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Acute hyperglycemia or stress hyperglycemia is a frequent finding in patients with acute coronary syndrome (ACS). Several studies have demonstrated the association between acute hyperglycemia with short- and long-term mortality in ACS patients. But the evidence is not concrete. We gathered 1056 articles from three databases, i.e., PubMed, Google Scholar, and Science Direct using different search strategies and filters. We then removed duplicates and 919 articles were screened with title abstract and full text. After a full-text screening of 169 articles, we removed 116 articles. We then applied eligibility criteria and did a quality assessment of articles and finally, we included 21 articles in our study. The 21 articles spanned years 2014 to 2024. Of them, 16 articles were observational studies, two were systematic reviews and meta-analyses, and three were review articles. Six articles used stress hyperglycemia ratio (SHR) alone, seven articles used admission blood glucose (ABG) alone, two used fasting plasma glucose (FPG) alone and one used SHR, ABG, and FPG together as a parameter to measure acute hyperglycemia. Short-term poor outcomes (in-hospital, <30 days) were studied in 12 studies, and long-term poor outcomes (>30 days-1 year, >1 year) were studied in six studies. A positive correlation between acute hyperglycemia and short- and long-term mortality was found in our 21 included studies. The three parameters which are used to quantify acute or stress hyperglycemia in our study, i.e., SHR, ABG, and FPG predict both short- and long-term mortality in ACS patients. Further study is needed to determine the accurate cutoff level of hyperglycemia to be called acute hyperglycemia in diabetics. We tried to review the recent literature on this topic to deepen our understanding of this topic and to provide a base for future research.

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Adverse Childhood Experiences (ACEs) represent a child's exposure to negative events that are detrimental to their mental and physical health. Despite this, very few studies have focused on the relationship between ACEs and physical health problems, in non-English-speaking populations and in emerging adulthood. Therefore, the objective of this study was to investigate the cumulative and differential impact of ACEs on diverse physical health problems in a Spanish population. Participants were 648 young adults (22% men), between the ages of 18 and 30 (mean age = 21.37, SD = 3.11), who completed the ACE Questionnaire and answered some questions about their health (e.g. asthma, obesity, global health). From the cumulative perspective, ACEs had a significant relationship with global health and asthma. Additionally, the differential approach revealed some specific ACEs related to three out of five health outcomes. Therefore, early detection of ACEs is of paramount importance to reduce their impact.

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Introduction: Frequent mental distress (FMD) is poor mental health for ≥14 days in the past month. Prevalence and risk for depression and suicide are higher among US veterans (USV) than US civilians (USC). Limited research has been done among USV regarding FMD. Anyone can experience mental distress without being clinically depressed-examining FMD more broadly captures health burden of poor mental state. This study's purpose was to examine the association between having a history of heart attack (HHHA) and FMD among USV vs USC. Methods: This cross-sectional study used the 2019 Behavioral Risk Factor Surveillance System (n = 274 352) data. Weighted and adjusted logistic regression models were conducted overall and by USV/USC status. Results: HHHA increases weighted adjusted odds (WAO) of FMD. Among insured not obese USV with HHHA, the WAO of FMD were 1.4x significantly greater (P < .05) than among insured not obese USV without HHHA. Among uninsured obese USC with HHHA, the WAO of FMD were 3.2x significantly greater (P < .0001) than among uninsured obese USC without HHHA, and significantly lower among USV. Conclusions: Study findings suggest a distinction in FMD among USV/USC with HHHA. Understanding this association can inform policy for FMD screening post-heart attack as another potential intervention to prevent/reduce suicide among USV/USC.

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Heart attack is a life-threatening condition which is mostly caused due to coronary disease resulting in death in human beings. Detecting the risk of heart diseases is one of the most important problems in medical science that can be prevented and treated with early detection and appropriate medical management; it can also help to predict a large number of medical needs and reduce expenses for treatment. Predicting the occurrence of heart diseases by machine learning (ML) algorithms has become significant work in healthcare industry. This study aims to create a such system that is used for predicting whether a patient is likely to develop heart attacks, by analysing various data sources including electronic health records and clinical diagnosis reports from hospital clinics. ML is used as a process in which computers learn from data in order to make predictions about new datasets. The algorithms created for predictive data analysis are often used for commercial purposes. This paper presents an overview to forecast the likelihood of a heart attack for which many ML methodologies and techniques are applied. In order to improve medical diagnosis, the paper compares various algorithms such as Random Forest, Regression models, K-nearest neighbour imputation (KNN), Naïve Bayes algorithm etc. It is found that the Random Forest algorithm provides a better accuracy of 88.52% in forecasting heart attack risk, which could herald a revolution in the diagnosis and treatment of cardiovascular illnesses.

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Myocardial infarction (MI), frequently referred to as a heart attack, happens when the blood supply to a region of the myocardium is reduced. It might be quiet or devastating, causing hemodynamic decline and rapid death. The most common cause of MI is coronary artery disease, which is the leading cause of mortality in the United States. Prolonged lack of oxygen can lead to myocardial cell loss and necrosis. Patients may report chest pain, pressure, and electrocardiogram alterations. Management of MI relies greatly on the interprofessional team. The purpose of this study was to determine the incidence of MI in Saudi Arabia. Between 2000 and 2024, English-language papers were gathered to demonstrate the prevalence of MI in Saudi Arabia. Overall, there were four articles. Surveys and studies of national databases were the most utilized methods (n=4). We found that heart attacks are a significant health issue in Saudi Arabia, with certain lifestyle choices and medical conditions increasing the risk. Heart attacks are a major health concern in Saudi Arabia. To lower the number of heart attacks, it's important for people to make healthier lifestyle choices.

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Type 2 Diabetes (T2D) is a prevalent lifelong health condition. It is predicted that over 500 million adults will be diagnosed with T2D by 2040. T2D can develop at any age, and if it progresses, it may cause serious comorbidities. One of the most critical T2D-related comorbidities is Myocardial Infarction (MI), known as heart attack. MI is a life-threatening medical emergency, and it is important to predict it and intervene in a timely manner. The use of Machine Learning (ML) for clinical prediction is gaining pace, but the class imbalance in predictive models is a key challenge for establishing a trustworthy deployment of the technology. This may lead to bias and overfitting in the ML models, and it may cause misleading interpretations of the ML outputs. In our study, we showed how systematic use of Class Imbalance Handling (CIH) techniques may improve the performance of the ML models. We used the Connected Bradford dataset, consisting of over one million real-world health records. Three commonly used CIH techniques, Oversampling, Undersampling, and Class Weighting (CW) have been used for Naive Bayes (NB), Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), and Ensemble models. We report that CW overperforms among the other techniques with the highest Accuracy and F1 values of 0.9948 and 0.9556, respectively. Applying the most appropriate CIH techniques for the ML models using real-world healthcare data provides promising results for helping to reduce the risk of MI in patients with T2D.

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Background: One of the most complicated cardiovascular diseases in the world is heart attack. Since men are the most likely to develop cardiac diseases, accurate prediction of these conditions can help save lives in this population. This study proposed the Chi-Squared Automated Interactive Detection (CHAID) model as a prediction algorithm to forecast death versus life among men who might experience heart attacks. Methods: Data were extracted from the electronic health solution system in Jordan using a retrospective, predictive study. Between 2015 and 2021, information on men admitted to public hospitals in Jordan was gathered. Results: The CHAID algorithm had a higher accuracy of 93.72% and an area under the curve of 0.792, making it the best top model created to predict mortality among Jordanian men. It was discovered that among Jordanian men, governorates, age, pulse oximetry, medical diagnosis, pulse pressure, heart rate, systolic blood pressure, and pulse pressure were the most significant predicted risk factors of mortality from heart attack. Conclusion: With heart attack complaints as the primary risk factors that were predicted using machine learning algorithms like the CHAID model, demographic characteristics and hemodynamic readings were presented.

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INTRODUCTION: Currently the patient is defined as an older adult (OA) when the age is at least 60 years. Given the long life expectancy, it is interesting to evaluate whether all OAs with acute myocardial infarction (AMI) are equal. The objectives were to know the prevalence of OA in AMI and within them, that of those ≥75 years of age and to analyze characteristics, reperfusion treatments and in-hospital mortality according to whether they are < or ≥ 75 years of age. METHODS: OA patients admitted to the National Registry of Infarction with ST segment elevation (ARGENIAM-ST) were analyzed. They were divided into group 1: 60-74 years old and group 2: ≥ 75 years old and compared with each other. RESULTS: 3626 AM, 75.9% from Group 1, the rest from Group 2. In group 2 there were more women, hypertensive and with a history of coronary arteries. There was a similar percentage of diabetes and dyslipidemia, but fewer of smokers. In Group 2, less reperfusion treatment was used (although more primary angioplasty), with similar door-to-balloon time. Patients in Group 2 received fewer medications of proven efficacy and in the hospital course, they had more bleeding (although not major), more heart failure and more mortality: 18.3% vs. 9.4%, p<0.001. Age ≥75 years was an independent predictor of mortality. CONCLUSIONS: one in four patients with AMI is over 75 years old; they receive less reperfusion, have more heart failure, bleeding and twice the mortality rate than patients between 60 and 74 years.

Introducción: Actualmente se define al paciente como adulto mayor (AM) si su edad es al menos de 60 años. Dada la expectativa de vida prolongada resulta interesante evaluar si todos los AM con infarto agudo de miocardio (IAM) son iguales. Los objetivos fueron conocer la prevalencia de AM en el IAM y dentro de ellos, la de los ≥75 años y analizar características, tratamientos de reperfusión y mortalidad intrahospitalaria de acuerdo a si son < o ≥ 75 años. Métodos: Se analizaron los pacientes AM ingresados en el Registro Nacional de Infarto con supra desnivel del segmento ST (ARGEN-IAM-ST). Se los dividió en grupo 1: 60-74 años y grupo 2: ≥ 75 años y se compararon entre sí. Resultados: AM 3626, 75.92% del Grupo 1, el resto del Grupo 2. En el grupo 2 hubo más mujeres, hipertensos y con antecedentes coronarios. Hubo similar porcentaje de diabetes y dislipidemia, pero menos de tabaquistas. En el Grupo 2 se empleó menos tratamiento de reperfusión (aunque más angioplastia primaria), con similar tiempo puerta-balón. Los pacientes del Grupo 2 recibieron menos medicamentos de probada eficacia y en la evolución hospitalaria, más sangrado (aunque no mayor), más insuficiencia cardíaca y más mortalidad: 18.3% vs 9.4%, p<0.001. La edad ≥75 años fue predictor independiente de mortalidad. Conclusiones: Uno de cada cuatro AM con IAM tiene más de 75 años; estos pacientes reciben menos reperfusión, presentan más insuficiencia cardíaca y sangrado y tienen el doble de mortalidad que los pacientes de entre 60 y 74 años.

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Acute myocardial infarction is a silent killer for people worldwide, especially older adults who often experience atypical symptoms, causing late decision-making and a high mortality rate. The unrecognition of atypical symptoms, unconcerned about their risk, and not knowing how to deal with this critical situation are the barriers to a quick decision to visit the emergency department and delaying treatment, resulting in serious adverse outcomes. Therefore, specific and effective health education among older adults is needed. This double-blinded randomized controlled trial explored the effectiveness of health education by applying a role-play promoting decision-making ability program when expecting acute myocardial infarction occurrence among community-dwelling older adults. The participants were 96 community-dwelling older adults in central northeastern Thailand. We collected data between November 2021 and April 2022. The multi-stage sampling was applied to include participants. The intervention was the role-play promoting decision-making ability program and home visit. Outcomes were measured a week before attending and after finishing the intervention. T-tests, Mann-Whitney U test, Chi-square, and Wilcoxon Signed Rank test compared the outcomes between and within groups. Moreover, adjusted analysis was also demonstrated. Results revealed that participants who attended the program improved their knowledge, belief, and decision-making; only perceived susceptibility did not show improvement. Moreover, after demonstrating an adjusted analysis, the program participants had better knowledge about symptoms, perceived benefits, barriers, self-regulation, possible calling 1669, and first action. In conclusion, a role-play promoting decision-making ability program can promote knowledge, belief, and decision-making when expecting acute myocardial infarction occurrence among community-dwelling older adults. This study proved that role-play is one strategy to promote the program's effectiveness by inducing attention before giving older adults health information. Nurses and other healthcare professionals can implement this program as part of standard practice.Clinical Trial Registration Number: TCTR20210928004 on 28/09/2021.

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BACKGROUND: A growing proportion of people experience incomplete recovery months after contracting coronavirus disease 2019 (COVID-19). These COVID-19 survivors develop a condition known as post-COVID syndrome (PCS), where COVID-19 symptoms persist for > 12 weeks after acute infection. Limited studies have investigated PCS risk factors that notably include pre-existing cardiovascular diseases (CVD), which should be examined considering the most recent PCS data. This review aims to identify CVD as a risk factor for PCS development in COVID-19 survivors. METHODS: Following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) checklist, systematic literature searches were performed in the PubMed, Scopus, and Web of Science databases from the earliest date available to June 2023. Data from observational studies in English that described the association between CVD and PCS in adults (≥ 18 years old) were included. A minimum of two authors independently performed the screening, study selection, data extraction, data synthesis, and quality assessment (Newcastle-Ottawa Scale). The protocol of this review was registered under PROSPERO (ID: CRD42023440834). RESULTS: In total, 594 studies were screened after duplicates and non-original articles had been removed. Of the 11 included studies, CVD including hypertension (six studies), heart failure (three studies), and others (two studies) were significantly associated with PCS development with different factors considered. The included studies were of moderate to high methodological quality. CONCLUSION: Our review highlighted that COVID-19 survivors with pre-existing CVD have a significantly greater risk of developing PCS symptomology than survivors without pre-existing CVD. As heart failure, hypertension and other CVD are associated with a higher risk of developing PCS, comprehensive screening and thorough examinations are essential to minimise the impact of PCS and improve patients' disease progression.

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Background: The assessment of heavy metals' effects on human health is frequently limited to investigating one metal or a group of related metals. The effect of heavy metals mixture on heart attack is unknown. Methods: This study applied the Bayesian kernel machine regression model (BKMR) to the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data to investigate the association between heavy metal mixture exposure with heart attack. 2972 participants over the age of 20 were included in the study. Results: Results indicate that heart attack patients have higher levels of cadmium and lead in the blood and cadmium, cobalt, and tin in the urine, while having lower levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. The estimated risk of heart attack showed a negative association of 0.0030 units when all the metals were at their 25th percentile compared to their 50th percentile and a positive association of 0.0285 units when all the metals were at their 75th percentile compared to their 50th percentile. The results suggest that heavy metal exposure, especially cadmium and lead, may increase the risk of heart attacks. Conclusions: This study suggests a possible association between heavy metal mixture exposure and heart attack and, additionally, demonstrates how the BKMR model can be used to investigate new combinations of exposures in future studies.

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BACKGROUND: The COVID-19 pandemic has profoundly affected out-of-hospital cardiac arrest (OHCA) and disrupted the chain of survival. Even after the end of the pandemic, the risk of new variants and surges persists. Analyzing the characteristics of OHCA during the pandemic is important to prepare for the next pandemic and to avoid repeated negative outcomes. However, previous studies have yielded somewhat varied results, depending on the health care system or the specific characteristics of social structures. OBJECTIVE: We aimed to investigate and compare the incidence, outcomes, and characteristics of OHCA during the prepandemic and pandemic periods using data from a nationwide multicenter OHCA registry. METHODS: We conducted a multicenter, retrospective, observational study using data from the Korean Cardiac Arrest Resuscitation Consortium (KoCARC) registry. This study included adult patients with OHCA in South Korea across 3 distinct 1-year periods: the prepandemic period (from January to December 2019), early phase pandemic period (from July 2020 to June 2021), and late phase pandemic period (from July 2021 to June 2022). We extracted and contrasted the characteristics of patients with OHCA, prehospital time factors, and outcomes for the patients across these 3 periods. The primary outcomes were survival to hospital admission and survival to hospital discharge. The secondary outcome was good neurological outcome. RESULTS: From the 3 designated periods, a total of 9031 adult patients with OHCA were eligible for analysis (prepandemic: n=2728; early pandemic: n=2954; and late pandemic: n=3349). Witnessed arrest (P<.001) and arrest at home or residence (P=.001) were significantly more frequent during the pandemic period than during the prepandemic period, and automated external defibrillator use by bystanders was lower in the early phase of the pandemic than during other periods. As the pandemic advanced, the rates of the first monitored shockable rhythm (P=.10) and prehospital endotracheal intubation (P<.001) decreased significantly. Time from cardiac arrest cognition to emergency department arrival increased sequentially (prepandemic: 33 min; early pandemic: 35 min; and late pandemic: 36 min; P<.001). Both survival and neurological outcomes worsened as the pandemic progressed, with survival to discharge showing the largest statistical difference (prepandemic: 385/2728, 14.1%; early pandemic: 355/2954, 12%; and late pandemic: 392/3349, 11.7%; P=.01). Additionally, none of the outcomes differed significantly between the early and late phase pandemic periods (all P>.05). CONCLUSIONS: During the pandemic, especially amid community COVID-19 surges, the incidence of OHCA increased while survival rates and good neurological outcome at discharge decreased. Prehospital OHCA factors, which are directly related to OHCA prognosis, were adversely affected by the pandemic. Ongoing discussions are needed to maintain the chain of survival in the event of a new pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT03222999; https://classic.clinicaltrials.gov/ct2/show/NCT03222999.

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BACKGROUND AND AIMS: The pathways and metabolites that contribute to residual cardiovascular disease risks are unclear. Low-calorie sweeteners are widely used sugar substitutes in processed foods with presumed health benefits. Many low-calorie sweeteners are sugar alcohols that also are produced endogenously, albeit at levels over 1000-fold lower than observed following consumption as a sugar substitute. METHODS: Untargeted metabolomics studies were performed on overnight fasting plasma samples in a discovery cohort (n = 1157) of sequential stable subjects undergoing elective diagnostic cardiac evaluations; subsequent stable isotope dilution liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses were performed on an independent, non-overlapping validation cohort (n = 2149). Complementary isolated human platelet, platelet-rich plasma, whole blood, and animal model studies examined the effect of xylitol on platelet responsiveness and thrombus formation in vivo. Finally, an intervention study was performed to assess the effects of xylitol consumption on platelet function in healthy volunteers (n = 10). RESULTS: In initial untargeted metabolomics studies (discovery cohort), circulating levels of a polyol tentatively assigned as xylitol were associated with incident (3-year) major adverse cardiovascular event (MACE) risk. Subsequent stable isotope dilution LC-MS/MS analyses (validation cohort) specific for xylitol (and not its structural isomers) confirmed its association with incident MACE risk [third vs. first tertile adjusted hazard ratio (95% confidence interval), 1.57 (1.12-2.21), P < .01]. Complementary mechanistic studies showed xylitol-enhanced multiple indices of platelet reactivity and in vivo thrombosis formation at levels observed in fasting plasma. In interventional studies, consumption of a xylitol-sweetened drink markedly raised plasma levels and enhanced multiple functional measures of platelet responsiveness in all subjects. CONCLUSIONS: Xylitol is associated with incident MACE risk. Moreover, xylitol both enhanced platelet reactivity and thrombosis potential in vivo. Further studies examining the cardiovascular safety of xylitol are warranted.

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Background: Health promoting schools (HPS) prioritize the health of students and community. One important target of HPS is noncommunicable diseases (NCDs), including prevention of heart attacks, due to their burden on healthcare. Objective: This study assesses the effectiveness of an educational intervention to promote knowledge of signs and symptoms, beliefs and attitudes towards heart attack, and promote knowledge of Cardiopulmonary resuscitation (CPR). Methods: The intervention consisted of a 6-minute educational video between a pre-and post-survey. Among other questions, the survey included the Calgary Charter on Health literacy scale, the acute coronary syndrome response index questionnaire, and items assessing knowledge of CPR. Results: A total of 401 high school students participated (58.9% females). Few students had adequate baseline knowledge of heart attack symptoms (22%) and CPR (7%). The sample showed moderate level of health literacy (12 ± 2.7). Chest pain was the most identified symptom (95%) while abdominal pain was the least identified (14.25%). The intervention significantly increased knowledge, beliefs and attitudes towards heart attack, and knowledge of CPR (p < 0.001). Following the intervention, 83.2% of students demonstrated sufficient knowledge of heart attack symptoms, and 45% exhibited adequate knowledge of CPR. Variables predictive of better attitude, in other words higher confidence in recognizing and reacting to symptoms of heart attack, included having higher health literacy and prior knowledge of risk factors (p < 0.05). Needing help reading medical instructions sometimes predicted worse belief in their capacity to act if they experienced or witnessed a heart attack [score (p < 0.05)]. It was also predictive of worse attitude towards heart attack (OR = 0.18). Conclusion: High school students in Lebanon lack appropriate knowledge, attitudes, and beliefs toward heart attack, and lack CPR qualifications. Scale up of this educational initiative, along with training of teachers and school personnel, can be used as part of a holistic HPS program aimed at raising awareness of heart attack and first responder preparedness.

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BACKGROUND: Cardiovascular diseases represent a significant global health burden, necessitating diverse approaches for effective management. Herbal interventions have gained attention as potential adjuncts or alternatives to conventional therapies due to their perceived safety and therapeutic potential. This structured abstract provides a comprehensive review of herbal interventions for the management of CVDs, summarising key findings, mechanisms of action, and clinical implications. OBJECTIVE: This systematic review aims to evaluate the impact of various herbal interventions employed for managing cardiovascular diseases. METHOD: We conducted an extensive literature search across electronic databases, including PubMed, Scopus, and Web of Science, from inception to 2022. Studies were included if they investigated the use of herbal remedies for preventing or treating CVDs. Data extraction and synthesis focused on botanical sources, active compounds, mechanisms of action, and clinical outcomes. RESULT: Numerous herbal interventions have demonstrated promising cardiovascular benefits. A number of medicinal herbs well identified to treat CVD are Moringaoleifera, Ginseng, Ginkgo biloba, Celosia argentea, Gongronematrifolium, Gynostemmapentaphyllum, Bombaxceiba, Gentianalutea, Allium sativum, Crataegus spp, Curcuma longa, Camellia sinensis, and Zingiber officinale. Mechanistic insights reveal that herbal interventions often target multiple pathways involved in CVD pathogenesis. These mechanisms encompass anti-inflammatory, antioxidant, anti-thrombotic, anti-hypertensive, and lipid-lowering effects. Additionally, some herbs enhance endothelial function, promote nitric oxide production, and exert vasodilatory effects, contributing to improved cardiovascular health. Clinical studies have provided evidence of the efficacy of certain herbal interventions in reducing CVD risk factors and improving patient outcomes. However, more rigorous, large-scale clinical trials are needed to establish their long-term safety and effectiveness. It is crucial to consider potential herb-drug interactions and standardise dosages for reliable therapeutic outcomes. CONCLUSION: This comprehensive review highlights the potential of herbal interventions as valuable adjuncts or alternatives for managing cardiovascular diseases. Herbal remedies offer diverse mechanisms of action, targeting key CVD risk factors and pathways. While promising, their clinical utility warrants further investigation through well-designed trials to establish their safety and efficacy, paving the way for integrated approaches to cardiovascular disease management. Healthcare providers and patients should engage in informed discussions about the use of herbal interventions alongside conventional therapies in the context of CVD prevention and treatment.

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Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

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Cardiovascular disease (CVD) is a leading global cause of mortality, difficult to predict in advance. Evidence indicates that the copy number of mitochondrial DNA (mtDNAcn) in blood is altered in individuals with CVD. MtDNA released into circulation may act as a mediator of inflammation, a recognized factor in the development of CVD, in the long distance. This pilot study aims to test if levels of mtDNAcn in buffy coat DNA (BC-mtDNA), in circulating cellfree DNA (cf-mtDNA), or in DNA extracted from plasma extracellular vesicles (EV-mtDNA) are altered in CVD patients and if they can predict heart attack in advance. A group of 144 people with different CVD statuses (50 that had CVD, 94 healthy) was selected from the LifeLines Biobank according to the incidence of new cardiovascular event monitored in 6 years (50 among controls had heart attack after the basal assessment). MtDNAcn was quantified in total cf-DNA and EV-DNA from plasma as well as in buffy coat. EVs have been characterized by their size, polydispersity index, count rate, and zeta potential, by Dynamic Light Scattering. BC-mtDNAcn and cf-mtDNAcn were not different between CVD patients and healthy subjects. EVs carried higher mtDNAcn in subject with a previous history of CVD than controls, also adjusting the analysis for the EVs derived count rate. Despite mtDNAcn was not able to predict CVD in advance, the detection of increased EV-mtDNAcn in CVD patients in this pilot study suggests the need for further investigations to determine its pathophysiological role in inflammation.

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The prediction of patient survival is crucial for guiding the treatment process in healthcare. Healthcare professionals rely on analyzing patients' clinical characteristics and findings to determine treatment plans, making accurate predictions essential for efficient resource utilization and optimal patient support during recovery. In this study, a hybrid architecture combining Stacked AutoEncoders, Particle Swarm Optimization, and the Softmax Classifier was developed for predicting patient survival. The architecture was evaluated using the Haberman's Survival dataset and the Echocardiogram dataset from UCI. The results were compared with several Machine Learning methods, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Neural Networks, Gradient Boosting, and Gradient Bagging applied to the same datasets. The findings indicate that the proposed architecture outperforms other Machine Learning methods in predicting patient survival for both datasets and surpasses the results reported in the literature for the Haberman's Survival dataset. In the light of the findings obtained, the models obtained with the proposed architecture can be used as a decision support system in determining patient care and applied methods.

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BACKGROUND: Cardiovascular diseases remain a leading cause of global morbidity and mortality, with heart attacks and strokes representing significant health challenges. The accurate, early diagnosis and management of these conditions are paramount in improving patient outcomes. The specific disease, cardiovascular occlusions, has been chosen for the study due to the significant impact it has on public health. Cardiovascular diseases are a leading cause of mortality globally, and occlusions, which are blockages in the blood vessels, are a critical factor contributing to these conditions. OBJECTIVE: By focusing on cardiovascular occlusions, the study aims to leverage machine learning to improve the prediction and management of these events, potentially helping to reduce the incidence of heart attacks, strokes, and other related health issues. The use of machine learning in this context offers the promise of developing more accurate and timely interventions, thus improving patient outcomes. METHODS: We analyze diverse datasets to assess the efficacy of various machine learning algorithms in predicting heart attacks and strokes, comparing their performance to pinpoint the most accurate and reliable models. Additionally, we classify individuals by their predicted risk levels and examine key features that correlate with the incidence of cardiovascular events. The PyCaret machine learning library's Classification Module was key in developing predictive models which were evaluated with stratified cross-validation for reliable performance estimates. RESULTS: Our findings suggest that machine learning can significantly improve the prediction accuracy for heart attacks and strokes, facilitating earlier and more precise interventions. We also discuss the integration of machine learning models into clinical practice, addressing potential challenges and the need for healthcare professionals to interpret and apply these predictions effectively. CONCLUSIONS: The use of machine learning for risk stratification and the identification of modifiable factors may empower preemptive approaches to cardiovascular care, ultimately aiming to reduce the occurrence of life-threatening events and improve long-term patient health trajectories.

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This paper investigates the effects of health-care spending on mortality rates of patients who experienced a heart attack. We relate in-hospital deaths to in-hospital spending and post-discharge deaths to post-discharge health-care spending. In our analysis, we use detailed administrative data on individual personal characteristics including comorbidities, information about the type of medical treatment and information about health-care expenses at the regional level. To account for potential selectivity in the region of health-care treatment we compare local patients with visitors and stayers with recent movers from a different region. We find that in regions with higher health-care spending mortality after heart attacks is substantially lower. From this we conclude that there are long-term returns to local health-care spending.