Risk factors and major adverse cardiovascular events of isolated coronary artery ectasia: An observational study.

To evaluate the cardiac index and major adverse cardiovascular events (MACE) events between isolated coronary artery ectasia (CAE) and control groups over 1 year period from diagnosis. A total of 18 patients who were diagnosed with isolated CAE in the Second Hospital of Hebei Medical University from December 2020 to December 2021 were included in CAE group. About 36 patients with non-obstructive coronary artery lesions were included in the control group. All patients in 2 groups completed dobutamine stress echocardiography (DSE) during hospitalization. The chamber size, wall thickness, left ventricular ejection fraction, and left ventricular diastolic function indicators (including E/A ratio, e', and E/e' ratio) were measured. MACE and all-cause death were measured during follow-up after discharge. Interventricular septum thickness (IVSd), left ventricular posterior wall (LVPW) thickness in diastole and E/e' in CAE group were significantly higher than control group (P < .05). No significant differences were found in prognosis including angina, myocardial ischemia (MI), patient readmission and cardiovascular death (P > .05). In CAE group, coronary angiography showed dilation of left anterior descending (LAD) in 1 case, left circumflex (LCX) in 3 cases and right coronary artery (RCA) in 14 cases. Multivariate logistic regression analysis showed that BMI and IVSd were independent risk factors for CAE. IVSd, LVPW thickness in diastole and E/e' in CAE group were significantly higher than control group. BMI and IVSd were independent risk factors for isolated CAE, and had a good predictive value for isolated CAE.

The predictive value of lesion-specific pericoronary fat attenuation index for major adverse cardiovascular events in patients with type 2 diabetes.

BACKGROUND: The purpose of this study was to explore the prognostic significance of the lesion-specific pericoronary fat attenuation index (FAI) in forecasting major adverse cardiovascular events (MACE) among patients with type 2 diabetes mellitus (T2DM). METHODS: This study conducted a retrospective analysis of 304 patients diagnosed with T2DM who underwent coronary computed tomography angiography (CCTA) in our hospital from December 2011 to October 2021. All participants were followed for a period exceeding three years. Detailed clinical data and CCTA imaging features were carefully recorded, encompassing lesion-specific pericoronary FAI, FAI of the three prime coronary arteries, features of high-risk plaques, and the coronary artery calcium score (CACS). The MACE included in the study comprised cardiac death, acute coronary syndrome (which encompasses unstable angina pectoris and myocardial infarction), late-phase coronary revascularization procedures, and hospital admissions prompted by heart failure. RESULTS: Within the three-year follow-up, 76 patients with T2DM suffered from MACE. The lesion-specific pericoronary FAI in patients who experienced MACE was notably higher compared to those without MACE (-84.87 ± 11.36 Hounsfield Units (HU) vs. -88.65 ± 11.89 HU, p = 0.016). Multivariate Cox regression analysis revealed that CACS ≥ 100 (hazard ratio [HR] = 4.071, 95% confidence interval [CI] 2.157-7.683, p < 0.001) and lesion-specific pericoronary FAI higher than - 83.5 HU (HR = 2.400, 95% CI 1.399-4.120, p = 0.001) were independently associated with heightened risk of MACE in patients with T2DM over a three-year period. Kaplan-Meier analysis showed that patients with higher lesion-specific pericoronary FAI were more likely to develop MACE (p = 0.0023). Additionally, lesions characterized by higher lesion-specific pericoronary FAI values were found to have a greater proportion of high-risk plaques (p = 0.015). Subgroup analysis indicated that lesion-specific pericoronary FAI higher than - 83.5 HU (HR = 2.017, 95% CI 1.143-3.559, p = 0.015) was independently correlated with MACE in patients with T2DM who have moderate to severe coronary calcification. Moreover, the combination of CACS ≥ 100 and lesion-specific pericoronary FAI>-83.5 HU significantly enhanced the predictive value of MACE in patients with T2DM within 3 years. CONCLUSIONS: The elevated lesion-specific pericoronary FAI emerged as an independent prognostic factor for MACE in patients with T2DM, inclusive of those with moderate to severe coronary artery calcification. Incorporating lesion-specific pericoronary FAI with the CACS provided incremental predictive power for MACE in patients with T2DM.

Epigenetics of hypertension as a risk factor for the development of coronary artery disease in type 2 diabetes mellitus.

Hypertension, a multifaceted cardiovascular disorder influenced by genetic, epigenetic, and environmental factors, poses a significant risk for the development of coronary artery disease (CAD) in individuals with type 2 diabetes mellitus (T2DM). Epigenetic alterations, particularly in histone modifications, DNA methylation, and microRNAs, play a pivotal role in unraveling the complex molecular underpinnings of blood pressure regulation. This review emphasizes the crucial interplay between epigenetic attributes and hypertension, shedding light on the prominence of DNA methylation, both globally and at the gene-specific level, in essential hypertension. Additionally, histone modifications, including acetylation and methylation, emerge as essential epigenetic markers linked to hypertension. Furthermore, microRNAs exert regulatory influence on blood pressure homeostasis, targeting key genes within the aldosterone and renin-angiotensin pathways. Understanding the intricate crosstalk between genetics and epigenetics in hypertension is particularly pertinent in the context of its interaction with T2DM, where hypertension serves as a notable risk factor for the development of CAD. These findings not only contribute to the comprehensive elucidation of essential hypertension but also offer promising avenues for innovative strategies in the prevention and treatment of cardiovascular complications, especially in the context of T2DM.

Impact of hyperaemic stenosis resistance on long-term outcomes of stable angina in the ILIAS Registry.

BACKGROUND: The hyperaemic stenosis resistance (HSR) index was introduced to provide a more comprehensive indicator of the haemodynamic severity of a coronary lesion. HSR combines both the pressure drop across a lesion and the flow through it. As such, HSR overcomes the limitations of the more traditional fractional flow reserve (FFR) or coronary flow reserve (CFR) indices. AIMS: We aimed to identify the diagnostic and prognostic value of HSR and evaluate the clinical implications. METHODS: Patients with chronic coronary syndromes (CCS) and obstructive coronary artery disease were selected from the multicentre ILIAS Registry. For this study, only patients with combined Doppler flow and pressure measurements were included. RESULTS: A total of 853 patients with 1,107 vessels were included. HSR more accurately identified the presence of inducible ischaemia compared to FFR and CFR (area under the curve 0.71 vs 0.66 and 0.62, respectively; p<0.005 for both). An abnormal HSR measurement was an independent and important predictor of target vessel failure at 5-year follow-up (hazard ratio 3.80, 95% confidence interval: 2.12-6.73; p<0.005). In vessels deferred from revascularisation, HSR seems to identify more accurately those vessels that may benefit from revascularisation rather than FFR and/or CFR. CONCLUSIONS: The present study affirms the theoretical advantages of the HSR index for the detection of ischaemia-Âinducing coronary lesions in a large CCS population. (Inclusive Invasive Physiological Assessment in Angina Syndromes Registry [ILIAS Registry], ClinicalTrials.gov: NCT04485234).

Vulnerable plaque features and adverse events in patients with diabetes mellitus: a post hoc analysis of the COMBINE OCT-FFR trial.

BACKGROUND: Thin-cap fibroatheroma (TCFA) lesions are associated with a high risk of future major adverse cardiovascular events. However, the impact of other optical coherence tomography-detected vulnerability features (OCT-VFs) and their interplay with TCFA in predicting adverse events remains unknown. AIMS: We aimed to evaluate the individual as well as the combined prognostic impact of OCT-VFs in predicting the incidence of the lesion-oriented composite endpoint (LOCE) in non-ischaemic lesions in patients with diabetes mellitus (DM). METHODS: COMBINE OCT-FFR (ClinicalTrials.gov: NCT02989740) was a prospective, double-blind, international, natural history study that included DM patients with ≥1 non-culprit lesions with a fractional flow reserve>0.80 undergoing systematic OCT assessment. OCT-VFs included the following: TCFA, reduced minimal lumen area (r-MLA), healed plaque (HP), and complicated plaque (CP). The primary endpoint, LOCE - a composite of cardiac mortality, target vessel myocardial infarction, or clinically driven target lesion revascularisation up to 5 years - was analysed according to the presence of these OCT-VFs, both individually and in combination. RESULTS: TCFA, r-MLA, HP and CP were identified in 98 (25.3%), 190 (49.0%), 87 (22.4%), and 116 (29.9%) patients, respectively. The primary endpoint rate increased progressively from 6.3% to 55.6% (hazard ratio 15.2, 95% confidence interval: 4.53-51.0; p<0.001) in patients without OCT-VFs as compared to patients with concomitant HP, r-MLA, CP, and TCFA. The coexistence of TCFA with other OCT-VFs resulted in an increased risk of the LOCE at 5 years. CONCLUSIONS: In DM patients with non-ischaemic lesions, TCFA was the strongest predictor of future LOCE events. However, lesions that present additional OCT-VFs are associated with a higher risk of adverse events than OCT-detected TCFA alone. Further randomised studies are warranted to confirm these findings and their potential clinical implications.

Effects of age on microvascular function in patients with normal coronary arteries.

BACKGROUND: It has been suggested that coronary microvascular function decreases with age, irrespective of the presence of epicardial atherosclerosis. AIMS: Our aim is to quantitatively investigate the effects of age on microvascular function in patients with normal coronary arteries. METHODS: In 314 patients with angina with no obstructive coronary artery disease (ANOCA), microcirculatory function was tested using the continuous thermodilution method. In 305 patients, the association between age and both resting and hyperaemic myocardial blood flow (Q), microvascular resistance (Rµ), absolute coronary flow reserve (CFR) and microvascular resistance reserve (MRR) was assessed. In addition, patients were divided into 3 groups to test for differences based on age quartiles (≤52 years [24.9%], 53-64 years [49.2%], ≥65 years [25.9%]). RESULTS: The mean age was 59±9 years with a range from 22 to 79 years. The mean resting Q (Qrest) was not different in the 3 age groups (88±34 mL/min, 82±29 mL/min, and 86±38 mL/min, R2=0.001; p=0.62). A trend towards a decreasing mean hyperaemic Q (Qmax) was observed with increasing age (223±79 mL/min, 209±84 mL/min, 200±80 mL/min, R2=0.010; p=0.083). The mean resting Rµ (Rµ,rest) were 1,204±460 Wood units (WU), 1,260±411 WU, and 1,289±455 WU (p=0.23). The mean hyperaemic Rµ (Rµ,hyp) increased significantly with advancing age (429±149 WU, 464±164 WU, 503±162 WU, R2=0.026; p=0.005). Consequently, MRR decreased with age (3.2±1.2, 3.1±1.0, 2.9±0.9; p=0.038). This trend was present in both the patients with (n=121) and without (n=184) coronary microvascular dysfunction (CMD). CONCLUSIONS: There is an age-dependent physiological increase in minimal microvascular resistance and decrease in microvascular function, which is represented by a decreased MRR and is independent of atherosclerosis. The age-dependent decrease in MRR was present in both patients with and without CMD and was most evident in patients with smooth coronary arteries.

Triglyceride-glucose index is associated with myocardial ischemia and poor prognosis in patients with ischemia and no obstructive coronary artery disease.

BACKGROUND: Ischemia and no obstructive coronary artery disease (INOCA) is increasingly recognized and associated with poor outcomes. The triglyceride-glucose (TyG) index is a reliable alternative measure of insulin resistance significantly linked to cardiovascular disease and adverse prognosis. We investigated the association between the TyG index and myocardial ischemia and the prognosis in INOCA patients. METHODS: INOCA patients who underwent both coronary angiography and myocardial perfusion imaging (MPI) were included consecutively. All participants were divided into three groups according to TyG tertiles (T1, T2, and T3). Abnormal MPI for myocardial ischemia in individual coronary territories was defined as summed stress score (SSS) ≥ 4 and summed difference score (SDS) ≥ 2. SSS refers to the sum of all defects in the stress images, and SDS is the difference of the sum of all defects between the rest images and stress images. All patients were followed up for major adverse cardiac events (MACE). RESULTS: Among 332 INOCA patients, 113 (34.0%) had abnormal MPI. Patients with higher TyG index had a higher rate of abnormal MPI (25.5% vs. 32.4% vs. 44.1%; p = 0.012). Multivariate logistic analysis showed that a high TyG index was significantly correlated with abnormal MPI in INOCA patients (OR, 1.901; 95% CI, 1.045-3.458; P = 0.035). During the median 35 months of follow-up, 83 (25%) MACE were recorded, and a higher incidence of MACE was observed in the T3 group (T3 vs. T2 vs. T1: 36.9% vs. 21.6% vs. 16.4%, respectively; p = 0.001). In multivariate Cox regression analysis, the T3 group was significantly associated with the risk of MACE compared to the T1 group (HR, 2.338; 95% CI 1.253-4.364, P = 0.008). CONCLUSION: This study indicates for the first time that the TyG index is significantly associated with myocardial ischemia and poor prognosis among INOCA patients.

High stress hyperglycemia ratio predicts adverse clinical outcome in patients with coronary three-vessel disease: a large-scale cohort study.

BACKGROUND: Coronary three-vessel disease (CTVD) accounts for one-third of the overall incidence of coronary artery disease, with heightened mortality rates compared to single-vessel lesions, including common trunk lesions. Dysregulated glucose metabolism exacerbates atherosclerosis and increases cardiovascular risk. The stress hyperglycemia ratio (SHR) is proposed as an indicator of glucose metabolism status but its association with cardiovascular outcomes in CTVD patients undergoing percutaneous coronary intervention (PCI) remains unclear. METHODS: 10,532 CTVD patients undergoing PCI were consecutively enrolled. SHR was calculated using the formula: admission blood glucose (mmol/L)/[1.59×HbA1c (%)-2.59]. Patients were divided into two groups (SHR Low and SHR High) according to the optimal cutoff value of SHR. Multivariable Cox regression models were used to assess the relationship between SHR and long-term prognosis. The primary endpoint was cardiovascular (CV) events, composing of cardiac death and non-fatal myocardial infarction (MI). RESULTS: During the median follow-up time of 3 years, a total of 279 cases (2.6%) of CV events were recorded. Multivariable Cox analyses showed that high SHR was associated with a significantly higher risk of CV events [Hazard Ratio (HR) 1.99, 95% Confidence interval (CI) 1.58-2.52, P < 0.001). This association remained consistent in patients with (HR 1.50, 95% CI 1.08-2.10, P = 0.016) and without diabetes (HR 1.97, 95% CI 1.42-2.72, P < 0.001). Additionally, adding SHR to the base model of traditional risk factors led to a significant improvement in the C-index, net reclassification and integrated discrimination. CONCLUSIONS: SHR was a significant predictor for adverse CV outcomes in CTVD patients with or without diabetes, which suggested that it could aid in the risk stratification in this particular population regardless of glucose metabolism status.

Disparity of gene expression in coronary artery disease: insights from MEIS1, HIRA, and Myocardin.

INTRODUCTION: Coronary artery disease (CAD) in young adults can have devastating consequences. The cardiac developmental gene MEIS1 plays important roles in vascular networks and heart development. This gene effects on the regeneration capacity of the heart. Considering role of MEIS1 in cardiac tissue development and the progression of myocardial infarction this study investigated the expression levels of the MEIS1, HIRA, and Myocardin genes in premature CAD patients compared to healthy subjects and evaluated the relationships between these genes and possible inflammatory factors. METHODS AND RESULTS: The study conducted a case-control design involving 35 CAD patients and 35 healthy individuals. Peripheral blood mononuclear cells (PBMCs) were collected, and gene expression analysis was performed using real-time PCR. Compared with control group, the number of PBMCs in the CAD group exhibited greater MEIS1 and HIRA gene expression, with fold changes of 2.45 and 3.6. The expression of MEIS1 exhibited a negative correlation with IL-10 (r= -0.312) expression and positive correlation with Interleukin (IL)-6 (r = 0.415) and tumor necrosis factor (TNF)-α (r = 0.534) gene expression. Moreover, there was an inverse correlation between the gene expression of HIRA and that of IL-10 (r= -0.326), and a positive correlation was revealed between the expression of this gene and that of the IL-6 (r = 0.453) and TNF-α (r = 0.572) genes. CONCLUSION: This research demonstrated a disparity in expression levels of MEIS1, HIRA, and Myocardin, between CAD and healthy subjects. The results showed that, MEIS1 and HIRA play significant roles in regulating the synthesis of proinflammatory cytokines, namely, TNF-α and IL-6.

Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography.

BACKGROUND: Current approaches for initial coronary artery disease (CAD) assessment rely on pretest probability (PTP) based on risk factors and presentations, with limited performance. Infrared thermography (IRT), a non-contact technology that detects surface temperature, has shown potential in assessing atherosclerosis-related conditions, particularly when measured from body regions such as faces. We aim to assess the feasibility of using facial IRT temperature information with machine learning for the prediction of CAD. METHODS: Individuals referred for invasive coronary angiography or coronary CT angiography (CCTA) were enrolled. Facial IRT images captured before confirmatory CAD examinations were used to develop and validate a deep-learning IRT image model for detecting CAD. We compared the performance of the IRT image model with the guideline-recommended PTP model on the area under the curve (AUC). In addition, interpretable IRT tabular features were extracted from IRT images to further validate the predictive value of IRT information. RESULTS: A total of 460 eligible participants (mean (SD) age, 58.4 (10.4) years; 126 (27.4%) female) were included. The IRT image model demonstrated outstanding performance (AUC 0.804, 95% CI 0.785 to 0.823) compared with the PTP models (AUC 0.713, 95% CI 0.691 to 0.734). A consistent level of superior performance (AUC 0.796, 95% CI 0.782 to 0.811), achieved with comprehensive interpretable IRT features, further validated the predictive value of IRT information. Notably, even with only traditional temperature features, a satisfactory performance (AUC 0.786, 95% CI 0.769 to 0.803) was still upheld. CONCLUSION: In this prospective study, we demonstrated the feasibility of using non-contact facial IRT information for CAD prediction.

Identifying risk factors and mortality rate of premature coronary artery disease in young Saudi population.

Coronary artery disease is a leading cause of morbidity and mortality worldwide. It occurs due to a combination of genetics, lifestyle, and environmental factors. Premature coronary artery disease (PCAD) is a neglected clinical entity despite the rising number of cases worldwide. This study aimed to investigate the risk factors of premature coronary artery disease. In this study, we searched articles that had studied the risk factors of premature coronary artery diseases from January 2000 to July 2022 in Saudi Arabia in Web of Science, Pub Med, Scopus, Springer, and Wiley databases. The final analysis is based on seven articles. The smoking prevalence was 39%, diabetes mellitus 41%, hypertension 33%, overweight and obesity 18%, family history of coronary artery disease (CAD) 19%, dyslipidemia 37%, and the prevalence range of low-density lipoprotein cholesterol was 33.8-55.0%. The results revealed a mortality prevalence of 4% ranging from 2 to 8% which is similar to the prevalence in older patients which was 2-10%. Smoking, diabetes mellitus, hypertension, family history of CAD, dyslipidemia, and overweight/obesity are significantly and positively associated with premature coronary artery diseases. The health authorities should design and implement an intensive and effective prophylactic plan to minimize the subsequent impact of PCAD on the young population. In addition, early diagnosis of PCAD has great value in providing timely treatment, managing the patients, and minimizing the burden of the disease.

Association between statin therapy and long-term clinical outcomes in patients with stable coronary disease undergoing percutaneous coronary intervention.

This longitudinal cohort study examined the long-term effect of statin therapy on clinical outcomes in patients undergoing percutaneous coronary intervention (PCI). A total of 1760 patients with stable coronary artery disease (CAD) were divided by receipt of statin therapy or not after index PCI. Baseline clinical characteristics, risk factors, angiographic findings, and medications after interventional procedure were assessed to compare long-term clinical outcomes between groups. Predictors for all-cause death and major adverse cardiovascular events (MACE), including myocardial infarction (MI), cardiovascular death, and repeated PCI procedures, were also analyzed. The statin therapy group had higher average serum cholesterol and more elevated low-density lipoprotein cholesterol (LDL-C) than the non-statin therapy group (189.0 ± 47.9 vs 169.3 ± 37.00 mg/dl, 117.2 ± 42.6 vs 98.7 ± 31.8 mg/dl, respectively, both P < 0.001). The non-statin group had higher rates of all-cause death and cardiovascular death compared to statin group (both P < 0.001). After adjustment for age, diabetes, and chronic kidney disease, Cox proportion hazard analysis revealed statin use significantly reduced all-cause death and repeated PCI procedure (hazard ratio: 0.53 and 0.69, respectively). Statin use seemed not reduce the hazard of cardiovascular death or MI in patients with stable CAD after PCI; however, statin therapy still was associated with reduced rates of all-cause death and repeat PCI procedure.

Effectiveness of the Green Heart Smartphone Application as a Self-Management Intervention for Hypertension and Dyslipidemia: A Randomized Clinical Trial.

BACKGROUND: Cardiovascular disease (CVD) is a major global health concern, the leading cause of death and disability. Thus, preventive interventions targeting modifiable risk factors are essential. Mobile-health technologies have emerged as promising tools for improving prevention by modifying risk factors. We created the "Green Heart" mobile app to help coronary artery disease (CAD) patients control their risk factors. The app has three modules: smoking cessation, dyslipidemia (DLP) control, and blood pressure (BP) management. This study evaluated the app's performance in monitoring hypertension (HTN) and DLP among known CAD cases. METHODS: A randomized controlled trial enrolled 1590 CAD subjects, including 1114 hypertensive patients and 1488 subjects with DLP, and assigned them randomly to paper-based education or application-based groups. RESULTS: Regarding HTN, after 6 months, we finally analyzed 545 and 546 hypertensive patients, assigned to the conventional and app groups, respectively. Patients in the app group were more likely to have their BP managed successfully (88.6% vs. 78.5%; P<0.001). The app group showed higher odds of successful BP management (odds ratio [OR]: 2.13; 95% CI: 1.51 - 3.03). Regarding DLP, we analyzed 728 patients in the conventional and 714 patients in the app group. A higher percentage of patients in the app group (24.8%) had low-density lipoprotein cholesterol (LDL-C) levels less than 70 mg/dL (16.1%; P<0.001). The app group showed higher odds of reducing LDL-C (OR: 1.72; 95% CI: 1.32-2.26). CONCLUSION: We found that using the Green Heart app in the self-monitoring setting significantly improved BP and DLP management across the study population.

Impacts of ACE insertion/deletion variant on cardiometabolic risk factors, premature coronary artery disease, and severity of coronary lesions.

Angiotensin-converting enzyme (ACE) is closely related to cardiometabolic risk factors and atherosclerosis. This study aims to investigate whether the insertion/deletion (I/D) variant of ACE gene impacts cardiometabolic risk factors, premature coronary artery disease (PCAD), and severity of coronary lesions. PubMed, Cochrane Library, Central, CINAHL, and ClinicalTrials.gov were searched until December 22, 2023. 94,270 individuals were included for the analysis. Carriers of DD genotype had higher levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), and waist circumference (WC) than carriers of II or ID genotypes. In addition, carriers of DD genotype were at high risk of PCAD and multiple vessel lesions. The impacts of ACE I/D variant on lipid levels were significant in American individuals but stronger in male individuals. In contrast, the impacts of ACE I/D variant on PCAD and severity of coronary lesions were primarily significant in Caucasian individuals. This study indicates that the ACE I/D variant has a slight but significant impact on cardiometabolic risk factors, PCAD, and severity of coronary lesions. Angiotensin-converting enzyme inhibitors (ACEI) may benefit high-risk populations with ACE DD genotype to prevent PCAD and multiple vessel lesions.PROSPERO registration number: CRD42023426732.

A lumped parameter model for evaluating coronary artery blood supply capacity.

The coronary artery constitutes a vital vascular system that sustains cardiac function, with its primary role being the conveyance of indispensable nutrients to the myocardial tissue. When coronary artery disease occurs, it will affect the blood supply of the heart and induce myocardial ischemia. Therefore, it is of great significance to numerically simulate the coronary artery and evaluate its blood supply capacity. In this article, the coronary artery lumped parameter model was derived based on the relationship between circuit system parameters and cardiovascular system parameters, and the blood supply capacity of the coronary artery in healthy and stenosis states was studied. The aortic root pressure calculated by the aortic valve fluid-structure interaction (AV FSI) simulator was employed as the inlet boundary condition. To emulate the physiological phenomenon of sudden pressure drops resulting from an abrupt reduction in blood vessel radius, a head loss model was connected at the coronary artery's entrance. For each coronary artery outlet, the symmetric structured tree model was appended to simulate the terminal impedance of the missing downstream coronary arteries. The particle swarm optimization (PSO) algorithm was used to optimize the blood flow viscous resistance, blood flow inertia, and vascular compliance of the coronary artery model. In the stenosis states, the relative flow and fractional flow reserve (FFR) calculated by numerical simulation corresponded to the published literature data. It was anticipated that the proposed model can be readily adapted for clinical application, serving as a valuable reference for diagnosing and treating patients.

Predictive nomogram model for severe coronary artery calcification in end-stage kidney disease patients.

INTRODUCTION: The Agatston coronary artery calcification score (CACS) is an assessment index for coronary artery calcification (CAC). This study aims to explore the characteristics of CAC in end-stage kidney disease (ESKD) patients and establish a predictive model to assess the risk of severe CAC in patients. METHODS: CACS of ESKD patients was assessed using an electrocardiogram-gated coronary computed tomography (CT) scan with the Agatston scoring method. A predictive nomogram model was established based on stepwise regression. An independent validation cohort comprised of patients with ESKD from multicentres. RESULTS: 369 ESKD patients were enrolled in the training set, and 127 patients were included in the validation set. In the training set, the patients were divided into three subgroups: no calcification (CACS = 0, n = 98), mild calcification (0 < CACS ≤ 400, n = 141) and severe calcification (CACS > 400, n = 130). Among the four coronary branches, the left anterior descending branch (LAD) accounted for the highest proportion of calcification. Stepwise regression analysis showed that age, dialysis vintage, ß-receptor blocker, calcium-phosphorus product (Ca × P), and alkaline phosphatase (ALP) level were independent risk factors for severe CAC. A nomogram that predicts the risk of severe CAC in ESKD patients has been internally and externally validated, demonstrating high sensitivity and specificity. CONCLUSION: CAC is both prevalent and severe in ESKD patients. In the four branches of the coronary arteries, LAD calcification is the most common. Our validated nomogram model, based on clinical risk factors, can help predict the risk of severe coronary calcification in ESKD patients who cannot undergo coronary CT analysis.

Association between Apo B, LDL-R and PCSK9 gene polymorphisms with coronary artery diseases in Egyptians.

BACKGROUND: Many studies have focused on the significance of lipid regulatory genes in the pathophysiology of Coronary artery disease (CAD). ApoB XbaI (rs693) and EcoRI (rs1042031) single nucleoid polymorphisms (SNPs) were investigated to detect whether they are risk factors for CAD. Till now, this association remains uncertain. SMARCA4 (rs1122608) SNP has directly related to dyslipidemia. Loss of function mutations (LOF) in PCSK9 result in a reduction in LDL cholesterol and are associated with protection from the development of CAD. METHODS: This study was conducted on 54 CAD patients who were admitted at Internal Medicine Specialized Hospital (Cardiology Department) and 47 healthy controls. Peripheral blood samples were taken from both groups. DNA was extracted from EDTA-blood samples, then PCR- RFLP for ApoB XbaI (rs693) and EcoRI (rs1042031), SMARCA4 (rs1122608) and PCSK9 (rs505151) SNPs was done. RESULTS: No statistically significant difference was found between patients and controls as regard EcoRI SNP. XbaI (rs693) X + X + genotype was significantly higher in control group (P = 0.0355). SMARCA4 (TT, GT + TT) genotypes, and T allele (P < 0.001); PCSK9 AG genotype and G allele (P = 0.027 and 0.032 respectively) were more frequent in CAD patients than controls. CONCLUSION: SMARCA4 (rs1122608) and PCSK9 (rs505151) SNPs are significantly accompanying with the risk of CAD development in the Egyptian population. X + X + genotype appeared to have a protective effect against CAD. However, no observed association between EcoRI (rs1042031) and the risk of CAD development was found.

Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.

Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a fundamentally important task in medicine. Classical detection approaches suffer from limited diagnostic accuracy or expose patients to possibly harmful radiation. Here we show how machine learning (ML) can outperform cardiologists in predicting the presence of stress-induced fCAD in terms of area under the receiver operating characteristic (AUROC: 0.71 vs. 0.64, p = 4.0E-13). We present two ML approaches, the first using eight static clinical variables, whereas the second leverages electrocardiogram signals from exercise stress testing. At a target post-test probability for fCAD of <15%, ML facilitates a potential reduction of imaging procedures by 15-17% compared to the cardiologist's judgement. Predictive performance is validated on an internal temporal data split as well as externally. We also show that combining clinical judgement with conventional ML and deep learning using logistic regression results in a mean AUROC of 0.74.

A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.

Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus.

BACKGROUND: Diabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients. METHODS: R language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC. RESULTS: The results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset. CONCLUSIONS: In this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.