Prior Diagnosis Of Coronary Artery Disease Research Articles

Predicting asymptomatic coronary artery disease in first-ever acute ischemic stroke patients: a cross-sectional study.

It is frequently observed that patients with first-ever acute ischemic stroke (AIS) have a common occurrence of asymptomatic coronary artery disease (CAD). This condition is associated with a poor prognosis. Early detection and recognition of asymptomatic CAD in first-ever AIS patients may optimize the clinical management and ultimately lead to improved outcomes. The aim of this study was to investigate the role of aortic arch plaque (AAP) detected through combined computed tomography angiography (CTA) as an early predictor of asymptomatic CAD in patients with first-ever AIS without prior diagnosis of CAD. A cross-sectional study was conducted at Xuanwu Hospital, Capital Medical University from January 2019 to December 2021, involving patients with first-ever AIS caused by large arterial atherosclerosis. Patients with a history of recognized cardiovascular disease, nonatherosclerotic arterial stenosis, atrial fibrillation related to cardioembolism, and complete carotid occlusions were excluded. The study utilized a combined coronary and cervicocephalic CTA to evaluate atherosclerosis in the cervicocephalic, aortic, and coronary arteries simultaneously. First-ever AIS patients without prior diagnosis of CAD were divided into 2 groups: 1 with asymptomatic CAD detected through CTA and the other without. Multivariate logistic regression was used to identify independent risk factors associated with the presence of asymptomatic CAD, including aortic arch, cervical and intracranial atherosclerotic characteristics (e.g., vascular stenosis, plaque thickness, extent, and complexity). Among 182 AIS patients, 84 (46.2%) had asymptomatic CAD. Increased aortic arch plaque (AAP) thickness per millimeter [adjusted odds ratio (aOR): 1.26; 95% confidence interval (CI): 1.08-1.47], presence of severe AAP (aOR: 4.24; 95% CI: 1.59-12.03), mixed AAP (aOR: 2.65; 95% CI: 1.09-6.62), and ulcerative AAP (aOR: 11.76; 95% CI: 2.05-222.84) raised the risk of asymptomatic CAD in stroke patients, independent of other factors. The combination of ulcerative AAP, diabetes mellitus, and smoking could predict asymptomatic CAD with an area under the receiver operating characteristic curve (AUC) of 0.71 (95% CI: 0.64-0.79; P<0.001). Ulcerative AAP was found to be more valuable than cervicocephalic atherosclerotic characteristics for predicting asymptomatic CAD in first-ever AIS patients. The presence of ulcerative AAP was associated with asymptomatic CAD in first-ever AIS patients. As an early systemic atherosclerosis feature, ulcerative AAP is probably a more valuable indicator than cervicocephalic atherosclerotic characteristics for predicting asymptomatic CAD in AIS patients.

Characteristics of sudden death by clinical criteria.

Sudden death is a leading cause of deaths nationally. Definitions of sudden death vary greatly, resulting in imprecise estimates of its frequency and incomplete knowledge of its risk factors. The degree to which time-based and coronary artery disease (CAD) criteria impacts estimates of sudden death frequency and risk factors is unknown. Here, we apply these criteria to a registry of all-cause sudden death to assess its impact on sudden death frequency and risk factors. The sudden unexpected death in North Carolina (SUDDEN) project is a registry of out of-hospital, adjudicated, sudden unexpected deaths attended by Emergency Medical Services. Deaths were not excluded by time since last seen or alive or by prior symptoms or diagnosis of CAD. Common criteria for sudden death based on time since last seen alive (both 24 hours and 1 hour) and prior diagnosis of CAD were applied to the SUDDEN case registry. The proportion of cases satisfying each of the 4 criteria was calculated. Characteristics of victims within each restrictive set of criteria were measured and compared to the SUDDEN registry. There were 296 qualifying sudden deaths. Application of 24 hour and 1 hour timing criteria compared to no timing criteria reduced cases by 25.0% and 69.6%, respectively. Addition of CAD criteria to each timing criterion further reduced qualifying cases, for a total reduction of 81.8% and 90.5%, respectively. However, characteristics among victims meeting restrictive criteria remained similar to the unrestricted population. Timing and CAD criteria dramatically reduces estimates of the number of sudden deaths without significantly impacting victim characteristics.

Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques.

BackgroundStress echocardiography is a well-established diagnostic tool for suspected coronary artery disease (CAD). Cardiovascular risk factors are used in the assessment of the probability of CAD. The link between the outcome of stress echocardiography and patients’ variables including risk factors, current medication, and anthropometric variables has not been widely investigated.ObjectiveThis study aimed to use machine learning to predict significant CAD defined by positive stress echocardiography results in patients with chest pain based on anthropometrics, cardiovascular risk factors, and medication as variables. This could allow clinical prioritization of patients with likely prediction of CAD, thus saving clinician time and improving outcomes.MethodsA machine learning framework was proposed to automate the prediction of stress echocardiography results. The framework consisted of four stages: feature extraction, preprocessing, feature selection, and classification stage. A mutual information–based feature selection method was used to investigate the amount of information that each feature carried to define the positive outcome of stress echocardiography. Two classification algorithms, support vector machine (SVM) and random forest classifiers, have been deployed. Data from 529 patients were used to train and validate the framework. Patient mean age was 61 (SD 12) years. The data consists of anthropological data and cardiovascular risk factors such as gender, age, weight, family history, diabetes, smoking history, hypertension, hypercholesterolemia, prior diagnosis of CAD, and prescribed medications at the time of the test. There were 82 positive (abnormal) and 447 negative (normal) stress echocardiography results. The framework was evaluated using the whole dataset including cases with prior diagnosis of CAD. Five-fold cross-validation was used to validate the performance of the framework. We also investigated the model in the subset of patients with no prior CAD.ResultsThe feature selection methods showed that prior diagnosis of CAD, sex, and prescribed medications such as angiotensin-converting enzyme inhibitor/angiotensin receptor blocker were the features that shared the most information about the outcome of stress echocardiography. SVM classifiers showed the best trade-off between sensitivity and specificity and was achieved with three features. Using only these three features, we achieved an accuracy of 67.63% with sensitivity and specificity 72.87% and 66.67% respectively. However, for patients with no prior diagnosis of CAD, only two features (sex and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker use) were needed to achieve accuracy of 70.32% with sensitivity and specificity at 70.24%.ConclusionsThis study shows that machine learning can predict the outcome of stress echocardiography based on only a few features: patient prior cardiac history, gender, and prescribed medication. Further research recruiting higher number of patients who underwent stress echocardiography could further improve the performance of the proposed algorithm with the potential of facilitating patient selection for early treatment/intervention avoiding unnecessary downstream testing.

Point of Care Clinical Risk Score to Improve the Negative Diagnostic Utility of an Agatston Score of Zero: Averting the Need for Coronary Computed Tomography Angiography.

Coronary artery calcification is a marker of underlying atherosclerotic vascular disease. The absence of coronary artery calcification is associated with a low prevalence of obstructive coronary artery disease (CAD), but it cannot be ruled out completely. We sought to develop a clinical tool that can be added to Agatston score of zero to rule out obstructive CAD with high accuracy. We developed a clinical score retrospectively from a cohort of 4903 consecutive patients with an Agatston score of zero. Patients with prior diagnosis of CAD, coronary percutaneous coronary intervention, or surgical revascularization were excluded. Obstructive CAD was defined as any epicardial vessel diameter narrowing of ≥50%. The score was validated using an external cohort of 4290 patients with an Agatston score of zero from a multinational registry. The score consisted of 7 variables: age, sex, typical chest pain, dyslipidemia, hypertension, family history, and diabetes mellitus. The model was robust with an area under the curve of 0.70 (95% CI, 0.65-0.76) in the derivation cohort and 0.69 (95% CI, 0.65-0.72) in the validation cohort. Patients were divided into 3 risk groups based on the score: low (≤6), intermediate (7-13), and high (≥14). Patients who score ≤6 have a negative likelihood ratio of 0.42 for obstructive CAD, whereas those who score ≥14 have a positive likelihood ratio of >5.5 for obstructive CAD. The outcome was ruled out in >98% of patients with a score ≤6 in the validation cohort. We developed a score that may be used to identify the likelihood of obstructive CAD in patients with an Agatston score of zero, which may be used to direct the need for additional testing. However, the results of this retrospective analysis are hypothesis generating and before clinical implementation should be validated in a trial with a prospectively collected data.

Abstract 286: Can the Functional Capacity Measured by Duke Activity Status Index Increase the Value of Imaging during Stress Testing?

Background: The incidence of abnormal myocardial perfusion imaging (MPI) has been reported to be very low in patients exercising &gt;10 metabolic equivalents (METs) without ST depression on EKG. We sought to determine if the Duke Activity Status Index (DASI), which predicts functional capacity(FC), can determine who benefits the most from exercise stress testing (EST) with MPI. We hypothesized that DASI-predicted FC of &lt;10 METs will identify patients with a higher likelihood of abnormal MPI. Methods: DASI questionnaire was prospectively administered to 163 patients without prior history of coronary artery disease (CAD) referred for EST with MPI. DASI was used to predict METs by dividing the total score by 3.5. The study population was divided based on DASI score&gt;35 (corresponding to METs&gt;10) to determine the relationship with abnormal MPI. Abnormal MPI was defined as a summed stress score (SSS) &gt;0 or left ventricular ejection fraction (LVEF) &lt;45%. These cutoffs were used to identify any abnormalities that may occur in this population without prior diagnosis of CAD. Results: Among the 163 patients, 52% were males, mean age was 53±10 years, 14% were diabetic, 93% had atypical symptoms and 74% had normal baseline EKG's. Mean DASI-predicted METS was 13.3±3.5 and mean measured METS was 10.4±3.0. There was a strong correlation between DASI-METS and measured METS (Spearman's rho=0.47; p&lt;0.001). DASI was &gt;35 in 128 (79%) and ≤35 in 35 (21%) patients, respectively. Abnormal MPI was noted in 8 patients (7 abnormal SSS, 1 abnormal EF). SSS was&gt;0 in 3 out of 128 (2%) in the DASI &gt;35 group compared to 4 of 35 (11%) in the DASI≤35 group (p&lt;0.05). LVEF&lt;45% was noted as an isolated abnormality in 1 patient with DASI&gt;35. All patients with DASI&gt;35 had a low-risk scan. High-risk scan (SSS&gt;8 and LVEF&lt;45%) was noted in 3 patients with DASI≤35. Conclusions: In patients without prior CAD referred for EST with MPI, DASI can help increase the value of MPI by identifying a subset that will benefit more from MPI. Patients with a DASI≤35 have a significantly higher likelihood of having both abnormal MPI and severely abnormal MPI. Functional capacity should be considered in addition to pretest probability of CAD and baseline EKG in evaluating the appropriateness of stress testing with imaging. Studies with larger sample sizes are warranted to validate this approach.

Association of Postprandial Triglyceride and Retinyl Palmitate Responses With Newly Diagnosed Exercise-Induced Myocardial Ischemia in Middle-Aged Men and Women

Although strong evidence exists linking fasting plasma levels of LDL cholesterol (LDL-C) and HDL cholesterol (HDL-C) to risk for development of coronary artery disease (CAD), the data in support of an independent role for fasting triglyceride (TG) concentrations are weak. Humans are in the postprandial state most of the day, however, and results from both basic and clinical studies suggest that postprandial TG levels may be atherogenic. Previous studies have not, however, attempted to determine if postprandial TG levels are associated with CAD independent of other traditional risk factors or plasma lipid levels, particularly fasting plasma concentrations of TG and HDL-C. Ninety-two men and 113 women (mean age, 51.6 and 53.6 years, respectively) were recruited from populations undergoing diagnostic exercise electrocardiographic or thallium stress tests at our medical centers. Twenty-six men and 24 women had positive tests. We chose exercise-induced myocardial ischemia (EIM) as the criterion for defining case and control subjects because we wanted participants who did not have a prior diagnosis of CAD. Blood samples were obtained for measurement of plasma TG, TG-rich lipoprotein TG, and retinyl palmitate (RP) levels 2, 3.5, 5, and 8 hours after the subjects had consumed a fatty test meal. Logistic regression models were developed to test for associations between each variable and case-control status. Among men but not women postprandial TG and RP responses were associated with EIMI independent of age, race, and smoking status. In the male group, the odds ratio (OR) for an increase in postprandial TG response of approximately 1 SD was 1.69 (P = .007); the OR for an increase in RP response of 1 SD was 2.47 (P = .011). However, when fasting TG was added to the model, the OR for postprandial TG area in the men was reduced to 1.44 (P = .17); the OR postprandial RP area in the men was reduced to 1.88 (P = .12). There was no effect of adding other risk factors, including LDL-C and HDL-C, to the model. Significant effect modification by body mass index (BMI) on the relationship between postprandial responses and case-control status was observed. In men with BMI < 30, the OR was 1.83 for postprandial TG (P = .041) and 2.77 for postprandial RP (P = .032) in models that included fasting TG, LDL-C, and hypertension.