Identification of diagnostic biomarkers and immune cell infiltration in coronary artery disease by machine learning, nomogram, and molecular docking.

Background: Coronary artery disease (CAD) is still a lethal disease worldwide. This study aims to identify clinically relevant diagnostic biomarker in CAD and explore the potential medications on CAD. Methods: GSE42148, GSE180081, and GSE12288 were downloaded as the training and validation cohorts to identify the candidate genes by constructing the weighted gene co-expression network analysis. Functional enrichment analysis was utilized to determine the functional roles of these genes. Machine learning algorithms determined the candidate biomarkers. Hub genes were then selected and validated by nomogram and the receiver operating curve. Using CIBERSORTx, the hub genes were further discovered in relation to immune cell infiltrability, and molecules associated with immune active families were analyzed by correlation analysis. Drug screening and molecular docking were used to determine medications that target the four genes. Results: There were 191 and 230 key genes respectively identified by the weighted gene co-expression network analysis in two modules. A total of 421 key genes found enriched pathways by functional enrichment analysis. Candidate immune-related genes were then screened and identified by the random forest model and the eXtreme Gradient Boosting algorithm. Finally, four hub genes, namely, CSF3R, EED, HSPA1B, and IL17RA, were obtained and used to establish the nomogram model. The receiver operating curve, the area under curve, and the calibration curve were all used to validate the accuracy and usefulness of the diagnostic model. Immune cell infiltrating was examined, and CAD patients were then divided into high- and low-expression groups for further gene set enrichment analysis. Through targeting the hub genes, we also found potential drugs for anti-CAD treatment by using the molecular docking method. Conclusions: CSF3R, EED, HSPA1B, and IL17RA are potential diagnostic biomarkers for CAD. CAD pathogenesis is greatly influenced by patterns of immune cell infiltration. Promising drugs offers new prospects for the development of CAD therapy.

Acute myocardial infarction due to giant coronary artery aneurysm and arteriovenous fistula: a challenging case report and review of the literature.

BACKGROUND: A coronary artery aneurysm (CAA) is an abnormal dilation of a coronary artery segment often accompanied by coronary artery fistula (CAF), leading to communication between a coronary artery and a cardiac chamber or a part of the coronary venous system. Both CAAs and CAFs can present with symptoms and signs of myocardial ischemia and infarction. CASE PRESENTATION: We describe the case of a 46-year-old woman with non-ST-elevation myocardial infarction (NSTEMI) caused by a "giant" CAA. Various imaging modalities revealed a thrombus-containing aneurysm located at the right-posterior cardiac border, with established arteriovenous communication with the distal part of left circumflex artery (LCx). After initial treatment with dual antiplatelet therapy, a relapse of pain was reported along with a new increase in troponin levels, electrocardiographic abnormalities, reduced left ventricular ejection fraction (LVEF) and thrombus enlargement. Surgical excision of the aneurysm was favored, revealing its true size of 6 cm in diameter. Τhe aneurysm was excised without complications. The patient remained asymptomatic during follow-up. CONCLUSIONS: Management of rare entities such as "giant" CAAs and CAFs can be challenging. Cases such as this can serve as precedents to facilitate treatment plans and develop consistent recommendations, emphasizing the importance of personalized strategies for future patients.

Health information-seeking behavior in patients with coronary artery disease: Activating methods.

INTRODUCTION: Coronary artery disease (CAD) has a high mortality rate worldwide, and continuous health behavior practice and careful management are required owing to risks such as rapid changes in symptoms and emergency hospitalization. The utilization of health-related information is an important factor for long-term disease management in patients with CAD. For this purpose, an understanding of health information-seeking behavior is needed first. METHODS: This study analyzed data from the 2021 Korea Medical Panel Survey, and logistic regression analysis was conducted to confirm the factors influencing the health information-seeking behavior of patients with CAD. RESULTS: The health information-seeking behavior of patients with CAD differed according to demographic characteristics, and differences in preferred information use were confirmed. Finally, it was identified that insufficient levels of health literacy were a major reason for CAD patients not engaging in health information-seeking behaviors (OR, 0.17; 95% CI, 0.09-0.33; p < 0.001). CONCLUSION: This study suggests that to improve health information-seeking behaviors, the application of education and intervention programs to increase the level of health literacy is necessary.

AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.

The association between C-reactive protein and coronary artery calcification: a systematic review and meta-analysis.

BACKGROUND: While coronary artery calcification (CAC) is recognized as a reliable marker for coronary atherosclerosis, the relationship between the concentration of C-reactive protein (CRP) and the incidence and progression of CAC remains controversial. METHOD: PubMed, Embase, Web of Science, and Scopus were systematically searched to identify relevant observational studies until October 2023. The methodological quality of the included studies was evaluated using the Newcastle-Ottawa Scale (NOS). A random-effects meta-analysis was employed to calculate pooled odd ratios (OR) and corresponding 95% confidence intervals, considering heterogeneity among the studies. RESULTS: Out of the 2545 records, 42 cross-sectional and 9 cohort studies were included in the systematic review. The meta-analysis on 12 eligible cross-sectional studies revealed no significant association between CAC and CRP [pooled OR: 1.03 (1.00, 1.06)]. Additionally, an insignificant association was found between CAC and CRP through meta-analysis on three eligible cohort studies [pooled OR: 1.05 (0.95, 1.15)] with no considerable heterogeneity across studies. Sensitivity analyses indicated that the meta-analysis models were robust. There was no evidence of publication bias. CONCLUSION: Based on the meta-analysis findings, elevated levels of CRP did not emerge as a valuable prognostic maker for CAC incidence and progression prediction.

Coronary Artery Bypass Grafting in a Patient With Multivessel Disease and Dextrocardia With Situs Inversus Totalis.

Dextrocardia with situs inversus totalis is a rare hereditary condition characterized by reversed orientation of the major thoracic and abdominal organs. Though dextrocardia itself is not believed to increase the risk of coronary artery disease, the workup and surgical management of patients with this condition may be technically challenging to heart team clinicians. This report describes the case management of a high-risk 56-year-old man with dextrocardia who presented with multivessel coronary artery disease.

Levels of adherence to treatment, illness perception and acceptance of illness in patients with coronary artery disease - descriptive and correlational study.

BACKGROUND: Understanding the disease and its acceptance significantly influence adherence to prescribed medications, a critical aspect in managing coronary artery disease (CAD). This study is designed to explore the multifaceted factors influencing medication adherence specifically in CAD patients. Of particular interest is investigating the interconnectedness between medication adherence, the perception of illness, and the level of acceptance of the illness itself among these individuals. METHODS: This cross-sectional study involved 280 confirmed CAD patients who were selected through a convenience sampling method adhering to predefined inclusion criteria. The study was conducted between March and September 2023. Three primary parameters-medication adherence, illness perception, and acceptance of illness-were evaluated using standardized tools: The Morisky Medication Adherence Scale-8, Illness Perception Questionnaire-Brief, and Acceptance of Illness Scale. Statistical analyses using SPSS (version 25) were used to analyze the data. RESULTS: Patients had moderate illness perception (51.82 ± 7.58) and low acceptance to illness (16.98 ± 4.75), and 61.8 of them adhered to their medication regimen. A positive relationship between acceptance of illness and medication adherence (r = 0.435, p-value < 0.01) was found. Level of education, type of drug and marital status had significantly impact on medication adherence, and gender, level of education, intention to stop drug and marital status were associated with acceptance of illness (p < 0.05). CONCLUSION: These results underscore the pivotal role of medication adherence in CAD management. Future interventions should target improving illness perception and acceptance of illness among CAD patients to enhance their overall adherence to prescribed medications and ultimately improve disease management.

Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease.

BACKGROUND: The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence. METHODS: Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features. RESULTS: HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively. CONCLUSIONS: In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

A unique cause of acute bypass graft failure: think outside the box.

A 62-year-old male presented with non-ST elevation myocardial infarction. Cardiac catheterization revealed multivessel coronary artery disease.

The role of exercise-based cardiac rehabilitation after percutaneous coronary intervention in patients with coronary artery disease: a meta-analysis of randomised controlled trials.

BACKGROUND: this study was designed to analyse patient outcomes using a combination of PCI and exercise-based cardiac rehabilitation compared with PCI alone. METHODS: PCI can improve the survival rate of patients with coronary artery disease, but it can also cause vascular endothelial cell injury, thrombosis, and even restenosis. Early cardiac rehabilitation exercise is crucial for patients with coronary heart disease after PCI. Five databases were examined for randomised controlled trials involving early cardiac rehabilitation exercise and standard treatment in patients with coronary heart disease after PCI. The search period lasted from the creation of the database (2006) until December 2022. The outcomes including angina, arrhythmia, coronary restenosis, left ventricular ejection fraction, left ventricular end diastolic diameter, 6-min walk distance, total cholesterol, heart rate, systolic blood pressure and diastolic blood pressure. RevMan 5.3 was used to analyse the data, and the Cochrane Collaboration was used to assess the quality of evidence. RESULTS: A total of 1231 patients were enrolled in this study. Angina pectoris (RR = 0.24, 95% CI [0.10, 0.57], p = 0.001), Arrhythmia (RR = 0.17, 95% CI [0.05, 0.55], p = 0.003), Coronary artery restenosis (RR = 0.10, 95% CI [0.01, 0.76], p = 0.03). CONCLUSION: Exercise after PCI improves LVEF, enhances 6MWD, lowers HR and minimises the risk of angina, arrhythmia and coronary artery restenosis in CHD patients. Exercise had no discernible effect on LVEDD, TC, SBP, or DBP.

The accuracy of detailed analysis of optical coherence tomography in detection of plaque lipid content: dual-imaging study with optical coherence tomography and near-infrared spectroscopy.

BACKGROUND: Lipid-rich plaque covered by a thin fibrous cap (FC) has been identified as a frequent morphological substrate for the development of acute coronary syndrome. Optical coherence tomography (OCT) permits the identification and measurement of the FC. Near-infrared spectroscopy (NIRS) has been approved for detection of coronary lipids. AIMS: We aimed to assess the ability of detailed OCT analysis to identify coronary lipids, using NIRS as the reference method. METHODS: In total, 40 patients with acute coronary syndrome underwent imaging of a non-culprit lesion by both NIRS and OCT. For each segment, the NIRS-derived 4 mm segment with maximal lipid core burden index (maxLCBI4mm) was assessed. OCT analysis was performed using a semi-automated method including measurement of the fibrous cap thickness (FCT) of all detected fibroatheromas. Subsequent quantitative volumetric evaluation furnished FCT, FC surface area (FC SA), lipid arc, and FC (fibrous cap) volume data. OCT features of lipid plaques were compared with maxLCBI4mm. Predictors of maxLCBI4mm >400 was assessed by using univariable and multivariable analysis. RESULTS: OCT features (mean FCT, total FC SA, FC volume, maximal, mean, and total lipid arcs) strongly correlated with the maxLCBI4mm (p = 0.012 for the mean FCT, respectively p < 0.001 for all other aforementioned features). The strongest predictors of maxLCBI4mm >400 were the maximal (p = 0.002) and mean (p = 0.002) lipid arc, and total FC SA (p = 0.012). CONCLUSIONS: We found a strong correlation between the OCT-derived features and NIRS findings. Detailed OCT analysis may be reliably used for detection of the presence of coronary lipids.

Diagnosing Coronary Artery Disease in the Patient Presenting with Stable Ischemic Heart Disease: The Role of Anatomic versus Functional Testing.

There are unique advantages and disadvantages to functional versus anatomic testing in the work-up of patients who present with symptoms suggestive of obstructive coronary artery disease. Evaluation of these individuals starts with an assessment of pre-test probability, which guides subsequent testing decisions. The choice between anatomic and functional testing depends on this pre-test probability. In general, anatomic testing has particular utility among younger individuals and women; while functional testing can be helpful to rule-in ischemia and guide revascularization decisions. Ultimately, selection of the most appropriate test should be individualized to the patient and clinical scenario.

The Pathophysiologic Basis of Managing Chronic Atherosclerotic Cardiovascular Disease.

Chronic coronary heart disease encompasses a broad spectrum of disorders that range in severity from trivial to imminently life-threatening. The primary care physician encounters coronary disease at all stages. The number of available diagnostic and therapeutic options for evaluating and treating coronary disease is vast, presenting a complex selection strategy challenge when making choices for the individual patient. The primary care physician is responsible to tailor evaluation and management strategies to each individual patient based on his/her particular disease characteristics. There are many categories of diagnostic studies and therapeutic interventions that have been shown at the population level in clinical trials to improve patient outcomes. Blindly applying the findings of all demonstrated studies and therapies to a patient with coronary disease would saddle him/her with an unsustainable burden of diagnostic tests and therapies. The core principle of the approach outlined in this article is to tailor diagnostic and therapeutic choices to the operative pathophysiology that drives a particular patient's disorder. This introductory article is intended to provide a conceptual framework for studying and applying the specialized topics discussed in the articles that follow.

[Correlation between serum growth differentiation factor 11 level and severity of coronary artery disease in patients with acute myocardial infarction].

Objective: To investigate the correlation between serum growth differentiation factor 11 (GDF11) level and coronary artery lesions in patients with ST-segment elevation myocardial infarction (STEMI), and the predictive efficacy of nomogram risk prediction model based on GDF11 combined with traditional risk factors on the occurrence of STEMI. Methods: This study was a retrospective cross-sectional study. Patients hospitalized in the Department of Cardiology of the 904th Hospital of Joint Logistic Support Force of People's Liberation Army of China from 2016 to 2018 were selected and divided into control group and STEMI group. The demographic data, blood lipid level, laboratory indicators of blood and GDF11 level were collected. Logistic regression analysis screened out independent correlated factors for the occurrence of STEMI. Spearman correlation analysis clarified the correlation of each indicator with the SYNTAX or Gensini scores. A nomogram risk prediction model for the risk of STEMI occurrence and the receiver operating characteristic curve was used to compare the prediction efficiency of each model. Results: A total of 367 patients were enrolled, divided into control group (n=172) and STEMI group (n=195), age (66.5±11.8), male 222 (60.49%). The serum GDF11 level of STEMI group was significantly lower than that of the control group (36.20 (16.60, 70.75) µg/L vs. 85.00 (53.93, 117.10) µg/L, P<0.001). The results of multivariate logistic regression analysis showed serum GDF11(OR=0.98, 95%CI: 0.97-0.99) and traditional independent risk factors such as smoking, diabetes, C-reactive protein, homocysteine, lipoprotein (a) and apolipoprotein A1/B were independent correlate factors for the occurrence of STEMI (P<0.05). Spearman correlation analysis showed that serum GDF11 was negatively correlated with SYNTAX score and Gensini score (P<0.05). The nomogram model constructed by serum GDF11 combined with traditional independent risk factors (AUC=0.85, 95%CI: 0.81-0.89) had better predictive value for the occurrence of STEMI than the traditional nomogram model constructed by independent risk factors(AUC=0.80, 95%CI:0.75-0.84) or serum GDF11 (AUC=0.76, 95%CI: 0.72-0.81), all P<0.01. Conclusions: Serum GDF11 is an independent correlate factor in the occurrence of STEMI and is negatively correlated with the severity of coronary artery lesions in patients with STEMI. The nomogram model constructed based on GDF11 combined with traditional risk factors can be a good predictor for the occurrence of STEMI.

Predicting the response to acetylcholine in ischemia or infarction with non-obstructive coronary arteries: The ABCD score.

BACKGROUND AND AIMS: Acetylcholine (ACh) provocation testing can detect vasomotor disorders in patients with ischemia and non-obstructed coronary arteries (INOCA) or myocardial infarction and non-obstructed coronary arteries (MINOCA). We aimed to derive and validate a simple risk score to predict a positive ACh test response. METHODS: We prospectively enrolled consecutive INOCA and MINOCA patients undergoing ACh provocation testing. Patients were split in two cohorts (derivation and validation) according to time of enrolment. The score was derived in 386 patients (derivation cohort) and then validated in 165 patients (validation cohort). RESULTS: 551 patients were enrolled, 371 (67.3%) INOCA and 180 (32.7%) MINOCA. ACh test was positive in 288 (52.3%) patients. MINOCA, myocardial bridge (MB), C-reactive protein (CRP) and dyslipidaemia were independent predictors of a positive ACh test in the derivation cohort. The ABCD (Acute presentation, Bridge, CRP, Dyslipidaemia) score was derived: 2 points were assigned to MINOCA, 3 to MB, 1 to elevated CRP and 1 to dyslipidaemia. The ABCD score accurately identified patients with a positive ACh test response with an AUC of 0.703 (CI 95% 0.652-0.754,p < 0.001) in the derivation cohort, and 0.705 (CI 95% 0.626-0.784, p < 0.001) in the validation cohort. In the whole population, an ABCD score ≥4 portended 94.3% risk of a positive ACh test and all patients with an ABCD score ≥6 presented a positive test. CONCLUSIONS: The ABCD score could avoid the need of ACh provocation testing in patients with a high score, reducing procedural risks, time, and costs, and allowing the implementation of a tailored treatment strategy. These results are hypothesis generating and further research involving larger cohorts and multicentre trials is needed to validate and refine the ABCD score.

Machine learning insights into thrombo-ischemic risks and bleeding events through platelet lysophospholipids and acylcarnitine species.

Coronary artery disease (CAD) often leads to adverse events resulting in significant disease burdens. Underlying risk factors often remain inapparent prior to disease incidence and the cardiovascular (CV) risk is not exclusively explained by traditional risk factors. Platelets inherently promote atheroprogression and enhanced platelet functions and distinct platelet lipid species are associated with disease severity in patients with CAD. Lipidomics data were acquired using mass spectrometry and processed alongside clinical data applying machine learning to model estimates of an increased CV risk in a consecutive CAD cohort (n = 595). By training machine learning models on CV risk measurements, stratification of CAD patients resulted in a phenotyping of risk groups. We found that distinct platelet lipids are associated with an increased CV or bleeding risk and independently predict adverse events. Notably, the addition of platelet lipids to conventional risk factors resulted in an increased diagnostic accuracy of patients with adverse CV events. Thus, patients with aberrant platelet lipid signatures and platelet functions are at elevated risk to develop adverse CV events. Machine learning combining platelet lipidome data and common clinical parameters demonstrated an increased diagnostic value in patients with CAD and might improve early risk discrimination and classification for CV events.

The combination of high-frequency QRS and ST-segment alterations during exercise stress tests enhanced the diagnostic efficacy for coronary artery disease.

BACKGROUND: High-frequency QRS (HF-QRS) manifests as a novel adjunct electrocardiographic marker with potential utility in coronary artery disease (CAD) detection. HYPOTHESIS: We hypothesize that HF-QRS analysis may be superior to conventional ST-segment analysis in detecting CAD, and the combination of these two analyses in the exercise stress test may enhance the diagnostic efficacy for CAD. METHODS: The study incorporated a sample of 157 patients (mean age 62 ± $\pm $ 9 years) referred for nonemergent angiography. Before angiography, patients underwent exercise stress testing utilizing an upright bicycle. High-resolution electrocardiogram (ECG) data were collected during the exercise test, facilitating both HF-QRS and conventional ST-segment analyses. The diagnostic efficacy of HF-QRS and ST-segment analysis were compared, utilizing angiographic outcomes as the gold standard. The study design integrated HF-QRS analysis and ST-segment analysis via sequential and concurrent testing protocols. RESULTS: In terms of CAD detection, HF-QRS analysis displayed superior sensitivity compared to conventional ST-segment analysis (63% vs. 37%, p = .002). The serial test significantly increased specificity from 79% to 97% (p = .002) compared to ST-deviation analysis alone. It showed a markedly low sensitivity of 26%. The parallel test significantly increased sensitivity from 37% to 77% (p < .001), while retaining a moderate level of specificity of 51%. The quantity of ECG leads exhibiting a positive HF-QRS response demonstrated a correlation with the severity of CAD (p < .001). CONCLUSIONS: HF-QRS analysis exhibited superior sensitivity in detecting angiographically confirmed CAD relative to conventional ST-segment analysis. Moreover, the combination of HF-QRS and ST-segment alterations during exercise stress test enhanced the diagnostic efficacy for CAD.