Search Results (95)

Myocardial infarction (MI) is a critical cardiovascular disease characterized by extensive myocardial necrosis occurring within a short timeframe. Traditional MI detection and localization techniques predominantly utilize single-domain features as input. However, relying solely on single-domain features of the electrocardiogram (ECG) proves challenging for accurate MI detection and localization due to the inability of these features to fully capture the complexity and variability in cardiac electrical activity. To address this, we propose a multi-domain feature fusion convolutional neural network (MFF–CNN) that integrates the time domain, frequency domain, and time-frequency domain features of ECG for automatic MI detection and localization. Initially, we generate 2D frequency domain and time-frequency domain images to combine with single-dimensional time domain features, forming multi-domain input features to overcome the limitations inherent in single-domain approaches. Subsequently, we introduce a novel MFF–CNN comprising a 1D CNN and two 2D CNNs for multi-domain feature learning and MI detection and localization. The experimental results demonstrate that in rigorous inter-patient validation, our method achieves 99.98% detection accuracy and 84.86% localization accuracy. This represents a 3.43% absolute improvement in detection and a 16.97% enhancement in localization over state-of-the-art methods. We believe that our approach will greatly benefit future research on cardiovascular disease. Full article

Background/Objectives: Polymer-free drug-eluting stents (PF-DESs) aim to mitigate long-term adverse effects associated with polymer-based platforms. However, clinical comparisons with biodegradable polymer DESs (BP-DESs) remain limited. The objective of this review is to assess the efficacy and safety of PF-DESs versus thin-struts (<100 μm) BP-DESs in patients undergoing percutaneous coronary intervention (PCI). Methods: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing PF-DESs and BP-DESs in adults undergoing PCI. PubMed, Embase, and CENTRAL were searched up to 1 February 2025. A random-effects model was used to calculate pooled risk ratios (RR) or mean differences (MD) with 95% confidence intervals (CI). Outcomes included myocardial infarction (MI), all-cause and cardiac death, target lesion revascularization (TLR), stent thrombosis, and angiographic/OCT parameters. Subgroup and sensitivity analyses were conducted for outcomes with high heterogeneity (I² > 50%). Results: Nine RCTs (n = 9597) were included. At 12 months, no significant differences were found between PF-DESs and BP-DESs for TLR (RR 1.51; 95% CI: 0.83–2.75), MI, or stent thrombosis. At 24 months, MI and all-cause death were similar between groups. A subgroup analysis showed lower cardiac death with the BioFreedom stent (RR 0.57; 95% CI: 0.35–0.90), not observed in non-BioFreedom devices. No significant differences were detected in angiographic or OCT outcomes, though heterogeneity was high. Conclusions: PF-DESs and BP-DESs demonstrated comparable clinical performance. The observed benefit in cardiac death with BioFreedom may reflect device-specific effects and merits further investigation. Full article

Background: Estrogens play a protective role during the early stages of life. However, endogenous 17β-estradiol (E2) can accelerate atherosclerosis progression. Aim: The purpose of this study was to test for the significance of the sex-specific associations of gonadal hormones with the extent of the inflammatory response, myocardial damage, and ventricular arrhythmia risk in acute myocardial infarction (MI). Materials and Methods: Study design: single-center cohort study. Blood samples for the assessment of sex steroids (E2, total testosterone [T]), oxidized low-density lipoproteins, high-sensitivity C-reactive protein (CRP), white blood cell (WBC) counts, and cardiac enzymes were collected 48 h after the onset of symptoms (and within 6 h after PCI) from 111 patients (37% women) with acute MI. Coronary disease severity, left ventricular systolic function (LV), and indices of ventricular repolarization were assessed using coronary angiography, echocardiography, and a conventional electrocardiogram, respectively. Results: In men with acute MI, peak cardiac enzyme levels were predicted by post-percutaneous coronary intervention (PCI) E2 plasma levels, peak WBC count, and peak CRP plasma levels. T levels and the E2/T ratio were associated with post-PCI CRP in these men. For women, peak WBC count was a marker of highest testosterone, and only WBC count was a significant indicator of myocardial injury extent. The incidence of acute ventricular tachycardia detected in AMI was significantly associated with left ventricular ejection fraction and with peak WBC count (as a tendency) regardless of sex. A longer duration of cardiac repolarization prior to PCI was predicted by lower ejection fractions in men and by age, CRP, and testosterone levels in female patients. Conclusions: During acute MI, elevated endogenous estradiol levels in men and increased leukocytes in women indicate acute myocardial damage. Post-PCI plasma inflammatory markers are sex-specific confounding factors for acute endogenous E2 levels, T levels, and the E2/T ratio. LV systolic function in men and, characteristically, the acute inflammatory response and testosterone levels in women are predictors of longer ventricular repolarization and arrhythmia risk. Full article

Background: Some authors advocate that ECGs with conventional computer algorithm (CCA) interpretations of “normal” need not be immediately reviewed. However, such ECGs may actually manifest findings of acute coronary occlusion myocardial infarction (OMI). We sought to determine if such cases can be detected by artificial intelligence (AI). Methods: We studied a retrospective series (2014–2024) of cases with ≥1 pre-angiography ECGs with a proven OMI outcome with a CCA ECG interpretation of “normal”. The OMI outcome was defined as (1) the diagnosis of acute type I MI, (2) an angiographic culprit with intervention, and (3) one of the following, (a) TIMI-0-2 flow, or (b) TIMI-3 or unknown flow, with high peak troponin or new wall abnormality. Each ECG as retrospectively interpreted by the PMcardio OMI AI ECG model. The primary analysis was the performance of AI in diagnosing "OMI" among these CCA “normal” ECGs. Results: Forty-two patients with OMI met the inclusion criteria. The first ECG was interpreted as “normal” by the CCA in 88% of cases; AI interpreted 81% as OMI and 86% as abnormal. Of the 78 total ECGs interpreted by the CCA, 73% were diagnosed as “normal”. Of this 73%, AI identified 81% as abnormal and 72% as OMI. Conclusion: The Conventional Computer Algorithm may interpret an ECG manifesting OMI as “normal”. AI not only recognized these as abnormal, but in 81% of patients, correctly recognized OMI on the first ECG and recognized 72% of all the CCA “normal” ECGs as OMI. It was rare for AI to diagnose a normal ECG for any OMI patient. Full article

Familial hypercholesterolemia (FH) is relatively prevalent in myocardial infarction (MI) sufferers, and its diagnosis could improve preventive treatment in family members. We aim to analyze the diagnosis of FH and the rate of genetic testing in a prospective cohort of 245 patients submitted to our Cardiac Rehabilitation Program (CRP) after MI. Baseline characteristics were registered, and basal low-density lipoprotein cholesterol (LDL-C) was calculated after correction for lipid-lowering therapies (LLT) before or during admission. Simplified Dutch Lipid Clinic Network Scores (sDLCNS) were retrospectively calculated based on personal and familial history of premature cardiovascular disease and basal LDL-C levels. Mean age was 62.19 ± 13.93 years, and most patients were male (81.6%). Mean LDL-C before admission and basal LDL-C corrected for LLT were 131.79 ± 45.34 mg/dL and 162.87 ± 44.17 mg/dL, respectively. Patients in the cohort were retrospectively categorized in the “unlikely” (<3 points; n = 162, 66.1%), “possible” (3–5 points; n = 72, 29.4%) and “probable” (6–8 points; n = 11, 4.5%) sDLCNS categories. Genetic testing for FH was requested in four (1.6%) patients, and no clinically significant genetic variants were detected. Patients who underwent genetic testing depicted significantly higher basal LDL-C (233 ± 49.09 vs. 161.71 ± 43.25 mg/dL, p = 0.001). However, the rate of individuals undergoing genetic testing was negligible even in the “possible” (n = 2, 2.8%) and “probable” (n = 1, 9.1%) sDLCNS categories. In conclusion, genetic testing for FH in our CRP after MI is largely underutilized, even in patients with a “possible” or “probable” diagnosis based on sDLCNS criteria, which represent about a third of the cohort. Strategies to improve screening for FH should be prospectively implemented. Full article

Background: The aim of this study was to assess the effect of right ventricular diastolic dysfunction on the results of 5-year follow-up of patients after coronary artery bypass grafting (CABG). Methods: Patients were enrolled in this prospective observational study examined before planned CABG from 2017 to 2018. In addition to the baseline preoperative indicators and perioperative data, the initial parameters of the left and right ventricle (RV) systolic and diastolic function were assessed. The long-term results after CABG were assessed after 5 years. The following endpoints were recorded in the remote period: coronary and non-coronary death, non-fatal myocardial infarction (MI), repeat myocardial revascularization. Results: The results of long-term follow-up were assessed in 148 patients, during which time MACE was registered in 43 patients (29.1%). In the group with MACE before CABG, a history of myocardial infarction (p = 0.008), functional class 3 NYHA of chronic heart failure (CHF) (p = 0.013), an increase in the left ventricle size, a decrease in the e′/a′ ratio (p = 0.041), and the presence of the right ventricle diastolic dysfunction (p = 0.037) were more often detected. Kaplan–Meier analysis revealed a better long-term prognosis (MACE-free survival) in the group without RVDD compared to the group with RVDD (p = 0.026). Conclusions: In patients after coronary artery bypass grafting, the development of adverse events was associated with both clinical factors and the presence of right ventricular diastolic dysfunction. Survival analysis revealed a worse prognosis in patients with preoperative RVDD compared with patients without RVDD. Full article

Background and Objectives: Atherosclerosis is an inflammatory condition that results in cholesterol accumulating within vessel wall cells. Atherosclerotic cardiovascular disease is the leading cause of mortality worldwide due to this disease being a major contributor to myocardial infarctions and cerebrovascular accidents. Research suggests that cholesterol accumulation occurring precisely within arterial endothelial cells triggers atherogenesis and exacerbates atherosclerosis. Furthermore, inflamed endothelium acts as a catalyst for atherosclerotic development. Therefore, enhancing cholesterol removal specifically in pro-inflammatory endothelial cells may be a potential treatment option for atherosclerosis. While we have previously shown that inhibiting the microRNA guide strand miR-33a-5p within pro-inflammatory endothelial cells increases both ABCA1 expression and apoAI-mediated cholesterol efflux, it is unknown whether inhibiting the miR-33a-3p passenger strand in pro-inflammatory endothelial cells causes similar atheroprotective effects. In this study, this is what we aimed to test. Materials and Methods: We used plasmid transfection to knockdown miR-33a-3p expression within cultured pro-inflammatory immortalized mouse aortic endothelial cells (iMAECs). We compared ABCA1 expression and apoAI-mediated cholesterol efflux within these cells to cultured pro-inflammatory iMAECs transfected with a control plasmid. Results: The knockdown of miR-33a-3p expression within pro-inflammatory iMAECs resulted in a significant increase in ABCA1 mRNA expression. However, the inhibition of miR-33a-3p did not significantly increase ABCA1 protein expression within pro-inflammatory iMAECs. Moreover, we failed to detect a significant increase in apoAI-mediated cholesterol efflux within pro-inflammatory iMAECs from miR-33a-3p knockdown. Conclusions: Our results indicative that the knockdown of miR-33a-3p alone does not enhance ABCA1-dependent cholesterol efflux within pro-inflammatory endothelial cells. To gain any atheroprotective benefit from inhibiting miR-33a-3p within pro-inflammatory endothelium, additional anti-atherogenic strategies would likely be needed in unison. Full article

EFNA1 (ephrinA1), a highly expressed tyrosine kinase receptor-ligand in healthy cardiomyocytes, is reduced following myocardial infarction (MI). A single intramyocardial injection of chimeric EFNA1-Fc at the time of ischemia mitigates the injury in both reperfused and non-reperfused mouse myocardium by reducing apoptosis, necrosis, and inflammation. Recently, we have successfully imaged and qualitatively identified endogenous EFNA1 pre- and post-MI using matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) coupled with a time-of-flight mass spectrometer (MALDI/TOF MS). Building on our previous work, we are currently focused on understanding and characterizing EFNA1’s role in cardiac tissue by developing an integrated quantitative method to determine endogenous levels of EFNA1 using MALDI-MSI technologies. Herein, we have optimized a method for the relative quantitation of endogenous tryptic EFNA1 peptides detected in the murine heart as compared with routine western blotting. In healthy myocardium, there was approximately 50 ng of endogenous EFNA1 per section of 9.43 mm³ tissue, or roughly 12 pg/µg of homogenized tissue. MALDI-MSI thus provides a tool for determining the anatomical distribution and relative quantitation of endogenous EFNA1 in cardiac tissue. Future applications of these tools will allow us to investigate the dynamic changes in EFNA1 expression profile that occur in pathological states such as myocardial infarction and upon therapeutic treatments. Full article

Background/Objectives: Heart attacks are the leading cause of death in the world. There are two important classes of heart attack: ST-segment Elevation Myocardial Infarction (STEMI) and Non-ST-segment Elevation Myocardial Infarction (NSTEMI) patient groups. While the STEMI group has a higher mortality rate in the short term, the NSTEMI group is considered more dangerous and insidious in the long term. Blocked coronary arteries can be predicted from ECG signals in STEMI patients but not in NSTEMI patients. Therefore, coronary angiography (CAG) is inevitable for these patients. However, in the elderly and some patients with chronic diseases, if there is a single blockage, the CAG procedure poses a risk, so medication may be preferred. In this study, a novel deep learning-based approach is used to automatically detect the occluded main coronary artery or arteries in NSTEMI patients. For this purpose, a new seven-class dataset was created with expert cardiologists. Methods: A new Multi Input-Multi Scale (MI-MS) ConvMixer model was developed for automatic detection. The MI-MS ConvMixer model allows simultaneous training of 12-channel ECG data and highlights different regions of the data at different scales. In addition, the ConMixer structure provides high classification performance without increasing the complexity of the model. Moreover, to maximise the classifier performance, the WSSE algorithm was developed to adjust the classification prediction value according to the feature importance weights. Results: This algorithm improves the SVM classifier performance. The features extracted from this model were classified with the WSSE algorithm, and an accuracy of 88.72% was achieved. Conclusions: This study demonstrates the potential of the MI-MS ConvMixer model in advancing ECG signal classification for diagnosing coronary artery diseases, offering a promising tool for real-time, automated analysis in clinical settings. The findings highlight the model’s ability to achieve high sensitivity, specificity, and precision, which could significantly improve. Full article

The intention of this study was to profile the cohort from the Greek Registry for the prevalence of Familial Hypercholesterolemia (GRegistry-FH) by estimating the prevalence of coronary artery disease (CAD), myocardial infarction (MI), stroke, dyslipidemia, arterial hypertension, diabetes mellitus (DM), pre-DM, smoking, abnormal thyroid function (ATF), and lipid values. The GRegistry-FH is a prospective study involving door-to-door interviews conducted by trained interviewers. Overall, 7704 individuals aged ≥18 years, randomly selected from all the regions of Greece, participated. The prevalence of atherosclerotic cardiovascular disease (ASCVD) was 13.9% (CAD 6%, MI 3.2%, stroke 4.7%). Treated hypercholesterolemia was present in 20.1%, arterial hypertension in 24%, and DM in 11.3% individuals (25.5% had pre-DM). The prevalence of smoking was 37.9% (29% current) and the prevalence of ATF was 13.1% (hypothyroidism 11.3%). A family history of ASCVD was reported by 60.5% (CAD 32.2%, stroke 28.3%). The mean (SD) lipid values in mg/dL were as follows: total cholesterol of 201.8 (41.5), low-density lipoprotein cholesterol of 126.3 (30.1), high-density lipoprotein cholesterol of 51.9 (12.5), and triglycerides of 135.9 (64.7). The GRegistry-FH highlights the significant prevalence of ASCVD and its risk factors among Greek adults, indicating a pressing need for early detection and management strategies to mitigate ASCVD burden. This nationwide registry serves as a crucial tool for guiding public health policies and personalized preventive measures (NCT03140605). Full article

Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and explainable results using ECG signals. To this end, a symbolic language, named Cardioish, has been introduced. Methods: In this research, two publicly available datasets were used: (i) a mental disorder classification dataset and (ii) a myocardial infarction (MI) dataset. These datasets contain ECG beats and include 4 and 11 classes, respectively. To obtain explainable results from these ECG signal datasets, a new explainable feature engineering (XFE) model has been proposed. The Cardioish-based XFE model consists of four main phases: (i) lead transformation and transition table feature extraction, (ii) iterative neighborhood component analysis (INCA) for feature selection, (iii) classification, and (iv) explainable results generation using the recommended Cardioish. In the feature extraction phase, the lead transformer converts ECG signals into lead indexes. To extract features from the transformed signals, a transition table-based feature extractor is applied, resulting in 144 features (12 × 12) from each ECG signal. In the feature selection phase, INCA is used to select the most informative features from the 144 generated, which are then classified using the k-nearest neighbors (kNN) classifier. The final phase is the explainable artificial intelligence (XAI) phase. In this phase, Cardioish symbols are created, forming a Cardioish sentence. By analyzing the extracted sentence, XAI results are obtained. Additionally, these results can be integrated into connectome theory for applications in cardiology. Results: The presented Cardioish-based XFE model achieved over 99% classification accuracy on both datasets. Moreover, the XAI results related to these disorders have been presented in this research. Conclusions: The recommended Cardioish-based XFE model achieved high classification performance for both datasets and provided explainable results. In this regard, our proposal paves a new way for ECG classification and interpretation. Full article

Background: Although healthy lifestyle has been linked with a reduced risk of cardiovascular diseases (CVDs), the potential metabolic mechanism underlying this association remains unknown. Methods: We included 161,018 CVD-free participants from the UK Biobank. Elastic net regression was utilized to generate a healthy lifestyle-related metabolic signature. The Cox proportional hazards model was applied to investigate associations of lifestyle-related metabolic signature with incident CVDs, and mediation analysis was conducted to evaluate the potential mediating role of metabolic profile on the healthy lifestyle-CVD association. Mendelian randomization (MR) analysis was conducted to detect the causality. Results: During 13 years of follow-up, 17,030 participants developed incident CVDs. A healthy lifestyle-related metabolic signature comprising 123 metabolites was established, and it was inversely associated with CVDs. The hazard ratio (HR) was 0.83 (95% confidence interval [CI]: 0.81, 0.84) for CVD, 0.83 (95% CI: 0.81, 0.84) for ischemic heart disease (IHD), 0.86 (95% CI: 0.83, 0.90) for stroke, 0.86 (95% CI: 0.82, 0.89) for myocardial infarction (MI), and 0.75 (95% CI: 0.72, 0.77) for heart failure (HF) per standard deviation increase in the metabolic signature. The metabolic signature accounted for 20% of the association between healthy lifestyle score and CVD. Moreover, MR showed a potential causal association between the metabolic signature and stroke. Conclusions: Our study revealed a potential link between a healthy lifestyle, metabolic signatures, and CVD. This connection suggests that identifying an individual’s metabolic status and implementing lifestyle modifications may provide novel insights into the prevention of CVD. Full article

Background: Traditional cardiovascular disease risk prediction models generate a combined risk assessment for myocardial infarction (MI) and ischemic stroke (IS), which may inadequately reflect the distinct etiologies and disparate risk factors of MI and IS. We aim to develop prediction models that separately estimate the risks of MI and IS. Methods: Our analysis included 6,242,404 individuals over 40 years old who participated in a cardiovascular health screening examination in 2009. Potential predictors were selected based on a literature review and the available data. Cox proportional hazards models were used to construct 5-year risk prediction models for MI, and IS. Model performance was assessed through discrimination and calibration. Results: During a follow-up of 39,322,434.39 person-years, 89,140 individuals were diagnosed with MI and 116,259 with IS. Both models included age, sex, body mass index, smoking, alcohol consumption, physical activity, diabetes, hypertension, dyslipidemia, chronic kidney disease, and family history. Statin use was factored into the classification of dyslipidemia. The c-indices for the prediction models were 0.709 (0.707–0.712) for MI, and 0.770 (0.768–0.772) for IS. Age and hypertension exhibited a more pronounced effect on IS risk prediction than MI, whereas smoking, body mass index, dyslipidemia, and chronic kidney disease showed the opposite effect. The models calibrated well for low-risk individuals. Conclusions: Our findings underscore the necessity of tailored risk assessments for MI and IS to facilitate the early detection and accurate identification of heterogeneous at-risk populations for atherosclerotic cardiovascular disease. Full article

Diagnosis and treatment of acute coronary syndrome (ACS) pose particular challenges in elderly patients. When high troponin levels are detected, the distinction between non-ischemic myocardial injury (NIMI), type 1, and type 2 myocardial infarction (MI) is the necessary first step to guide further care. However, the assessment of signs of ischemia is hindered in older patients, and no simple clinical or laboratory tool proved useful in this discrimination task. Current evidence suggests a benefit of an invasive vs. conservative approach in terms of recurrence of MI, with no significant impact on mortality. In patients with multivessel disease in which the culprit lesion has been treated, a physiology-guided complete percutaneous revascularization significantly reduced major events. The management of ACS in elderly patients is an example of the actual need for a multimodal, thorough clinical approach, coupled with shared decision-making, in order to ensure the best treatment and avoid futility. Such a need will likely grow throughout the next decades, with the aging of the world population. In this narrative review, we address pivotal yet common questions arising in clinical practice while caring for elderly patients with ACS. Full article

Background: After myocardial infarction (MI), adverse left ventricular (LV) remodeling may occur. This is followed by LV hypertrophy and eventually heart failure. The remodeling process is complex and goes through multiple phases. The aim of this study was to investigate the expression of HMGB1, TGF-β1, BIRC3, ADAM17, CDKN1A, and FTO, each involved in a specific step of LV remodeling, in association with the change in the echocardiographic parameters of LV structure and function used to assess the LV remodeling process in the peripheral blood mononuclear cells (PBMCs) of patients six months after the first MI. The expression of selected genes was also determined in PBMCs of controls. Methods: The study group consisted of 99 MI patients, who were prospectively followed-up for 6 months, and 25 controls. Cardiac parameters, measured via conventional 2D echocardiography, were evaluated at two time points: 3–5 days and 6 months after MI. The mRNA expression six-months-post-MI was detected using TaqMan^® technology (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA). Results:HMGB1 mRNA was significantly higher in patients with adverse LV remodeling six-months-post-MI than in patients without adverse LV remodeling (p = 0.04). HMGB1 mRNA was significantly upregulated in patients with dilated LV end-diastolic diameter (LVEDD) (p = 0.03); dilated LV end-diastolic volume index (LVEDVi) (p = 0.03); severely dilated LV end-systolic volume index (LVESVi) (p = 0.006); impaired LV ejection fraction (LVEF) (p = 0.01); and LV enlargement (p = 0.03). It was also significantly upregulated in PBMCs from patients compared to controls (p = 0.005). TGF-β1 and BIRC3 mRNA were significantly lower in patients compared to controls (p = 0.02 and p = 0.05, respectively). Conclusions: Our results suggest that HMGB1 is involved in adverse LV remodeling six-months-post-MI, even on the mRNA level. Further research and validation are needed. Full article