Predictor Of Acute Myocardial Infarction Research Articles

Can prediction of acute myocardial infarction using a deep learning-enabled electrocardiogram be used in real-world practice?

Abstract Background Diagnosing acute myocardial infarction (MI) in the emergency department poses challenges. The utilization of a 12-lead electrocardiogram (ECG) is pivotal for detecting ST-segment elevation myocardial infarction (STEMI), yet enhancing ECG interpretation through a systematic algorithm could significantly improve diagnostic accuracy. Nonetheless, delayed diagnosis or misdiagnosis of STEMI is not uncommon in daily practice. Purpose This study aims to develop a fully convolutional network (FCN) based on ECG data as a diagnostic support tool and to evaluate its effectiveness in STEMI detection. Methods A retrospective cohort study involved 3,298 patients with angiographically confirmed STEMI and 37,393 control ECGs, comprising normal readings, false positives, and ECGs mimicking STEMI. The FCN underwent internal training, external validation, and testing across various data splits (80:10:10, 70:15:15, and 60:20:20). Performance evaluation on validation data included assessing the area under the receiver operating characteristic curve (AUC). Subsequently, the trained model predicted unseen data, and sensitivity, specificity, false positives, and false negatives were calculated to evaluate model performance. Results The FCN exhibited exceptional performance in STEMI detection during both internal training and external validation across different data splits. The receiver operating characteristic curves yielded AUC values of 0.995 (80:10:10), 0.994 (70:15:15), and 0.995 (60:20:20). Consistently high values were observed in precision-recall curves, with AUC values of 0.974 (80:10:10), 0.962 (70:15:15), and 0.973 (60:20:20). When utilizing this FCN model in the clinical setting to reanalyze 20 cases of previously misdiagnosed electrocardiograms, it correctly diagnosed 17 cases. Conclusion Integrating the FCN into clinical practice can notably improve the timely and accurate diagnosis of acute MI, reducing instances of delayed or erroneous STEMI diagnoses and facilitating prompt reperfusion therapy.

Gender Differences in Trends in Incidence and Mortality of Acute Myocardial Infarction in the Small Island Developing State of Barbados.

Objective To determine trends, identify predictors of acute myocardial infarction (AMI) incidence and mortality, and explore performance metrics for AMI care in Barbados. Methods Data on all cases diagnosed with AMI were collected by the Barbados National Registry for Non-Communicable Diseases (BNR) from the island's only tertiary hospital, the Queen Elizabeth Hospital, and the National Vital Registration Department. Participants who survived hospital admission were then followed up at 28 days and one year post event via telephone survey and retrieval of death certificates. Age-standardized incidence and mortality rates were calculated. Determinants of mortality at 28 days were examined in multivariable logistic regression models. Median and interquartile ranges (IQR) were calculated for performance metrics (e.g., time from pain onset to reperfusion). Results In a 10-year period between 2010 and 2019, 4,065 cases of myocardial infarction were recorded. The median age of the sample was 73 years (IQR: 61,83), and approximately half (47%) were female. Over a 10-year period, standardized incidence increased in women on average yearly by three per 100,000 (95% CI: 1 to 6; p=0.02), while in men, the average increase per year was six per 100,000 (95% CI: 4 to 8; p<0.001). There was no increase in 28-day mortality in women; mortality in men increased each year by 2.5 per 100,000 (95% CI: 0.4 to 4.5; p=0.02). The time from arrival at the hospital to the ECG was 44 minutes IQR (20,113). Conclusion AMI incidence and mortality are increasing in Barbados, and men have a higher velocity of mortality rate increase than women, which contradicts global data.

Risk prediction of acute myocardial infarction based on deep neural network

Risk prediction of acute myocardial infarction based on deep neural network

Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Cardiac image and mesh are two primary modalities to present the shape and structure of the heart and have been demonstrated to be efficient in CVD prediction and diagnosis. However, previous research has been generally focussed on a single modality (image or mesh), and few of them have tried to jointly consider the image and mesh representations of heart. To obtain efficient and explainable biomarkers for CVD prediction and diagnosis, it is needed to jointly consider both representations. We design a novel multi-channel variational auto-encoder, mesh-image variational auto-encoder, to learn joint representation of paired mesh and image. After training, the shape-aware image representation (SAIR) can be learned directly from the raw images and applied for further CVD prediction and diagnosis. We demonstrate our method on data from UK Biobank study and two other datasets via extensive experiments. In acute myocardial infarction prediction, SAIR achieves 81.43% accuracy, significantly higher than traditional biomarkers like metadata and clinical indices (left ventricle and right ventricle clinical indices of cardiac function like chamber volume, mass, and ejection fraction). Our mesh-image variational auto-encoder provides a novel approach for 3D cardiac mesh reconstruction from images. The extraction of SAIR is fast and without need of segmentation masks, and its focussing can be visualized in the corresponding cardiac meshes. SAIR archives better performance than traditional biomarkers and can be applied as an efficient supplement to them, which is of significant potential in CVD analysis.

Improved Risk Prediction of Acute Myocardial Infarction in Patients With Stable Coronary Artery Disease Using an Amino Acid‐Assisted Model

Improved Risk Prediction of Acute Myocardial Infarction in Patients With Stable Coronary Artery Disease Using an Amino Acid‐Assisted Model

PP90 Artificial Intelligence To Detect Ischemic Heart Disease In Non-traumatic Chest Pain At The Emergency Department – SmartHeart Study

IntroductionAn estimated 17.9 million people died from cardiovascular diseases (CVDs) in 2019, which is 32 percent of all global deaths and 85 percent were due to heart attack and stroke. Chest pain is one of the most common reasons for presenting to the emergency department (ED). It is increasingly recognized that artificial intelligence (AI) will have a significant impact on the practice of medicine in the near future and may help with diagnosis and risk stratification. We aim to estimate a diagnostic prediction of acute myocardial infarction by the development and validation of an AI model.MethodsData on 134 variables of 3,986 consecutive patients who presented to the ED with non-traumatic chest pain were included in the analysis. Using AI tools, a neural network model was developed to establish the risk of acute myocardial infarction (AMI) to achieve n=150 patients over 18 years of age attending the ED.ResultsThe mean age was 65.5 (±13.7) years and 63.6 percent were male. Most (60.1%) patients were admitted to hospital, with only 20.3 percent diagnosed at hospital discharge with ischemic heart disease (IHD). All patients were followed up for two months, and 6.3 percent were readmitted to the ED, but none presented with an episode of IHD. In the data analysis of the entire sample we obtained a probability of diagnosing IHD by the SmartHeart model (S=93.1%, E=47.3%, PPV=31.0%, and NPV=96.4%). When we analyzed the sample of patients with no history of IHD (n=104), the diagnosis accuracy was as follows (S=100%, E=77.5%, PPV=42.8%, and NPV=100%).ConclusionsOur AI model provides information to predict patients who are suffering from acute IHD. AI has been reported to outperform emergency physicians and current risk stratification tools to diagnose IHD, but has rarely been integrated into practice. This study highlights the diagnostic applicability and accuracy of this type of tool and that is why studies should be implemented to see its effectiveness in routine practice in EDs.

Abscence of high lipoprotein(a) levels is and independent predictor of acute myocardial infarction without coronary lesions

Abstract Ministry of Science. The pathophysiological mechanisms underlying Myocardial Infarction with Non-Obstructive Coronary Artery Disease (MINOCA) are still under debate. Lipoprotein (a) [Lp(a)] has proinflammatory and prothrombotic actions and has been involved in the pathogenesis of atherosclerosis. However, no previous studies have linked Lp(a) levels with the probability of developing MINOCA. Moreover, the relationship between MINOCA and the plasma levels of other proatherogenic and proinflammatory molecules such as Interleukin-18 (IL18) and proprotein convertase subtilisin/kexin type 9 has not been studied. We conducted a prospective, multicenter study involving 1042 patients with acute myocardial infarction (AMI). Seventy-six patients had no significant coronary lesions. All patients underwent plasma analysis on admission. MINOCA patients were younger (57 (47–68) vs. 61 (52–72) years; p = 0.010), more frequently female (44.7% vs. 21.0%; p &amp;lt; 0.001), and had lower rates of diabetes and of Lp(a) &amp;gt; 60 mg/dL (9.2% vs. 19.8%; p = 0.037) than those with coronary lesions; moreover, High Density Lipoprotein cholesterol (HDL-c) levels were higher in MINOCA patients. The absence of Lp(a) &amp;gt; 60 mg/dL and of diabetes were independent predictors of MINOCA, as well as female sex, high HDL-c levels, and younger age. IL-18 and PCSK9 levels were not predictors of MINOCA. During a follow-up of 5.23 (2.89, 7.37) years, the independent predictors of the primary outcome (acute ischemic events or death) in the whole sample were Lp(a) &amp;gt; 60 mg/dL, older age, low estimated Glomerular Filtration rate (eGFR), hypertension, previous heart failure (HF), coronary artery bypass graft, use of insulin, and no therapy with acetylsalicylic acid. In conclusion, in AMI patients, the absence of high Lp(a) levels, as well high HDL-c levels, were independent predictors of the inexistence of coronary artery disease. High Lp (a) levels were also an independent predictor of ischemic events or death.

Effects of vitamins K2 and D3 supplementation in patients with severe coronary artery calcification: a study protocol for a randomised controlled trial

IntroductionCoronary artery calcification (CAC) and especially progression in CAC is a strong predictor of acute myocardial infarction and cardiovascular mortality. Supplementation with vitamin K2 and D3 has been suggested to...

#6717 IS PERITONEAL DIALYSIS RELATED PERITONITIS A MAJOR RISK FACTOR FOR CARDIOVASCULAR EVENTS?

Abstract Background and Aims Cardiovascular (CV) disease remains the leading cause of death in peritoneal dialysis (PD) patients and traditional CV risk factors are unable to fully account for this high incidence. The aim of our study was to establish the incidence of CV events such as acute myocardial infarction (AMI) in the PD population and assess possible risk factors for its occurrence. Method We retrospectively studied patients on PD in our unit with a minimum 3-years dialysis vintage, between January 1st 2015 and April 30th 2022. Patients with previous CV disease were excluded. Demographic and clinical data were collected, such as traditional CV risk factors, AMI, peripheral artery disease, cerebrovascular events, as well as dialysis efficiency, hydration status obtained by Body Composition Monitor (Fresenius Medical Care) and PD-related infections before AMI or end of follow-up. Univariate analysis was performed and logistic regression was applied to access predictors of AMI. Results Of the 43 patients recruited, 53.5% were male, with a mean age of 56.4 ± 11.8 years. About 98% of patients were hypertensive, 21% were diabetic, 19% were obese, and 54% had dyslipidemia. Five (11.6%) patients had a CV event while on PD, 4 suffered AMI and 1 patient had an ischemic stroke. Patients’ characteristics are presented in Table 1. In the follow-up, peripheral arterial disease (p = 0.037), hyperphosphatemia (p = 0.035) and the cumulative number of peritonitis (p = 0.018) were associated with the occurrence of AMI. In our population we found a higher frequency of peritonitis in the year prior to the AMI (p = 0.024). In logistic regression, the cumulative number of peritonitis was a predictor of AMI (OR 4.918, CI 95%:1.093-22.132). In the mean follow-up time of 4.7 ± 1.4 years, the overall mortality was 21,7%, with CV disease accounting for 60% of the observed deaths. AMI and peritonitis were associated with higher mortality in PD patients (p = 0.016 and p = 0.009, respectively). Conclusion PD-related peritonitis was a predictor of AMI in these population. The risk is higher in the year following a peritonitis episode. This may be due to a chronic inflammatory state that persists after successful treatment of peritonitis which may predispose to enhanced CV risk. Similar to previous studies, we found that cumulative episodes of peritonitis were associated with increased mortality in PD population.

Predictive value of atherogenic index of plasma and atherogenic index of plasma combined with low-density lipoprotein cholesterol for the risk of acute myocardial infarction.

Acute myocardial infarction (AMI) is a prevalent medical condition associated with significant morbidity and mortality rates. The principal underlying factor leading to myocardial infarction is atherosclerosis, with dyslipidemia being a key risk factor. Nonetheless, relying solely on a single lipid level is insufficient for accurately predicting the onset and progression of AMI. The present investigation aims to assess established clinical indicators in China, to identify practical, precise, and effective tools for predicting AMI. The study enrolled 267 patients diagnosed with acute myocardial infarction as the experimental group, while the control group consisted of 73 hospitalized patients with normal coronary angiography. The investigators collected general clinical data and relevant laboratory test results and computed the Atherogenic Index of Plasma (AIP) for each participant. Using acute myocardial infarction status as the dependent variable and controlling for confounding factors such as smoking history, fasting plasma glucose (FPG), low-density lipoprotein cholesterol (LDL-C), blood pressure at admission, and diabetes history, the researchers conducted multivariate logistic regression analysis with AIP as an independent variable. Receiver operating characteristic (ROC) curves were employed to determine the predictive value of AIP and AIP combined with LDL-C for acute myocardial infarction. The results of the multivariate logistic regression analysis indicated that the AIP was an independent predictor of acute myocardial infarction. The optimal cut-off value for AIP to predict AMI was -0.06142, with a sensitivity of 81.3%, a specificity of 65.8%, and an area under the curve (AUC) of 0.801 (95% confidence interval [CI]: 0.743-0.859, P < 0.001). When AIP was combined with LDL-C, the best cut-off value for predicting acute myocardial infarction was 0.756107, with a sensitivity of 79%, a specificity of 74%, and an AUC of 0.819 (95% CI: 0.759-0.879, P < 0.001). The AIP is considered an autonomous determinant of risk for AMI. Utilizing the AIP index alone, as well as in conjunction with LDL-C, can serve as effective predictors of AMI.

In search of the optimal ECG input architecture for deep learning-based prediction of acute myocardial infarction

Las complicaciones respiratorias ocurren en 10-20% de pacientes en el postoperatorio inmediato y constituyen la complicación peroperatoria más frecuente. Tanto factores asociados al paciente, como la edad o la presencia de comorbilidades, y al procedimiento anestésico-quirúrgico, tales como la duración, tipo de cirugía y grado de urgencia, se encuentran asociadas a la aparición de dichas complicaciones. Desde un punto de vista fisiopatológico, la alteración de la mecánica ventilatoria en cirugía mayor, el efecto residual de las medicaciones usadas para la anestesia y otras alteraciones metabólicas, desencadenadas por el estado de enfermedad o la agresión quirúrgica, pueden jugar un papel en este proceso. La respiración tiene como objetivo oxigenar la sangre, a la vez que se elimina el CO2 producido durante el metabolismo aerobio. En el postoperatorio, la hipoxemia ocurre con frecuencia como consecuencia de alteraciones en la relación ventilación/perfusión, como las que se dan en presencia de atelectasias, que ocasionan un fenómeno de shunt; mientras que la hipercapnia refleja una reducción de la ventilación alveolar, comúnmente relacionada con hipoventilación global en el contexto de sedación o bloqueo neuromuscular residual. Asimismo, aquellas situaciones en las que exista un incremento de la relación ventilación/perfusión (espacio muerto) favorecerán la aparición de hipercapnia si no se produce un incremento del volumen/minuto suficiente como para compensar el espacio muerto generado. Además, cabe tener en cuenta que no solo las alteraciones respiratorias pueden traducirse en un incremento del espacio muerto, sino que la caída del gasto cardiaco puede favorecer la aparición de este efecto por la incapacidad de aportar un flujo sanguíneo adecuado a todas las porciones ventiladas del pulmón. Por ello, la monitorización continua ofrece la posibilidad de identificar alteraciones fisiopatológicas de manera temprana con el objetivo de reducir las complicaciones perioperatorias.Respiratory complications occur in 10-20% of patients in the immediate postoperative period and represent the most frequent perioperative complication. Patient-associated factors, such as age or comorbidities, and the anesthetic-surgical procedure, such as duration, type of surgery and degree of urgency, are associated with the appearance of these complications. From a pathophysiological point of view, the alteration of ventilatory mechanics in major surgery, the residual effect of medications used for anesthesia and other metabolic alterations, triggered by the state of disease or surgical aggression, may play a role in this process. Respiration aims to oxygenate the blood, while removing CO2 produced during aerobic metabolism. In the postoperative period, hypoxemia frequently occurs as a result of ventilation/perfusion changes, such as those that occur in the presence of atelectasis, which lead to shunt; while hypercapnia reflects a reduction in alveolar ventilation, commonly related to global hypoventilation in the context of sedation or residual neuromuscular blockade. Likewise, those situations in which there is an increase in the ventilation/perfusion ratio (dead space) will favor the appearance of hypercapnia if there is not enough compensatory increase in minute volume. In addition, it should be borne in mind that not only respiratory alterations can result in an increase in dead space, but the fall in cardiac output can favor the appearance of this effect due to the inability to provide adequate blood flow to all ventilated portions of the lung. Given the impact of complications on prognosis, early detection of these alterations through continuous monitoring can potentially improve clinical outcomes.

Increased Neck Circumference and Increased Waist-Hip Ratio: Predictive Factors of Acute Myocardial Infarction.

Background We hypothesize that neck circumference (NC) is a better predictor of acute myocardial infarction (AMI) compared to the waist-hip ratio (WHR) in patients presenting with acute coronary syndrome (ACS). The objective of this study is to investigate the association between NC and WHR with AMI and determine whether NC is a superior predictor of AMI in ACS patients compared to WHR. Methods This cross-sectional observational study was conducted in the Department of Cardiology at the Medical Teaching Institute, Lady Reading Hospital, Peshawar. The study lasted from February 20, 2018, to September 12, 2018. Patients having ACS who presented to the emergency department were enrolled via non-probability convenient sampling. Demographic data and baseline variables, including NCand WHR, were documented using a pre-designed pro forma. SPSS V.20 (IBM Corp, Armonk, NY) was used for data analysis. Continuous variables were expressed as mean ± standard deviation, while categorical variables were presented as frequencies and percentages. Chi-square tests were performed to determine the association between variables, and logistic regression models were used to measure odds ratios (ORs). Results In this study, 180 patients were included, with a mean age of 54.48±8.48 years and a male predominance of 51.5%. The results indicated a significant association between increased NCand WHRwith AMI. The chi-square values for NC and WHR were 78.26 (p≤0.001) and 43.38 (p≤0.001), respectively. As NC increased from <37 cm to >38.5 cm, the ORfor AMI increased from 0.46 to 4.51. Furthermore, the prevalence odds ratio (POR) of AMI increased by 2.185 times with an increase in WHR from 0.90. Conclusion Increased NC and increased WHR are statistically significantly associated and strong predictors of AMI in ACS patients. However, NC being more reliable, effective, and user-friendly should be the preferred measure.

Assessment of serum levels of bone morphogenetic proteins as sensitive biomarkers for early prediction of acute myocardial infarction.

Although acute myocardial infarction (AMI) is a life-threatening disease, rapid diagnosis may reduce mortality risk. Despite being highly specific for AMI, cardiac troponin I (cTnI) does not distinguish between the etiologically diverse of myocardial injury that may be due to MI or non-MI causes. Bone morphogenetic proteins (BMPs), which are important modulators for cardiac morphogenesis, are members of the transforming growth factor-beta superfamily. This work intended to evaluate the utility of 2 members of BMPs (BMP2 and BMP4) as diagnostic biomarkers for AMI. This study included 110 AMI patients and 30 healthy volunteers. AMI patients were subdivided into 39 patients without hypercholesterolemia or diabetes mellitus (AMI without HC or DM), 33 patients with hypercholesterolemia (AMI with HC), and 38 patients with diabetes mellitus (AMI with DM). BMP2 and BMP4 levels were assessed in all subjects. The results showed that serum levels of both BMPs showed significant elevation in all AMI patients compared to healthy controls. In contrast, serum BMP2 level only was significantly elevated in AMI patients with DM compared to those without HC or DM and therefore was able to discriminate between the two subgroups. Notably, the diagnostic efficacy of the two BMPs was improved when combined. In conclusion, the two BMPs are good diagnostic biomarkers for AMI. Nevertheless, BMP2 had a higher diagnostic performance than BMP4 in discriminating AMI with DM from AMI without HC or DM with AUC 0.919 (p=0.001) and the highest PPV (100%), meanwhile, BMP4’s AUC is 0.891 (p=0.001) and a PPV (85.7%).

Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation

AimsIn the present study, we aimed to evaluate the performance of machine learning (ML) models for identification of acute myocardial infarction (AMI) or death within 30 days among emergency department (ED) chest pain patients.Methods and resultsUsing data from 9519 consecutive ED chest pain patients, we created ML models based on logistic regression or artificial neural networks. Model inputs included sex, age, ECG and the first blood tests at patient presentation: High sensitivity TnT (hs-cTnT), glucose, creatinine, and hemoglobin. For a safe rule-out, the models were adapted to achieve a sensitivity > 99% and a negative predictive value (NPV) > 99.5% for 30-day AMI/death. For rule-in, we set the models to achieve a specificity > 90% and a positive predictive value (PPV) of > 70%. The models were also compared with the 0 h arm of the European Society of Cardiology algorithm (ESC 0 h); An initial hs-cTnT < 5 ng/L for rule-out and ≥ 52 ng/L for rule-in. A convolutional neural network was the best model and identified 55% of the patients for rule-out and 5.3% for rule-in, while maintaining the required sensitivity, specificity, NPV and PPV levels. ESC 0 h failed to reach these performance levels.DiscussionAn ML model based on age, sex, ECG and blood tests at ED arrival can identify six out of ten chest pain patients for safe early rule-out or rule-in with no need for serial blood tests. Future studies should attempt to improve these ML models further, e.g. by including additional input data.

Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure.

The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, f1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.

Absence of High Lipoprotein(a) Levels Is an Independent Predictor of Acute Myocardial Infarction without Coronary Lesions.

The pathophysiological mechanisms underlying Myocardial Infarction with Non-Obstructive Coronary Artery Disease (MINOCA) are still under debate. Lipoprotein (a) [Lp(a)] has proinflammatory and prothrombotic actions and has been involved in the pathogenesis of atherosclerosis. However, no previous studies have linked Lp(a) levels with the probability of developing MINOCA. Moreover, the relationship between MINOCA and the plasma levels of other proatherogenic and proinflammatory molecules such as Interleukin-18 (IL18) and proprotein convertase subtilisin/kexin type 9 (PCSK9) has not been studied. We conducted a prospective, multicenter study involving 1042 patients with acute myocardial infarction (AMI). Seventy-six patients had no significant coronary lesions. All patients underwent plasma analysis on admission. MINOCA patients were younger (57 (47-68) vs. 61 (52-72) years; p = 0.010), more frequently female (44.7% vs. 21.0%; p < 0.001), and had lower rates of diabetes and of Lp(a) > 60 mg/dL (9.2% vs. 19.8%; p = 0.037) than those with coronary lesions; moreover, High Density Lipoprotein cholesterol (HDL-c) levels were higher in MINOCA patients. The absence of Lp(a) > 60 mg/dL and of diabetes were independent predictors of MINOCA, as well as female sex, high HDL-c levels, and younger age. IL-18 and PCSK9 levels were not predictors of MINOCA. During a follow-up of 5.23 (2.89, 7.37) years, the independent predictors of the primary outcome (acute ischemic events or death) in the whole sample were Lp(a) > 60 mg/dL, older age, low estimated Glomerular Filtration rate (eGFR), hypertension, previous heart failure (HF), coronary artery bypass graft, use of insulin, and no therapy with acetylsalicylic acid. In conclusion, in AMI patients, the absence of high Lp(a) levels, as well high HDL-c levels, were independent predictors of the inexistence of coronary artery disease. High Lp (a) levels were also an independent predictor of ischemic events or death.

Investigation of potential biomarkers in prediction of acute myocardial infarction via explainable artificial intelligence

Remodeling of the left ventricle (LV) after myocardial infarction (MI) is a process of infarct enlargement. Despite the relevance of the inflammatory response and healing process in LV remodeling after MI, the mechanisms that begin and govern these processes remain unknown. Based on the important information highlighted in different studies, the current research aims to investigate potential biomarkers for left ventricular remodeling after acute MI based on the interpretation of the explainable artificial intelligence (XAI). The project research from which the public dataset was obtained was designed in an experimental type. A cohort study involving 66 patients with coronary heart disease and 34 healthy community controls provided the platelet samples for the current research, which used available omics data on those samples. For discovering significant mechanistic connections between metabolites and glycans, the metabolomics and glycomics datasets were analyzed using biostatistics/metabolomics and explainable artificial intelligence techniques. Metabolomics data of 100 patients (AMI=66; Control=34) including 75 males and 25 females were evaluated in this study. As a result of experimental omics analyses, 102 metabolite levels of the patients were obtained. When FC values were examined, creatinine and dl-pipecolic acid levels were 0.50 and 0.55-fold down-regulated and glutamine, myoinositol, and cytosine levels were 1.34, 1.33, and 1.53-fold up-regulated in the AMI group compared to the control group. Findings of metabolomics data and XAI analyses revealed that five lipid metabolites may be used as potential predictors of AMI.

Assessment of Hematological Predictors via Explainable Artificial Intelligence in the Prediction of Acute Myocardial Infarction

Assessment of Hematological Predictors via Explainable Artificial Intelligence in the Prediction of Acute Myocardial Infarction

Elevated lipoprotein (a) another significant predictor of first-onset myocardial infarction among Malaysian adults

In recent years, Lipoprotein (a) [Lp(a)] has been in the spotlight to be the new biochemical predictor for acute myocardial infarction (AMI). As Lp(a) has prothrombotic and proinflammatory properties, it was postulated to play a role in the mechanism of acute thrombosis in AMI.

Prediction of Acute Myocardial Infarctionin Asian Patients With AcuteIschemic Stroke: The CTRAN Score.

Patients with acute ischemic stroke (AIS) are susceptible to acute myocardial infarction (AMI), which would lead to a dramatic increase of in-hospital mortality. The authors established and validated an easy-used model to stratify the risk of in-hospital AMI among patients with AIS. We consecutively included patients with AIS who were admitted within 7days from symptom onset in our prospectively maintained database (NCT04487340) from January 2016 to December 2020. In the derivation cohort from 70 centers, we developed a score to predict in-hospital AMI by integrating the bedside-accessible predictors identified via multivariable logistic regression. Then in the validation cohort from 22 centers, we externally evaluated the performance of this score. Overall, 96,367 patients were included. In-hospital AMI occurred in 392 (0.41%) patients. The final model, named CTRAN, incorporated 5 predictors including the history of coronary heart disease, malignant tumor, renal insufficiency, age, and baseline National Institutes of Health Stroke Scale score. The CTRAN score was confirmed in the validation cohort using receiver operating characteristic curve, which yielded an area under the curve of 0.758 (95%CI: 0.718-0.798). The CTRAN score could be a good tool for clinicians to identify patients with AIS at high in-hospital AMI risk.