Traditional Acute Coronary Syndrome Research Articles

Non-Atherosclerosis Acute Coronary Syndrome: A Review Article

Non-atherosclerotic acute coronary syndrome (ACS) refers to a group of conditions that cause myocardial damage and clinical symptoms like traditional ACS but are not primarily caused by coronary artery plaque rupture or obstruction. This abstract provides an overview of the etiology, clinical presentation, diagnosis, and management of non-atherosclerotic ACS. Several etiologies can lead to non-atherosclerotic ACS, including coronary artery vasospasm, connective tissue disorders, stress-induced cardiomyopathy (Takotsubo syndrome), and vasculitis. The clinical presentation can mimic traditional ACS, with chest pain being the most common symptom. Diagnostic tools such as electrocardiography (ECG), laboratory biomarkers, echocardiography, coronary angiography, cardiac magnetic resonance imaging (MRI), and nuclear imaging play a crucial role in distinguishing non-atherosclerotic ACS from traditional ACS. Treatment strategies for non-atherosclerotic ACS are often based on expert consensus and clinical experience due to the lack of specific guidelines. Management approaches depend on the underlying etiology and may include pharmacological therapy, invasive interventions such as percutaneous coronary intervention (PCI) or surgery, and risk factor modification. Prompt diagnosis and appropriate management are crucial for improving patient outcomes. Further research and prospective studies are needed to enhance our understanding of non-atherosclerotic ACS and optimize its management. Keyword: Coronary Artery Spasm, Coronary Syndrome, Non-Atherosclerosis Acute Myocardial Injury, Takotsubo Syndrome, Vasculitis.

A Case of Kounis Syndrome Caused by Anaphylaxis During Surgery

Kounis syndrome is a hypersensitivity coronary disorder associated with several triggers, such as drugs, foods, environmental, coronary stents and other factors. It was originally proposed by the Greek cardiologist Nicholas Kounis in 1991. Kounis syndrome is a life-threatening and challenging emergency disease, which is constantly being recognized and studied. It is a disease that is constantly being recognized and Kounis syndrome can be classified into three types, involving coronary artery spasm, plaque rupture or stent thrombosis respectively. Most patients with coronary artery spasm caused by allergies can recover completely after active treatment, and the prognosis is good. If not, it may cause severe myocardial damage and even myocardial infarction, which threatens the patient's life. In our case, a 56-year-old female patient was scheduled to undergo right pelvic resection. A few minutes after the start of intravenous succinylated gelatin infusion, she was experienced a decrease in heart rate, blood pressure, oxygen saturation and ST elevation on 2-lead. An anaphylactic reaction was suspected after rapid differential diagnosis and gradually recovered with treatment. In this context, a rapid differential diagnosis and a highly suspicious become essential, because the management of Kounis syndrome is different from the traditional acute coronary syndrome. Kounis syndrome requires simultaneous diagnosis and treatment of both allergic reaction and acute coronary syndrome based on their manifestations and risk factors. This case describes the onset, development, and recovery of Kounis syndrome in this patient in detail, and illustrates the classification, pathophysiology, and treatment of Kounis syndrome. It is hoped that through the management of this case, the occurrence of Kounis syndrome can be rapidly identified and treated accordingly so that avoiding disaster for patients.

Is rheumatoid arthritis a risk factor for acute coronary syndrome also among individuals at elevated risk, such as individuals presenting with acute chest pain?

BackgroundPatients with rheumatoid arthritis (RA) are, on average, at increased risk of acute coronary syndrome (ACS) compared to the general population, but it remains unknown whether RA remains an ACS...

The role of the plasma glycosylated hemoglobin A1c/Apolipoprotein A-l ratio in predicting cardiovascular outcomes in acute coronary syndrome

Background and aimsGlucose and lipid metabolism are major prognostic indicators of coronary heart disease. The ratio of plasma glycosylated hemoglobin A1c (HbA1c) to apolipoprotein A-l (ApoA-l) is an indirect measure of insulin resistance. The study aimed to evaluate whether the HbA1c/ApoA-1 ratio can predict the prognosis in patients with the acute coronary syndrome (ACS). Methods and resultsA total of 476 ACS patients diagnosed by coronary angiography were enrolled in this longitudinal, observational, retrospective study. Plasma HbA1c, fasting blood glucose and lipid profile were measured. Patients were stratified according to the tertiles of HbA1c/ApoA-l levels. Cox proportional hazard model was used to examine the predictive value of HbA1c/ApoA-l for study endpoints. The association between the Log HbA1c/ApoA-l ratio and major adverse cardiovascular events (MACEs) was estimated using multiple logistic regression. Baseline characteristics showed a mean age of 66 ± 8 years, and 52.5% were hypertensive, 26.8% diabetic, and 54.5% current or prior smokers. During a mean follow-up period of 22.3 ± 1.7 months, 59 deaths occurred. After adjusting for age, gender, smoking, hypertension, diabetes, and coronary artery disease severity, patients in the highest HbA1c/ApoA-l ratio tertile had a 4.36-fold increased risk of mortality compared with those in the lowest tertile. The multivariate logistic regression showed that the Log HbA1c/ApoA-l ratio was associated with MACEs (Odds ratio 2.95, p = 0.013). ConclusionAfter adjusting for traditional cardiovascular risk factors and ACS severity scores, the HbA1c/ApoA-1 ratio remained an independent predictor of all-cause mortality and MACEs in the ACS patients undergoing angiography.

Psychological and clinical characteristics of patients with spontaneous coronary artery dissection: A case-control study

BackgroundThe relative frequency of psychological factors in patients with spontaneous coronary artery dissection (SCAD) compared to patients with traditional atherosclerosis-related type 1 acute coronary syndrome (ACS) is unknown. This study examines whether psychological factors and emotional or physical precipitants are more common in SCAD patients versus atherosclerosis-related ACS patients. MethodsParticipants with SCAD were recruited from a Dutch SCAD database. Given the predominance of SCAD in women (>90%), only female patients were included. The age- and sex-matched atherosclerosis-related ACS group was identified from a registry database. Online questionnaires and medical records were used to investigate psychological factors and clinical information. Univariate and multivariate logistic regression models were used to examine differences between 172 SCAD patients and 76 ACS patients on emotional and physical precipitants prior to the event and psychological factors after the event. ResultsPatients with SCAD were more likely to experience an emotional precipitant in the 24 h prior to the event (56%), compared with the ACS group (39%) (OR = 1.98, 95%CI 1.14–3.44). Multivariate analyses showed that this association remained significant after adjustment for covariates (OR = 2.17, 95%CI 1.08–4.36). At an average of 3.2 years post-hospitalization for the SCAD or atherosclerosis-related ACS event, both patient groups had similar high levels of perceived stress (50% vs. 45%, p = .471) and fatigue (56% vs. 53%, p = .643). ConclusionsThis study shows that risk profiles for SCAD differ from traditional atherosclerosis-related ACS. Our findings may help health professionals to recognize SCAD and offer tailored rehabilitation and prevention programs.

Evidential MACE prediction of acute coronary syndrome using electronic health records

BackgroundMajor adverse cardiac event (MACE) prediction plays a key role in providing efficient and effective treatment strategies for patients with acute coronary syndrome (ACS) during their hospitalizations. Existing prediction models have limitations to cope with imprecise and ambiguous clinical information such that clinicians cannot reach to reliable MACE prediction results for individuals.MethodsTo remedy it, this study proposes a hybrid method using Rough Set Theory (RST) and Dempster-Shafer Theory (DST) of evidence. In details, four state-of-the-art models, including one traditional ACS risk scoring model, i.e., GRACE, and three machine learning based models, i.e., Support Vector Machine, L1-Logistic Regression, and Classification and Regression Tree, are employed to generate initial MACE prediction results, and then RST is applied to determine the weights of the four single models. After that, the acquired prediction results are assumed as basic beliefs for the problem propositions and in this way, an evidential prediction result is generated based on DST in an integrative manner.ResultsHaving applied the proposed method on a clinical dataset consisting of 2930 ACS patient samples, our model achieves 0.715 AUC value with competitive standard deviation, which is the best prediction results comparing with the four single base models and two baseline ensemble models.ConclusionsFacing with the limitations in traditional ACS risk scoring models, machine learning models and the uncertainties of EHR data, we present an ensemble approach via RST and DST to alleviate this problem. The experimental results reveal that our proposed method achieves better performance for the problem of MACE prediction when compared with the single models.

Utilizing electronic health records to predict multi-type major adverse cardiovascular events after acute coronary syndrome

Acute coronary syndrome (ACS), as a leading cause of death worldwide, is responsible for over 1.5 million hospital admissions in China each year. Major adverse cardiovascular event (MACE) after ACS often occurs suddenly resulting in high mortality and morbidity. Timely and accurate prediction of MACE after ACS could assist clinicians to pay more attention to high-risk patients and improve the quality and efficiency of care accordingly. Traditional prediction of MACE after ACS is based on aggressive medical therapy and guideline-driven treatment of a small set of coronary risk factors and is designed to predict one specific type of MACE. With the rapid development of electronic health records (EHR), more and more data-driven approaches have been proposed to explore the huge potentials of EHR data. However, existing data-driven approaches have typically been conducted by building one common predictive model and neglecting the relations among multi-type MACEs. In this paper, we propose a novel boosted regularized multi-label learning approach for multi-type MACE prediction. In detail, we formulate the prediction problem of multi-type MACE as a multi-label classification problem and incorporate the relational information among multi-type MACEs into modeling by using regularization terms to encode the corresponding relational information. In addition and to address the problem that some types of MACE are rarely occur, we utilize a boosting weighted sampling strategy to iteratively select a small subset of patient samples to train new multi-type MACE prediction models that can correct the previously wrongly predicted patient samples. A case study was conducted on a real ACS clinical dataset consisting of 2930 patient samples and collected from a Chinese hospital to validate the effectiveness of the proposed model. The performance of our best model remains robust and reaches 0.7 and 0.640 for both ischemic and hemorrhagic event prediction, respectively, in terms of AUC, and had over 2.7% and 23.1% performance gain in comparison with the state-of-the-art GRACE and CRUSADE model, respectively. The experimental results show the efficiency of our model in improving the performances on multi-type MACE prediction after ACS, by comparing with both traditional ACS risk stratification models which are widely adopted in the medical domain and state-of-the-art multi-label learning methods without efforts on incorporating the relational information of multi-type MACE into learning. In addition, we illustrate some interesting results pertaining to predictive risk factors to specific types of MACE, some of which are not only consistent with existing medical domain knowledge, but also contain suggestive hypotheses that could be validated by further investigations in the medical domain.

Importance of a Risk Stratification Strategy to Identify High-risk Patients Presenting With Cocaine-associated Acute Coronary Syndrome.

Current guidelines recommend treating patients with cocaine-associated chest pain, unstable angina, or myocardial infarction similarly to patients with traditional acute coronary syndrome (ACS). Risk stratifying these patients could potentially reduce unnecessary procedures and improve resource utilization. This is a retrospective cross-sectional analysis of 258 patients presenting with cocaine-associated ACS who underwent cardiac catheterization in a community teaching hospital between 2006 and 2015. The primary outcome was the prevalence of acute obstructive coronary artery disease (CAD) requiring percutaneous coronary intervention and coronary artery bypass grafting compared with that of patients with normal coronary or nonobstructive disease. Of the studied population, 36% had obstructive CAD requiring intervention and 64% were found to have normal coronaries or nonobstructive disease. Significant risk factors for obstructive CAD were older age, history of CAD, diabetes mellitus, dyslipidemia, ST-segment-elevation myocardial infarction, and troponin elevation. A logistic model was developed based on these variables, applied to the studied population, and was found to have 93% sensitivity in predicting the likelihood of obstructive CAD. Cardiac catheterization in patients presenting with cocaine-associated ACS may be overutilized. A predictive model based on clinical risk factors may help individualize patient care and reduce unnecessary invasive diagnostic interventions.