plaque in multi-angle fusion CPR images

Coronary heart disease is a disease that seriously endangers human health and life which is caused by plaque formation due to coronary artery atherosclerosis or spasm. The detection of coronary plaques through medical imaging is a non-destructive and fast diagnostic method, which holds significant medical and clinical value for the diagnosis of coronary heart disease. In this paper, We proposed an automatic detecting method for coronary artery vulnerable plaque based on CTA image sequence. We collected CTA image sequences from 132 patients and obtained the coronary artery tree by performing three-dimensional (3D) segmentation. The coronary artery centerline was extracted using the skeleton thinning method. To address the issue of insufficient features in a single CPR image, we constructed Curved Planar Reconstructed (CPR) images from multiple directions perpendicular to the coronary artery, following the centerline. In total, we established 8797 images in the training dataset and 1553 images in the test dataset. We proposed an attentive detection method that fused a new Cascade RCNN ResNet50-FPN neural network model with a convolutional attention module (CBAM). This method achieved automatic recognition and detection of plaques on the two-dimensional multi-angle fused CPR images. The proposed ResNet50-FPN network performed feature extraction, and CBAM was added after the first and last convolutional layers to enhance the network’s attention to plaque features. Our experimental results demonstrate that deep learning neural networks can provide a feasible approach to automatically detecting coronary artery plaques. Our method achieved a plaque detection accuracy of 94.6%, outperforming the RCNN method proposed by Zriek.

Diagnostic and prognostic value of serum Cys-C, retinol-binding protein, and ischemia-modified albumin in patients with coronary heart disease: A diagnostic accuracy study.

The use of 3 biomarkers - cystatin-C (Cys-C), retinol-binding protein (RBP), and ischemia-modified albumin (IMA) - for the clinical classification and outcome of coronary heart disease (CHD) has not been adequately evaluated. We explored the serum levels of these 3 markers and evaluated their diagnostic and prognostic values in patients with CHD. This retrospective case-control study, conducted between June 2017 and June 2018, included 201 patients with CHD hospitalized at the Henan Provincial People's Hospital and 127 healthy individuals from Henan Provincial People's Hospital as controls. Cys-C, RBP, IMA levels, and other laboratory parameters in the 2 groups were determined, and patient outcomes were analyzed. Cys-C, RBP, and IMA levels were higher in the case group than in the control group (P < .05). Logistic regression analysis confirmed that these 3 biomarkers were independent risk factors for CHD. Each indicator has clinical significance in the diagnosis and prognosis of CHD, with RBP being the most significant. The AUC value for CHD detection using a combination of the 3 indicators was 0.783, and the sensitivity and specificity values were 78% and 74.6%, respectively. Simultaneous detection of Cys-C, RBP, and IMA could be an optimal method for early diagnosis and prognosis of CHD.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

HighlightsPhotoresponsive black phosphorus (BP)/Ti3C2Tx composites were synthesized by a self-assembly strategy.Enhanced gas sensitive property was achieved by visible light modulation.Light activated virtual sensor array was fabricated based on BP/Ti3C2Tx composite.Diagnosis of coronary heart disease was achieved with the help of machine learning.

Understanding the disconnect between lifestyle advice and patient engagement: a discourse analysis of how expert knowledge is constructed by patients with CHD

Objective Adherence to healthy lifestyle advice is effective in prevention of non-communicable diseases like coronary heart disease (CHD). Yet patient disengagement is the norm. We take a novel discursive approach to explore patients’ negotiation of lifestyle advice and behaviour change. Method A discourse analysis was performed on 35 longitudinal interviews with 22 heterosexual British people in a long-term relationship, where one had a diagnosis of CHD. The analysis examined the relationships between patients’ constructions of expert knowledge and the implications of these accounts for patients’ dis/engagement with lifestyle advice. Results Expert knowledge was constructed in four ways: (1) Expert advice was valued, but adherence created new risks that undermined it; (2) expert knowledge was problematised as multiple, contradictory, and contested and therefore difficult to follow; (3) expert advice was problematised as too generalised to meet patients’ specific needs; and (4) expert advice was understood as limited and only one form of valued knowledge. Conclusion Patients and partners simultaneously valued and problematised expert knowledge, drawing on elaborate lay epistemologies relating to their illness which produced complex patterns of (dis)engagement with expert lifestyle advice. Recognition of the multiple and fluid forms of knowledge mobilised by CHD patients could inform more effective interventions.

Peer Support Interventions Improve Disease Self-Management After Coronary Heart Disease Diagnosis: A Systematic Review and Meta-Analysis

Peer Support Interventions Improve Disease Self-Management After Coronary Heart Disease Diagnosis: A Systematic Review and Meta-Analysis

The use of genetic algorithm and particle swarm optimization on tiered feature selection method in machine learning-based coronary heart disease diagnosis system

Coronary heart disease (CHD) is a leading global cause of death. Early detection is the right step to reduce mortality rates and treatment costs. Early detection can be developed using machine learning by utilizing patient medical record datasets. Unfortunately, this dataset has excessive features which can reduce machine learning performance. For this reason, it is necessary to reduce the number of redundant features and irrelevant data to improve machine learning performance. Therefore, this research proposes a tiered of feature selection model with genetic algorithm (GA) and particle swarm optimization (PSO) to improve the performance of the diagnosis model. The feature selection model is evaluated using parameters derived from the confusion matrix and using the CatBoost machine learning algorithm. Model testing uses z-Alizadeh Sani, Cleveland, Statlog, and Hungarian datasets. The best results for this model were obtained on the z-Alizadeh Sani dataset with 6 selected features from 54 features and the resulting performance for accuracy parameters was 99.32%, specificity 98.57%, sensitivity 100.00%, area under the curve (AUC) 99.28%, and F1-Score 99.37%. The proposed feature selection model is able to provide machine learning performance in the very good category. The diagnostic model proposed is of excellent standard.

Immature Surfactant Protein Type B and Surfactant Protein Type D Correlate with Coronary Heart Disease in Patients with Type 2 Diabetes.

Different specific surfactant proteins (SPs) have been associated with various pathological conditions, not only of the respiratory system, but also more recently with cardiovascular diseases, such as heart failure. The aim of the present study was to evaluate the role of SP-A, SP-D, and the precursor protein of SP-B (proSP-B) in the pathogenesis of cardiovascular damage in patients affected by type 2 diabetes (T2D). The study considered 31 patients with T2D (DN group), 34 patients with both T2D and coronary heart disease (CHD) (DC group), and 30 patients without diabetes but with a diagnosis of CHD (NC group). SP-A, SP-D, and proSP-B concentrations were determined in plasma samples, and were statistically compared using parametric and multivariate methods. Higher plasma concentrations of SP-D and proSP-B were found in patients affected by both T2D and CHD (DC group), and in patients with CHD without diabetes (NC group), in comparison to T2D patients (DN group). A significant correlation, both with linear regression (r = 0.3565, p = 0.001) and Principal Component Analysis (PCA), was found between the plasma levels of SP-D and proSP-B in the overall cohort of patients. No differences in SP-A were observed among the three groups of subjects. The present study extends the knowledge on the role of plasma SPs' levels as possible indicators of the risk of CHD being linked to T2D disease progression.

Progress And Challenges of Coronary Angiography in The Diagnosis and Treatment of Coronary Heart Disease

Progress And Challenges of Coronary Angiography in The Diagnosis and Treatment of Coronary Heart Disease

Novel Diagnostic Biomarkers Related to Necroptosis and Immune Infiltration in Coronary Heart Disease.

Necroptosis, a monitored form of inflammatory cell death, contributes to coronary heart disease (CHD) progression. This study examined the potential of using necroptosis genes as diagnostic markers for CHD and sought to elucidate the underlying roles. Through bioinformatic analysis of GSE20680 and GSE20681, we first identified the differentially expressed genes (DEGs) related to necroptosis in CHD. Hub genes were identified using least absolute shrinkage and selection operator (LASSO) regression and random forest analysis after studying immune infiltration and transcription factor-miRNA interaction networks according to the DEGs. Quantitative polymerase chain reaction and immunohistochemistry were used to further investigate hub gene expression in vivo, for which a diagnostic model was constructed and the predictive efficacy was validated. Finally, the CHD group was categorized into high- and low-score groups in accordance with the single-sample gene set enrichment analysis (ssGSEA) score of the necroptosis genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, GSEA, and further immune infiltration analyses were performed on the two groups to explore the possible roles of hub genes. Based on the results of the LASSO regression and random forest analyses, four genes were used to construct a diagnostic model to establish a nomogram. Additionally, an extensive analysis of all seventeen necroptosis genes revealed notable distinctions in expression between high-risk and low-risk groups. Evaluation of immune infiltration revealed that neutrophils, monocytes, B cells, and activated dendritic cells were highly distributed in the peripheral blood of patients with CHD. Specifically, the high CHD score group exhibited greater neutrophil and monocyte infiltration. Conversely, the high-score group showed lower infiltration of M0 and M2 macrophages, CD8+ T, plasma, and resting mast cells. TLR3, MLKL, HMGB1, and NDRG2 may be prospective biomarkers for CHD diagnosis. These findings offer plausible explanations for the role of necroptosis in CHD progression through immune infiltration and inflammatory response.

Cirrhotic cardiomyopathy – Clinically fact or academic curiosity? Review. Part 2: ECG, functional tests, images, biomarkers, screening for coronary heart disease and differentianting diagnosis

The diagnosis of Cirrhotic Cardiomyopathy is based on severe hepatic cirrosis with deterioration of cardiac function without previous cardiopathy, but this is subclinical during a long time. In this second part we review the non-invasive diagnostic methods and their prognostic value in patients with or without hepatic transplant, from ECG to cardiac images of magnetic resonance.

Serum cystatin C, monocyte/high-density lipoprotein-C ratio, and uric acid for the diagnosis of coronary heart disease and heart failure

BACKGROUND Coronary heart disease (CHD) and heart failure (HF) are the major causes of morbidity and mortality worldwide. Early and accurate diagnoses of CHD and HF are essential for optimal management and prognosis. However, conventional diagnostic methods such as electrocardiography, echocardiography, and cardiac biomarkers have certain limitations, such as low sensitivity, specificity, availability, and cost-effectiveness. Therefore, there is a need for simple, noninvasive, and reliable biomarkers to diagnose CHD and HF. AIM To investigate serum cystatin C (Cys-C), monocyte/high-density lipoprotein cholesterol ratio (MHR), and uric acid (UA) diagnostic values for CHD and HF. METHODS We enrolled 80 patients with suspected CHD or HF who were admitted to our hospital between July 2022 and July 2023. The patients were divided into CHD (n = 20), HF (n = 20), CHD + HF (n = 20), and control groups (n = 20). The serum levels of Cys-C, MHR, and UA were measured using immunonephelometry and an enzymatic method, respectively, and the diagnostic values for CHD and HF were evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS Serum levels of Cys-C, MHR, and UA were significantly higher in the CHD, HF, and CHD + HF groups than those in the control group. The serum levels of Cys-C, MHR, and UA were significantly higher in the CHD + HF group than those in the CHD or HF group. The ROC curve analysis showed that serum Cys-C, MHR, and UA had good diagnostic performance for CHD and HF, with areas under the curve ranging from 0.78 to 0.93. The optimal cutoff values of serum Cys-C, MHR, and UA for diagnosing CHD, HF, and CHD+HF were 1.2 mg/L, 0.9 × 109, and 389 µmol/L; 1.4 mg/L, 1.0 × 109, and 449 µmol/L; and 1.6 mg/L, 1.1 × 109, and 508 µmol/L, respectively. CONCLUSION Serum Cys-C, MHR, and UA are useful biomarkers for diagnosing CHD and HF, and CHD+HF. These can provide information for decision-making and risk stratification in patients with CHD and HF.

Diagnostic value of multimodal cardiovascular imaging technology coupled with biomarker detection in elderly patients with coronary heart disease

Aims/Background Coronary heart disease is a common disease in the elderly and has a complex pathogenesis, which complicates the clinical diagnostic process. Thus, enhancing the diagnostic efficiency for coronary heart disease is imperative to improve the life expectancy of the elderly. This study aimed to explore the diagnostic value of multimodal cardiovascular imaging technology coupled with biomarker detection in elderly patients with coronary heart disease. Methods The medical records of 421 patients with suspected coronary heart disease obtained from the geriatric department of the First Affiliated Hospital of Hebei North University from February 2020 to February 2023 were retrospectively analysed. After excluding 10 patients who did not meet the inclusion criteria, the remaining 411 patients were included in this study. The included subjects had undergone coronary computed tomography angiography and were divided into coronary heart disease group (n=208) and non-coronary heart disease group (n=203) according to the diagnostic results. Multimodal cardiovascular imaging (coronary computed tomography angiography and echocardiography) and detection of serum biomarkers such as small dense low-density lipoprotein, lipoprotein a, and gamma-glutamyl transferase were performed in both groups. The clinical indicators of the two groups were compared, and the combined diagnostic efficacy of multimodal cardiovascular imaging and biomarker detection was evaluated. Results Compared to the non-coronary heart disease group, the coronary heart disease group had significantly higher levels of maximum area stenosis, total plaque volume, total plaque burden and fibrotic plaque volume (p &lt; ..001), and lower left ventricular ejection fraction level (p &lt; ..001). Additionally, the coronary heart disease group exhibited higher levels of left ventricular end-diastolic volume, left ventricular end-systolic volume and stroke volume than the non-coronary heart disease group (p &lt; ..001), and had higher levels of small dense low-density lipoprotein, lipoprotein a and gamma-glutamyl transferase (p &lt; ..001). Our results demonstrated that combined diagnosis had better diagnostic efficacy than individual approaches, marked by higher area under the curve and sensitivity of the former (p &lt; ..001). Conclusion Multimodal cardiovascular imaging technology combined with biomarker detection can distinctly improve the accuracy of coronary heart disease diagnosis in elderly patients.

The Degrees of Coronary Heart Disease and the Degrees of New-Onset Blepharitis: A Nationwide Cohort Study.

In this study, we aimed to evaluate the association between the severity of coronary heart disease (CHD) and the subsequent severity of blepharitis. This retrospective population-based cohort study was conducted using the National Health Insurance Research Database (NHIRD) of Taiwan. The participants with a CHD diagnosis were divided into mild CHD and severe CHD groups at a 1:2 ratio, according to whether percutaneous coronary intervention (PCI) was performed. The main outcomes were the development of blepharitis and severe blepharitis with the application of antibiotics. Cox proportional hazard regression was performed to obtain the adjusted hazard ratio (aHR) for blepharitis, with a 95% confidence interval (CI) between the groups. There were 22,161 and 15,369 blepharitis events plus 9597 and 4500 severe blepharitis episodes in the mild and severe CHD groups, respectively. The severe CHD group showed a significantly higher incidence of blepharitis development (aHR, 1.275; 95% CI: 1.051-1.912, p = 0.0285), whereas the incidence of severe blepharitis was not significantly different between the groups (aHR, 0.981; 95% CI: 0.945-1.020, p = 0.3453). The cumulative probability of blepharitis was significantly higher in the severe CHD group than in the mild CHD group (p < 0.001). In the subgroup analyses, the correlation between severe CHD and blepharitis was more significant in patients older than 70 years compared to the younger group (p = 0.0115). In conclusion, severe CHD is associated with a higher incidence of blepharitis than mild CHD, and this correlation is more prominent in individuals older than 70 years.

Early diagnostic value of carotid artery ultrasound parameters combined with epicardial adipose layer thickness in coronary heart disease

BACKGROUND Coronary heart disease is associated with coronary atherosclerosis indicated by carotid intima-media thickness (CIMT) thickening and altered vascular elasticity. The epicardial adipose layer can secrete proinflammatory factors that promote the formation of coronary atherosclerosis. Thus, the epicardial fat layer thickness (EAT) may also predict coronary heart disease. AIM To determine the role of common carotid artery ultrasound parameters and EAT in the early diagnosis of coronary artery disease. METHODS Based on coronary angiography, patients with newly suspected coronary heart disease were divided into case (n = 107) and control (n = 41) groups. The carotid ultrasound parameters, including vascular stiffness (β), elastic coefficient (EP), pulse wave conduction velocity (PWV-β), CIMT, and EAT were compared between the case and control groups and among patients with different lesion numbers in the case group. Pearson correlation was used to evaluate the early diagnostic value of EAT, common carotid artery elasticity, and CIMT for coronary heart disease. RESULTS EP, β, PWV-β, CIMT, and EAT were significantly higher in the case group compared with the levels in the control group (all P &lt; 0.001). In the case group, lesions were detected in one vessel in 34 patients, two vessels in 38 patients, and three vessels in 35 patients. Within the case group, β, EP, PWV-β, CIMT, and EAT levels significantly increased with an increased number of lesions (all P &lt; 0.001). EAT positively correlated with β, EP, PWV-β, and CIMT (all P &lt; 0.01). The area under the curve for diagnosing coronary heart disease using EAT combined with CIMT and carotid elasticity was 0.893, and the sensitivity and specificity were 0.890 and 0.837. CONCLUSION EAT correlated well with changes in carotid artery elasticity and CIMT in patients with coronary heart disease. The combination of EAT, carotid artery elasticity, and CIMT facilitates the early diagnosis of coronary heart disease.

Preliminary Study on Diagnosis of Coronary Heart Disease Based on Four Diagnostic Information

Preliminary Study on Diagnosis of Coronary Heart Disease Based on Four Diagnostic Information

#1458 Adverse muscle composition predicts all-cause mortality in CKD

Abstract Background and Aims Sarcopenia is common in CKD and associates with morbidity and mortality. A rapid and standardized MRI (Magnetic Resonance Imaging) scan protocol allows an accurate muscle composition analysis by quantification of skeletal muscle volume and muscle fat infiltration (MFI). The combined observation of low muscle volume and high muscle fat infiltration, i.e. adverse muscle composition (AMC), is a prevalent muscle composition phenotype within CKD and associates to poor function, high prevalence of comorbidity, and an increased risk for coronary heart disease (CHD). It has also been shown that AMC is an independent predictor of all-cause mortality in patients with non-alcoholic fatty liver disease and in general population. In this study we aimed to investigate muscle composition as predictor of all-cause mortality within participants with CKD in the UK Biobank imaging study (UKB). Method This is a prospective study including UKB participants with CKD defined as having an eGFR cystatin C &amp;lt; 60 ml/min/1, 73 m2. A control group was created by matching each participant with CKD for sex, age, and BMI to four participants with normal eGFR. Cystatin C was analyzed based on blood samples at time of study enrolment 7-9 years prior to the MRI. Fat-tissue free thigh muscle volume and MFI were quantified using a rapid neck-to-knee water and fat separated MRI protocol and automated image analysis (AMRA® Researcher). For each participant, a personalized muscle volume z-score (sex- and body size-specific) was calculated and combined with MFI to divide the participants into four muscle composition phenotype groups; 1) normal muscle composition, 2) only low muscle volume, 3) only high MFI and 4) AMC, using previously published thresholds based on the whole UKB cohort. The thresholds were the 25th percentile (&amp;lt; -0.68 SD) for muscle volume z-score and the 75th percentile (men &amp;gt;7.69%; women &amp;gt;8.82%) for MFI. The mortality data were obtained through the UKB´s linkage to national death registries. All-cause mortality was investigated using Kaplan-Meier curves and Cox regression. Models within participants with CKD were crude and subsequently adjusted for sex, age, BMI, low hand grip strength (&amp;lt;16/27 kg for females/males), physical activity, smoking, alcohol, and previous diagnosis of cancer, prevalent CHD and type 2 diabetes, all assessed at the time of imaging. Results Conclusion AMC, determined by standardized MRI, was a strong predictor of all-cause mortality in CKD. The study results indicate that CKD patients with poor muscle health, identified as having both low muscle volume z-score and high MFI, have a significantly higher mortality risk. This vulnerable group would potentially benefit from targeted interventions and is also of specific interest when evaluating new treatments for CKD.

Polychlorinated biphenyls and incident coronary heart disease-related outcomes in Great Lakes fish consumers

BackgroundExposure to polychlorinated biphenyls (PCBs) has been linked to risk factors for cardiovascular disease such as increased inflammation, accelerated atherosclerosis, diabetes, and sex hormone dysregulation. Furthermore, there is increasing evidence suggesting associations between internal dose of PCBs and cardiovascular outcomes. ObjectivesThe purpose of this study is to investigate longitudinal associations of PCBs with coronary heart disease (CHD)-related outcomes in a cohort of Great Lakes sport fish consumers. MethodsThe Great Lakes Sport Fish Consumer cohort was established in the early 1990's. Eight hundred nineteen participants were followed from 1993 to 2017. Serum PCBs were measured in 1994/1995 (baseline), in 2001, and in 2004, while health history questionnaires were administered in 1996, 2003, 2010, and 2017. Cox models were used to prospectively investigate associations of total PCBs and PCB groupings, based on aryl hydrocarbon receptor activity, with incident self-reported physician diagnosis of coronary heart disease (CHD), myocardial infarction (MI), and angina pectoris. ResultsA 2-fold increase in phenobarbital-type PCBs was associated with a 72% increase in likelihood of self-reported incident diagnosis of CHD (HR=1.72, 95% CI: 1.06–2.81; p=0.0294). Similar results were observed for total PCBs (HR=1.68, 95% CI: 1.05–2.69; p=0.0306) and mixed methacholine/phenobarbital type (mixed-type) PCBs (HR=1.60, 95% CI: 1.02–2.52; p=0.0427), but not methacholine-type PCBs. PCBs were not strongly associated with risk of MI or angina. ConclusionsThis study presents evidence that exposure to PCBs increases the risk of developing coronary heart disease. Given the large number of risk factors and causal pathways for CHD, future research is required to better understand biological mechanisms of action for PCBs on CHD.

Prediction model based on artificial intelligence for identifying risk of coronary atherosclerotic heart disease in computed tomography

ObjectiveTo analyze the application value of artificial intelligence (AI) in coronary computed tomography angiography (CCTA) image processing and diagnosis of coronary atherosclerotic heart disease (CHD). MethodsA total of 80 patients with suspected CHD in our hospital were selected for CCTA examination and blood lipid examination. The convolutional neural networks (CNN) model of coronary artery plaque detection was constructed, and the data set was randomly divided into training set and test set after pretreatment of lipid characteristics and image characteristics. The prediction efficiency and accuracy of the model were evaluated. ResultsIn the data set, the lipid indexes LDL-C, TC, and TG of patients in the CHD group were significantly higher than those in the Non-CHD group (P < 0.05). The average processing and diagnosis time of the AI model was (187.19 ± 18.79) s, which was significantly shorter than the average time of doctors (989.07 ± 50.40) s, and the difference was statistically significant (P < 0.05). There was no significant difference in the detection of calcified plaque, non-calcified plaque, and mixed plaque between doctors and AI models (P > 0.05). However, 5 plaques were misdiagnosed in the AI model (3.38%). The area under the curve (AUC) value of the CNN recognition model-based AI and manual recognition of doctors for the CHD were 0.870 (95% CI: 0.698–0.931) and 0.870 (95% CI: 0.691–0.926) (P < 0.001). ConclusionAI integrated with lipid parameters has certain clinical value in CCTA image processing efficiency and plaque diagnosis, and can be used as an effective auxiliary tool to analyze and diagnose CHD.

Possibilities of noninvasive diagnostics of cardiovascular diseases associated with atherosclerosis

Background. Despite the global trend towards a decrease in mortality from cardiovascular pathology of atherosclerotic genesis, this group of diseases continues to occupy a leading position in the structure of disability and mortality among the population of many countries. Aim. Identification of noninvasive markers of arterial wall lesions associated with the presence of arterial hypertension (AH) and coronary heart disease (CHD) in persons younger than 55 years. Materials and methods. The study included 81 people. Three groups were distinguished from them: 1st (n=31) – a control group of practically healthy individuals, average age – 43 (40; 48) years; 2nd group (n=21) – patients with coronary heart disease without a history of cardiovascular diseases (CVD), average age – 45,5±6,1; group 3 (n=29) – patients with AH without CHD, average age – 47 (35; 50) years. The parameters of a biochemical blood test were determined, volumetric sphygmography and a sample with post-occlusive reactive hyperemia were performed. Results. In groups of patients with CHD and AH, negative changes in the lipid profile and higher glucose levels were recorded compared to healthy individuals. According to the results of volumetric sphygmography, the pulse wave velocity in arteries of predominantly elastic type (R/L-PWV) in group 1 was 10.1±1.7 m/s, in group 2 – 12.9±1.8 m/s, in group 3 – 13.1 (12; 14) m/s (р1–2=0.029; р1–30.001); cardio-ankle vascular index (CAVI) – 6.5±0.7, 7.7 (7; 8.7) and 7.8 (7.3; 8.4), respectively (р1–20.001; р1–30.001). According to the data of the test with post-occlusive reactive hyperemia, a comparably high frequency of pathological values of flow-dependent vasodilation (FDV) and reactivity index (RI) was demonstrated in patients with CHD and AH (р1–20.05; р1–30.05). A multifactorial model of noninvasive diagnosis of CHD in individuals without a history of CVD, including non-high-density lipoprotein cholesterol, R/L-PWV, and FDV, has been developed. For patients with AH, glucose, lipid metabolism, R/L-PWV, pulse wave velocity in muscle-type arteries (B-PWV), augmentation index (AI), biological age, CAVI, FDV, RI were the risk factors for the presence of the disease in the anamnesis. Conclusion. The results obtained confirm the importance of an integrated approach in the screening diagnosis of CVD.

The Use of Precision Epigenetic Methods for the Diagnosis and Care of Stable Coronary Heart Disease Reduces Healthcare Costs.

The cost of secondary prevention of coronary heart disease (CHD) is continuing to increase, with a substantial portion of this acceleration being driven by the expense of confirmatory diagnostic testing. Conceivably, newly developed precision epigenetic technologies could drive down these costs. However, at the current time, their impact on overall expense for CHD care is poorly understood. We hypothesized that the use of a newly developed, highly sensitive, and specific epigenetic test, PrecisionCHD, could decrease the costs of secondary prevention. To test this hypothesis, we constructed a budget impact analysis using a cost calculation model that examined the effects of substituting PrecisionCHD for conventional CHD diagnostic tests on the expenses of the initial evaluation and first year of care of stable CHD using a 1-year time horizon with no discounting. The model projected that for a commercial insurer with onemillion members, full adoption of PrecisionCHD as the primary method of initial CHD assessment would save approximately $113.6million dollars in the initial year. These analyses support the use of precision epigenetic methods as part of the initial diagnosis and care of stable CHD and can meaningfully reduce cost. Real-world pilots to test the reliability of these analyses are indicated.