Predictor Of Acute Coronary Syndrome Research Articles

Incidence and Predictors of Acute Coronary Syndrome in Patients on Maintenance Hemodialysis: A Prospective Cohort Study.

Cardiovascular diseases are the leading cause of death among patients on hemodialysis, with approximately 40% of the cardiovascular deaths linked to acute coronary syndrome. We aimed to investigate the incidence and risk factors of acute coronary syndrome in patients undergoing hemodialysis. Patients undergoing hemodialysis were prospectively enrolled from January 2018. Data regarding hospitalization due to acute coronary syndrome were collected at 3-month intervals through December 31, 2021. Cox regression model was used to estimate the association between baseline factors and incident acute coronary syndrome during follow-up. Patients' mean age was 66 years, 48% were men, and 16% had a history of coronary artery disease at enrolment. Over a median follow-up of 1,187 days, 85 patients were hospitalized due to acute coronary syndrome. Left main or triple vessel disease was identified in 67 patients. Risk factors associated with incident acute coronary syndrome included aging, male sex, smoking, low diastolic blood pressure, and baseline comorbidities, in addition to dialysis factors including low urea clearance, central venous catheter use, and history of dialysis access dysfunction. After multivariate analysis, age, diabetes, hyperlipidemia, smoking, and frequent interventions for vascular access remained significant risk factors. A high acute coronary syndrome incidence was observed in our cohort, with traditional risk factors playing a consistent role with that in the general population. A history of frequent dialysis access dysfunction was also associated with incident acute coronary syndrome.

Ceramides and pro-inflammatory cytokines for the prediction of acute coronary syndrome: a multi-marker approach

BackgroundThere is a growing body of evidence supporting the significant involvement of both ceramides and pro-inflammatory cytokines in the occurrence and progression of acute coronary syndrome (ACS).MethodsThis study encompassed 216 participants whose laboratory variables were analysed using standardised procedures. Parameters included baseline serum lipid markers, comprising total cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, triglycerides (TGs), lipoprotein(a) (LPa), fasting blood glucose, B-natriuretic peptide and hypersensitive C-reactive protein. Liquid chromatography-tandem mass spectrometry measured the concentrations of plasma ceramides. Enzyme-linked immunosorbent assay quantified tumour necrosis factor-α (TNF-α), interleukin 6 (IL6) and IL8. The correlation between ceramides and inflammatory factors was determined through Pearson’s correlation coefficient. Receiver operating characteristic (ROC) curve analysis and multivariate logistic regression evaluated the diagnostic potential of models incorporating traditional risk factors, ceramides and pro-inflammatory cytokines in ACS detection.ResultsAmong the 216 participants, 138 (63.89%) were diagnosed with ACS. Univariate logistic regression analysis identified significant independent predictors of ACS, including age, gender, history of diabetes, smoking history, TGs, TNF-α, IL-6, ceramide (d18:1/16:0), ceramide (d18:1/18:0), ceramide (d18:1/24:0), ceramide (d18:1/20:0) and ceramide (d18:1/22:0). Multivariate logistic regression analysis revealed significant associations between gender, diabetes mellitus history, smoking history, LPa, IL-6, ceramide (d18:1/16:0) and ACS. Receiver operating characteristic analysis indicated that model 4, which integrated traditional risk factors, IL-6 and ceramide (d18:1/16:0), achieved the highest area under the curve (AUC) of 0.827 (95% CI 0.770–0.884), compared with model 3 (traditional risk factors and ceramide [d18:1/16:0]) with an AUC of 0.782 (95% CI 0.720–0.845) and model 2 (traditional risk factors and IL-6), with an AUC of 0.785 (95% CI 0.723–0.846) in ACS detection.ConclusionsIn summary, incorporating the simultaneous measurement of traditional risk factors, pro-inflammatory cytokine IL-6 and ceramide (d18:1/16:0) can improve the diagnostic accuracy of ACS.

Elevated plasma pyruvate kinase M2 concentrations are associated with the clinical severity and prognosis of coronary artery disease.

Pyruvate kinase M2 (PKM2) was involved in the pathophysiology of atherosclerosis and coronary artery disease (CAD). We tested whether plasma PKM2 concentrations were correlated with clinical severity and major adverse cardiovascular events (MACEs) in CAD patients. A total of 2443 CAD patients and 238 controls were enrolled. The follow-up time was two years. Plasma PKM2 concentrations were detected by enzyme-linked immunosorbent assay (ELISA) kits (Cloud-Clone, Wuhan, China) using SpectraMax i3x Multi-Mode Microplate Reader (Molecular Devices, San Jose, USA). The predictors of acute coronary syndrome (ACS) were assessed by logistic regression analysis. The association between PKM2 concentration in different quartiles and MACEs was evaluated by Kaplan-Meier (KM) curves with log-rank test and Cox proportional hazard models. The predictive value of PKM2 and a cluster of conventional risk factors was determined by Receiver operating characteristic (ROC) curves. The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were utilized to evaluate the enhancement in risk prediction when PKM2 was added to a predictive model containing a cluster of conventional risk factors. In CAD patients, PKM2 concentration was the independent predictor of ACS (P < 0.001). Kaplan-Meier cumulative survival curves and Cox proportional hazards analyses revealed that patients with a higher PKM2 concentration had higher incidence of MACEs compared to those with a lower PKM2 concentration (P < 0.001). The addition of PKM2 to a cluster of conventional risk factors significantly increased its prognostic value of MACEs. Baseline plasma PKM2 concentrations predict the clinical severity and prognosis of CAD.

Automatic prediction of acute coronary syndrome based on pericoronary adipose tissue and atherosclerotic plaques.

Cardiovascular disease is the leading cause of human death worldwide, and acute coronary syndrome (ACS) is a common first manifestation of this. Studies have shown that pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation and atherosclerotic plaque characteristics can be used to predict future adverse ACS events. However, radiomics-based methods have limitations in extracting features of PCAT and atherosclerotic plaques. Therefore, we propose a hybrid deep learning framework capable of extracting coronary CT angiography (CCTA) imaging features of both PCAT and atherosclerotic plaques for ACS prediction. The framework designs a two-stream CNN feature extraction (TSCFE) module to extract the features of PCAT and atherosclerotic plaques, respectively, and a channel feature fusion (CFF) to explore feature correlations between their features. Specifically, a trilinear-based fully-connected (FC) prediction module stepwise maps high-dimensional representations to low-dimensional label spaces. The framework was validated in retrospectively collected suspected coronary artery disease cases examined by CCTA. The prediction accuracy, sensitivity, specificity, and area under curve (AUC) are all higher than the classical image classification networks and state-of-the-art medical image classification methods. The experimental results show that the proposed method can effectively and accurately extract CCTA imaging features of PCAT and atherosclerotic plaques and explore the feature correlations to produce impressive performance. Thus, it has the potential value to be applied in clinical applications for accurate ACS prediction.

Diagnoses and Outcomes of Patients with Suspicion of Acute Coronary Syndrome and Raised High Sensitive Troponin I: A Single Center Study from Pakistan.

Objectives Troponins are classically raised in acute coronary syndrome (ACS) although other cardiovascular and non-cardiovascular causes are recognized. We aimed to see the association of high sensitivity (Hs) Troponin I values exceeding the sex-specific 99th percentile upper reference limit (URL) with diagnoses, emergency department (ED) outcomes, 30-day outcomes of admitted patients and predictors of ACS in both genders. Materials and Methods A retrospective study of all patients presenting to the emergency department from January 2019 to April 2021 with suspicion of ACS and Hs-Troponin I values greater than the sex-specific 99th percentile URL. Statistical Analysis SPSS version 24 was used, Pearson's chi-square tests, Fisher's exact test, Kruskal-Wallis test, Mann-Whitney U test, and odds ratios, including the 95% confidence intervals, for each characteristic were used for analysis. A p -value of < 0.05 was considered significant. Results There were a total of 5,982 patients (3,031 males, 2,951 females), out of which 878 patients were admitted under the cardiology specialty. In patients who were admitted to the ward, mortality was higher in females (8.2%) with less than a 10-fold rise in Hs-Troponin I while similar in both genders (7.6%) in patients with Hs-troponin I greater than 10-fold of sex-specific 99th percentile URL. Raised low-density lipoprotein-cholesterol was a significant factor associated with 2.4 times higher odds of ACS. Conclusion Women with Hs-Troponin values up to 10 times the URL, i.e., 15.6-160 ng/L have higher mortality than their male counterparts. LDL-cholesterol is a significant risk factor for ACS which should be controlled for its prevention.

Clinical Presentation and Predictors of Acute Coronary Syndrome: A Cross Sectional Study

N/A

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis.

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches.

Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study.

Acute coronary syndrome (ACS) has been one of the most important issues in global public health. The high recurrence risk of patients with coronary heart disease (CHD) has led to the importance of post-discharge care and secondary prevention of CHD. Previous studies provided binary results of ACS recurrence risk; however, studies providing the recurrence risk of an individual patient are rare. In this study, we conducted a model which provides the recurrence risk probability for each patient, along with the binary result, with two datasets from the Korea Health Insurance Review and Assessment Service and Chungbuk National University Hospital. The total data of 6,535 patients who had been diagnosed with ACS were used to build a machine learning model by using logistic regression. Data including age, gender, procedure codes, procedure reason, prescription drug codes, and condition codes were used as the model predictors. The model performance showed 0.893, 0.894, 0.851, 0.869, and 0.921 for accuracy, precision, recall, F1-score, and AUC, respectively. Our model provides the ACS recurrence probability of each patient as a personalized ACS recurrence risk, which may help motivate the patient to reduce their own ACS recurrence risk. The model also shows that acute transmural myocardial infarction of an unspecified site, and other sites and acute transmural myocardial infarction of an unspecified site contributed most significantly to ACS recurrence with an odds ratio of 97.908 as a procedure reason code and with an odds ratio of 58.215 as a condition code, respectively.

Endothelial dysfunction by brachial artery flow-mediated dilatation as predictor of major adverse cardiovascular event in acute coronary syndrome

Endothelial dysfunction (ED) is the earliest marker in the process of atherosclerosis formation and is thought to be a risk predictor of acute coronary syndrome (ACS) outcome. Our aim is to analyse the ED by brachial artery flow-mediated dilatation (FMD) as a predictor of major adverse cardiovascular event (MACE) in ACS patients. This is a prospective cohort study with consecutive sampling for ACS in a hospital at Indonesia in April-May, 2021. 69 patients who met the inclusion were examined for brachial artery FMD with value &lt;5% stated as ED. MACE was observed during in-hospital, 1 month, and 6 months after hospitalization, and analysed using bivariate analysis and multivariate logistic regression analysis. From bivariate analysis, significant result was found for FMD &lt;5% (RR 7,13; 95%CI=2,07-24,58; p=0.001) with 1 month of follow-up MACE and diabetes mellitus (RR 2,57; 95%CI=1,15-5,73; p=0,043) and FMD &lt;5% (RR=5,60; 95%CI=2,44-12,86; p&lt;0.001) with 6 months of follow-up MACE. In multivariate analysis, FMD &lt;5% was found as the most significant predictor of 1 month and 6 months follow-up MACE. ED by brachial artery FMD was an important independent predictor of the MACE during 1 month and 6 months follow-up after ACS and might be useful for risk stratification outcome.

Acute coronary syndrome prediction in emergency care: A machine learning approach

Background and ObjectiveClinical concern for acute coronary syndrome (ACS) is one of emergency medicine's most common patient encounters. This study aims to develop an ensemble learning-driven framework as a diagnostic support tool to prevent misdiagnosis. MethodsWe obtained extensive clinical electronic health data on patient encounters with clinical concerns for ACS from a large urban emergency department (ED) between January 2017 and August 2020. We applied an analytical framework equipped with many well-developed algorithms to improve the data quality by addressing missing values, dimensionality reduction, and data imbalance. We trained ensemble learning algorithms to classify patients with ACS or non-ACS etiologies of their symptoms. We used performance evaluation metrics such as accuracy, sensitivity, precision, F1-score, and the area under the receiver operating characteristic (AUROC) to measure the model's performance. ResultsThe analysis included 31,228 patients, of whom 563 (1.8%) had ACS and 30,665 (98.2%) had alternative diagnoses. Eleven features, including systolic blood pressure, brain natriuretic peptide, chronic heart disease, coronary artery disease, creatinine, glucose, heart attack, heart rate, nephrotic syndrome, red cell distribution width, and troponin level, are reported as significantly contributing risk factors. The proposed framework successfully classifies these cohorts with sensitivity and AUROC as high as 86.3% and 93.3%. Our proposed model's accuracy, precision, specificity, Matthew's correlation coefficient, and F1-score were 85.7%, 86.3%, 93%, 80%, and 86.3%, respectively. ConclusionOur proposed framework can identify early patients with ACS through further refinement and validation.

Current recommendations for revascularization of non-infarct-related artery in patients presenting with ST-segment elevation myocardial infarction and multivessel disease.

ST-segment elevation myocardial infarction (STEMI) is a leading cause of morbidity and mortality worldwide. Immediate reperfusion therapy of the infarct-related artery (IRA) is the mainstay of treatment, either via primary percutaneous coronary intervention (PPCI) or thrombolytic therapy when PPCI is not feasible. Several studies have reported the incidence of multivessel disease (MVD) to be about 50% of total STEMI cases. This means that after successful PPCI of the IRA, residual lesion(s) of the non-IRA may persist. Unlike the atherosclerotic plaque of stable coronary artery disease, the residual obstructive lesion of the non-IRA contains a significantly higher prevalence of vulnerable plaques. Since these lesions are a strong predictor of acute coronary syndrome, if left untreated they are a possible cause of future adverse cardiovascular events. Percutaneous coronary intervention (PCI) of the obstructive lesion of the non-IRA to achieve complete revascularization (CR) is therefore preferable. Several major randomized controlled trials (RCTs) and meta-analyses demonstrated the clinical benefits of the CR strategy in the setting of STEMI with MVD, not only for enhancing survival but also for reducing unplanned revascularization. The CR strategy is now supported by recently published clinical practice guidelines. Nevertheless, the benefit of revascularization must be weighed against the risks from additional procedures.

Increased Risk of Acute Coronary Syndrome in Ankylosing Spondylitis Patients With Uveitis: A Population-Based Cohort Study.

BackgroundUveitis, a sight-threatening ocular inflammatory state, is associated with autoimmune diseases and systemic inflammation. This prolonged systemic inflammation may cause plaque formation in coronary arteries, subsequently resulting in acute coronary syndrome (ACS).MethodsThis retrospective, population-based study (15-year period) used the Longitudinal Health Insurance Database based on the National Health Insurance Research Database in Taiwan. Chi-square and Student’s t-tests were used to examine differences between the study and comparison cohorts for categorical and continuous variables, respectively. Fine and Gray’s competing risk model was used to determine the hazard ratio of the risk of ACS. Furthermore, the cumulative risk of ACS was determined using Kaplan-Meier analysis.ResultsA total of 1,111 patients with AS and uveitis were enrolled in this study cohort, and 4,444 patients with AS without uveitis were enrolled in the comparison cohort. After adjustment for age, sex, and comorbidities, patients with AS and uveitis demonstrated an increased risk of ACS compared to those without uveitis (adjusted hazard ratio: 1.675, p<0.001). In addition, Kaplan-Meier analysis revealed that patients with AS and uveitis had a significantly higher risk of ACS than those without uveitis (p<0.001). Age, diabetes mellitus, hypertension, hyperlipidemia, chronic obstructive pulmonary disease, asthma, and systemic steroids were significant risk factors for ACS. Both anterior uveitis and posterior segment involvement were associated with an increased risk of ACS in patients with AS. All-cause mortality was higher in the uveitis group (9.81%) than in the non-uveitis group (8.10%) (p=0.015).ConclusionOur analysis revealed that uveitis could potentially be a predictor of ACS in patients with AS. However, further prospective controlled studies are required to assess the association between uveitis and ACS in patients with AS.

Scoping review of the association between postsurgical pain and heart rate variability parameters.

Surgical interventions can elicit neuroendocrine and sympathovagal responses, leading to cardiac autonomic imbalance. Cardiac complications account for approximately 30% of postoperative complications. Altered heart rate variability (HRV) was initially described in the 1970s as a predictor of acute coronary syndromes and has more recently been shown to be an independent predictor of postoperative morbidity and mortality after noncardiac surgery. In general, HRV reflects autonomic balance, and altered HRV measures have been associated with anesthetic use, chronic pain conditions, and experimental pain. Despite the well-documented relationship between altered HRV and postsurgical outcomes and various pain conditions, there has not been a review of available evidence describing the association between postsurgical pain and HRV. We examined the relationship between postsurgical pain and HRV. MEDLINE and EMBASE databases were searched until December 2020 and included all studies with primary data. Two reviewers independently assessed risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Review of Interventions. A total of 8 studies and 1002 participants were included. Studies examined the association of postsurgical pain and HRV or analgesia nociception index derived from HRV. There was a statistically significant association between HRV measures and postsurgical pain in 6 of 8 studies. Heterogeneity of studies precluded meta-analyses. No studies reported cardiovascular outcomes. There is a potential association between postsurgical pain and HRV or analgesia nociception index, although results are likely impacted by confounding variables. Future studies are required to better delineate the relationship between postsurgical pain and HRV and impacts on cardiovascular outcomes.

Use of the Progression of Adapted Diabetes Complications Severity Index to Predict Acute Coronary Syndrome, Ischemic Stroke, and Mortality in Asian Patients with Type 2 Diabetes Mellitus: A Nationwide Cohort Investigation

BackgroundWe report on a retrospective population study aimed at identifying and validating the progression of adapted Diabetes Complications Severity Index (DCSI) for acute coronary syndrome (ACS), ischemic stroke, and mortality in Asian people with type 2 diabetes mellitus (DM). MethodsUtilizing a Taiwanese national dataset, we included 84 450 type 2 diabetic individuals between 2000 and 2011. The area under the receiver operating characteristic curve (C statistics of logistic model) and the C statistics of the Cox model were used to evaluate whether the progression of diabetic complication status could be a predictor of ACS, ischemic stroke, and death. The optimum threshold for adverse outcomes risk stratification were obtained using Youden's J statistic as the cutoff that gives the highest threshold. ResultsAmong the study patients, the C statistics of the logistic model of the progression of the score predictive of ACS, ischemic stroke, and death were 0.72 (95% confidence interval [CI]: 0.71-0.73), 0.84 (95% CI: 0.84-0.85), and 0.66 (95% CI: 0.65-0.67), respectively. The progression of adapted DCSI had moderate discrimination for ACS, ischemic stroke, and death (C statistics = 0.71, 0.72, and 0.75, respectively) based on Cox regression analysis (Harrell C). The optimum threshold of the progression of the score for ACS, ischemic stroke, and death in type 2 DM patients were 0.30, 0.36, and 0.39, respectively. Conclusions: The acceptable discriminative power of the progression of adapted DCSI for Asian people affected by type 2 DM was demonstrated in a large cohort in Taiwan.

MicroRNA-320a-3p Signatures as a Satisfactory Predictor of Acute Coronary Syndrome and Attenuates Inflammation by Targeting X-Linked Inhibitor of Apoptosis Protein

Acute coronary syndrome (ACS) is a heart disease with a high mortality rate. Recently, more and more evidence illustrated that microRNAs (miRNA) participated in regulating the occurrence of heart disease. This study aimed to detect the level of serum miR-320a-3p in patients with ACS, predict its possibility as a candidate gene for diagnosis, and explore its potential mechanism in the regulation of ACS. 139 ACS patients and 126 controls were recruited in this study. The expression level of miR-320a-3p was determined by qRT-PCR. The predictive value in ACS was assessed by receiver operating characteristic (ROC) curve. Enzyme-linked immunosorbent assay (ELISA) was used to measure the protein expression levels of inflammatory factors. The downstream targets of miR-320a-3p were verified by luciferase reporter gene assay. In ACS patients and rat models, the expression level of serum miR-320a-3p was significantly increased. ROC curve revealed that abnormal expression of miR-320a-3p was of diagnostic value for ACS. In an in vivo rat model, down-regulation of miR-320a-3p inhibited the production of von Willebrand factor (vWF), Heart fatty acid-binding protein (H-FABP), interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α). In other words, down-regulation of miR-320a-3p reduced rat vascular endothelial injury and inflammation. X-linked inhibitor of apoptosis protein (XIAP) was determined to be a direct target of miR-320a-3p. miR-320a-3p is useful for the diagnosis of ACS. Animal experiments confirmed that up-regulated miR-320a-3p promoted vascular endothelial injury and inflammatory response by targeting XIAP, thus promoting the development of ACS. MiR-320a-3p may be a new breakthrough in the diagnosis and treatment of ACS.

Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography

ObjectivesTo evaluate whether radiomics signature of pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could improve the prediction of future acute coronary syndrome (ACS) within 3 years.MethodsWe designed a retrospective case-control study that patients with ACS (n = 90) were well matched to patients with no cardiac events (n = 1496) during 3 years follow-up, then which were randomly divided into training and test datasets with a ratio of 3:1. A total of 107 radiomics features were extracted from PCAT surrounding lesions and 14 conventional plaque characteristics were analyzed. Radiomics score, plaque score, and integrated score were respectively calculated via a linear combination of the selected features, and their performance was evaluated with discrimination, calibration, and clinical application.ResultsRadiomics score achieved superior performance in identifying patients with future ACS within 3 years in both training and test datasets (AUC = 0.826, 0.811) compared with plaque score (AUC = 0.699, 0.640), with a significant difference of AUC between two scores in the training dataset (p = 0.009); while the improvement of integrated score discriminating capability (AUC = 0.838, 0.826) was non-significant. The calibration curves of three predictive models demonstrated a good fitness respectively (all p > 0.05). Decision curve analysis suggested that integrated score added more clinical benefit than plaque score. Stratified analysis revealed that the performance of three predictive models was not affected by tube voltage, CT version, different sites of hospital.ConclusionCCTA-based radiomics signature of PCAT could have the potential to predict the occurrence of subsequent ACS. Radiomics-based integrated score significantly outperformed plaque score in identifying future ACS within 3 years.Key Points• Plaque score based on conventional plaque characteristics had certain limitations in the prediction of ACS.• Radiomics signature of PCAT surrounding plaques could have the potential to improve the predictive ability of subsequent ACS.• Radiomics-based integrated score significantly outperformed plaque score in the identification of future ACS within 3 years.

Small Dense Low-Density Lipoprotein for Risk Prediction of Acute Coronary Syndrome.

BackgroundElevated Low density Lipoprotein (LDL) levels have been traditionally associated with development of atherosclerotic coronary artery disease (CAD). As LDL-cholesterol levels are not always raised in acute coronary syndrome (ACS) patients, sd-LDL is an emerging risk factor. The purpose of the present study was tri-fold. Firstly, it aimed to investigate the association of sd-LDL as a risk factor in ACS patients. Secondly, it aimed to correlate the presence of sd-LDL with severity of coronary artery disease as determined by coronary angiography. Lastly, it aimed to correlate the presence of sd-LDL with short-term outcomes.MethodsThis was a prospective, hospital-based, cross-sectional, case-control study conducted over a 1-year study duration. Patients above the age of 18 years diagnosed with ACS within 24 h of admission were studied. Blood samples were collected after all patients had undergone coronary angiography and sd-LDL levels were measured.ResultsACS patients had significantly higher sd-LDL levels than non-ACS patients (16.10 ± 1.42 mg/dL vs. 12.67 ± 0.71 mg/dL, P = 0.036). Males had significantly higher sd-LDL levels than females (16.79 ± 1.55 mg/dl vs. 10.77 ± 2.62 mg/dl, P=0.047). Patients with non-left anterior descending coronary artery (LAD) involvement had significantly higher sd-LDL levels compared to patients with LAD involvement (21.92 ± 3.55 mg/dL vs. 14.03 ± 1.35 mg/dL, P = 0.007).ConclusionThese results suggest that sd-LDL is a risk factor for the development of ACS in an Indian population.

High Cardiovascular Risk in Older Men With Severe Peripheral Artery Calcification on High-Resolution Peripheral QCT Scans: The STRAMBO Study.

[Figure: see text].

External validation of a clinical decision rule to identify patients at low risk for acute coronary syndrome who do not need objective coronary artery disease testing

Abstract Background Rapid high-sensitivity cardiac troponin (hs-cTn) based algorithms have substantially improved the early rule-out of acute myocardial infarction (AMI) and thereby facilitated the selection of patients eligible for outpatient management. However, it remains unclear, which patients after rule-out of AMI should still undergo objective anatomic or functional cardiac testing for the detection of relevant coronary artery disease. A pilot study has derived a clinical decision rule for the selection of patients who do not need objective anatomic or functional cardiac testing for coronary artery disease (“No Objective Testing” (NOT) rule). Purpose To externally validate the performance of the NOT-rule in a multicentre study. Methods Patients presenting to the ED with symptoms suggestive of an acute coronary syndrome (ACS) were enrolled in a large prospective international multicentre study at 12 study sites in five European countries. Two independent cardiologists centrally adjudicated the final diagnosis using all clinical data including cardiac imaging and at least 90-day follow-up. The NOT-rule is applied in patients, in whom a 2h accelerated diagnostic protocol (using hs-cTnI concentrations at 0h/2h and ECG data) has ruled-out AMI and based on clinical variables. In brief, the first rule is a weighted score derived from independent predictors of ACS that classifies patients as low-risk if they score ≤4 points. The second rule was simplified and ruled patients out if they were younger than 50 years, had no history of an AMI or known CAD, and no prescribed nitrates. The third rule equals the second except nitrate use was omitted. Primary objective was the safety and efficacy of the NOT-rules for rule-out of major adverse cardiac events (MACE) including AMI, unstable angina pectoris, urgent or emergency revascularisation or cardiovascular death at 30-days of follow-up. Secondary objective was the safety and efficacy for rule-out of MACE at 2-years. Results Out of 3188 enrolled patients, 2162 (68%) had hs-cTnI concentrations at 0h and 2h below the 99th centile as well as a non-diagnostic ECG and were therefore eligible for the analysis. MACE at 30-days occurred in 302 (14%) patients. The second and third rule offered highest safety and efficacy for rule-out of MACE at 30-days. Both identified 492 (23%) patients at low-risk with a sensitivity of 99.7% (95% CI 98.2–99.9%) and a negative predictive value (NPV) of 99.8% (95% CI 98.6–99.9%). One MACE was missed within 30-days (revascularisation of a one-vessel CAD). Sensitivity 98.9% (95% CI 97.1–99.7%) and NPV 99.2% (95% CI 97.8–99.7) were also very high for 1-year MACE, as well as 2-year MACE 98.4% (95% CI 96.5–99.4%) and 98.4% (95% CI 96.5–99.3%), respectively. Conclusions The NOT rules proved to be a safe tool that identifies nearly one-fourth of patients at very low risk for MACE, who may not need objective anatomic or functional cardiac testing for coronary artery disease. Performance of NOT rules Funding Acknowledgement Type of funding source: Other. Main funding source(s): Swiss National Science Foundation, the Swiss Heart Foundation, the KTI, the European Union, the Stiftung für kardiovaskuläre Forschung Basel, the University of Basel, the University Hospital Basel

Growth Differentiation Factor 15 in Patients with Acute Coronary Syndrome and Its Relation to Type 2 Diabetes Mellitus

Background: Growth differentiation factor 15 (GDF15) gene expression level is a novel biomarker that reflects inflammation and oxidative stress in patients with acute coronary syndrome and those complicated with type 2 diabetes mellitus (T2DM). Growth differentiation factor 15 high levels could be a reliable sensitive biomarker for the prediction of acute coronary syndrome. Patients and Methods: This Cross-sectional/case-control study was conducted at the Cardiology Department, Assiut University during the period from January 2018 to December 2019, and patients were selected with the exclusion of any inflammatory or autoimmune disease. Results: Growth differentiation factor 15 gene expression was significantly higher in acute coronary syndrome patients compared to the controls (p-value <0.001) and its expression was significantly higher in acute coronary syndrome patients with type 2 diabetes mellitus group compared to acute coronary syndrome patient group (p-value<0.05). Growth differentiation factor 15 had 92% sensitivity and 77% specificity in the prediction of acute coronary syndrome which was statistically significant (p-value< 0.001), while yielded 73% sensitivity and 53% specificity of no statistical significance (p-value=0.829) in the prediction of type 2 diabetes mellitus among patients with acute coronary syndrome. Conclusion: Growth differentiation factor 15 gene expression could be used as a reliable sensitive biomarker for prediction of the acute coronary syndrome but more studies will be mandatory soon to clarify its role as a predictive marker for type 2 diabetes mellitus development among patients with the acute coronary syndrome.