Heart Stroke Research Articles

Knowledge, attitudes, and beliefs regarding acute coronary syndrome in patients with type 2 diabetes: a cross-sectional study

IntroductionCardiovascular diseases are one of the leading causes of mortality and disability among individuals with diabetes. Cardiac mortality in these patients is often associated with the prevalence of silent ischemia. Increasing awareness and changing patient behaviors and attitudes can reduce disease-related mortality. Therefore, this study aimed to determine the knowledge, attitudes, and beliefs regarding acute coronary syndrome in patients with type 2 diabetes mellitus (T2DM).MethodsThis cross-sectional study was conducted on 303 diabetic patients attending the Velayat and Bou Ali hospitals in Qazvin, Iran, in 2024. Inclusion criteria included a willingness to participate in the study, mental health status (based on existing records), and no history of myocardial infarction (MI). Data collection tools comprised a demographic questionnaire and the ACS Response questionnaire. Data analysis was performed using descriptive and analytical statistics, with a significance level of <0.05 for all tests.ResultsThe mean (standard deviation) scores for knowledge, attitudes, and performance of patients with T2DM were 67.46 (6.3), 62.11 (28.2), and 97.22 (5.4), respectively. In the knowledge section, the highest percentage of correct responses was related to awareness of facilities in the hospital to reduce heart attack-related damages (62.3%) and familiarity with the symptoms of arm or hand numbness (43.41%). Patients had a low attitude towards heart disease, with approximately 36.4% being somewhat or completely confident in their ability to assist others with a heart attack. Regarding performance, 45.7% of individuals believed that they should promptly go to the hospital during a heart attack. Additionally, 44.2% preferred others to transport them to the hospital during a heart attack instead of using an ambulance.ConclusionOur findings indicate that patients with T2DM lack sufficient knowledge, attitudes, and beliefs regarding the symptoms of acute coronary syndrome (ACS). Although ACS manifests through various symptoms, patients in this study primarily identified chest pain, arm pain, and shortness of breath as indicators of ACS. At the same time, over half of the participants were unaware of other symptoms. Therefore, it is essential to educate individuals about the full range of potential ACS symptoms.

Association between weight-adjusted-waist index and long-term prognostic outcomes in cardiovascular disease patients: results from the NHANES 1999–2018 study

BackgroundAs cardiovascular disease (CVD) morbidity and mortality increase yearly, this study aimed to explore the potential of the weight-adjusted-waist index (WWI) and its relation to long-term mortality in patients with CVD.MethodsThe diagnosis of CVD was based on standardized medical condition questionnaires that incorporated participants’ self-reported physician diagnoses. WWI (cm/√kg) is a continuous variable and calculated as waist circumference (WC, cm) divided by square root of body weight (kg). For analysis purposes, the participants were divided into four groups based on the quartiles (Q1 – Q4) of the WWI. The study’s primary outcome was all-cause mortality in patients with CVD, with cardiovascular mortality as the secondary outcome, and sample weights and complex survey designs were used to ensure reliable, accurate results.ResultsThe final analysis included 4,445 study participants. In the fully adjusted model, the highest quartile (WWI > 12.05 cm/√ kg) showed a higher all-cause mortality rate compared with the lowest quartile (WWI < 11.03 cm/√ kg) (HR = 1.37, 95% CI: 1.03, 1.82, P < 0.05). The risk of all-cause mortality increased with WWI and showed a linear association in patients with congestive heart failure, heart attack (P-overall < 0.05, P − nonlinear > 0.05); WWI was nonlinearly associated with the risk of all-cause mortality in patients with coronary heart disease and angina (P-overall < 0.05, P − nonlinear < 0.05). Survival curve analysis further showed that all cause and cardiovascular mortality were higher in the high WWI group (Q4) (P < 0.001). The time-dependent receiver operating characteristic (ROC) curve showed that WWI’s area under the curves (AUC) for 5- and 10-year survival rates were 0.76 and 0.792 for all-cause mortality and 0.734 and 0.757 for CVD mortality. WWI’s AUC were higher than those of body mass index (BMI) and WC (all P < 0.01).ConclusionOur findings indicate that a high WWI is positively associated with an increased risk of all-cause mortality. Additionally, the high AUC values for WWI strengthen its potential as a meaningful prognostic marker, underscoring its utility in clinical practice for assessing long-term survival risk in patients with CVD.

The Correlation Between the Type of Fluid Administration and the Incidence of Hypotension in Patients Undergoing Spinal Anesthesia at RS Wijayakusuma Purwokerto

Background: Spinal anesthesia is a type of regional anesthesia that involves injecting a local anesthetic into the subarachnoid area. The effect of sympathetic blockade in spinal anesthesia can cause dilation of blood vessels, suppress systemic vascular resistance and result in hypotension [1]. The effects of hypotension are pulmonary aspiration, cardiac arrest, respiratory depression, and loss of consciousness. A sudden drop in blood pressure can also cause a heart attack, a complication of spinal anesthesia. Method: A quantitative approach using a correlational observational design with research variables, namely the administration of fluid types with the incidence of hypotension in patients undergoing spinal anesthesia. The research was conducted in the IBS room at Wijayakusuma Hospital, Purwokerto, with samples was calculated obtained 53 respondents who were selected using a purposive sampling technique. The relationship between the two variables tested was carried out using the non-parametric chi square statistical test with the Fisher exact test Results: Among the respondents, the type of fluid administered was crystalloids in 98.1% of cases, with 70.2% of respondents not experiencing hypotension. The chi-square test yielded a p-value of 1.000, indicating no significant correlation between the type of fluid administered and the incidence of hypotension in patients undergoing spinal anesthesia. Conclusion: There is no significant correlation between the administration of crystalloid or colloid fluids and the incidence of hypotension in patients undergoing spinal anesthesia at RST Wijayakusuma Purwokerto.

Blockchain-enabled digital twin system for brain stroke prediction

A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions. Moreover, concerns around data security and privacy continue to challenge the widespread adoption of these models. To address these challenges, we developed a secure, machine learning powered digital twin application with three main objectives enhancing prediction accuracy, strengthening security, and ensuring scalability. The application achieved an accuracy of 98.28% for brain stroke prediction on the selected dataset. The data security was enhanced by integrating consortium blockchain technology with machine learning. The results show that the application is tamper-proof and is capable of detecting and automatically correcting backend data anomalies to maintain robust data protection. The application can be extended to monitor other pathologies such as heart attacks, cancers, osteoporosis, and epilepsy with minimal configuration changes.

Multimorbidity patterns and cognitive change among Mexican older adults: findings from the Mexican Health and Aging Study

Introduction: Mexico faces unique challenges due to the confluence of population ageing, an increasing burden of chronic conditions and limited resources to address these concerns. Substantial evidence links both independent and co-occurring chronic conditions to the risk of cognitive decline, but the association between common multimorbidity patterns and change in cognitive function has not been examined among older adults in Mexico. Objective: The objective of our research was to identify the most common multimorbidity patterns among ageing Mexican adults in 2012, then examine the association between these multimorbidity patterns and change in cognitive function with advancing age. Methodology: Data from the Mexican Health and Aging Study (2012–21; n = 6,082) was used to identify multimorbidity patterns in 2012 (including hypertension, diabetes, cancer, lung disease, heart attack, stroke, arthritis and depressive symptoms), then latent growth modelling was used to examine associations between the mean cognitive trajectory and the baseline multimorbidity patterns. Results: Several baseline multimorbidity patterns were associated with variation in cognitive function. Diabetes and depressive symptoms were present in the majority of multimorbidity patterns associated with lower cognitive function at baseline, and respondents with either lone diabetes or hypertension+diabetes+arthritis at baseline experienced more rapid cognitive decline than those reporting no conditions at baseline. Conclusion: Our findings suggest that prevention and management of diabetes may reduce cognitive gaps that manifest prior to older adulthood and protect against rapid cognitive decline among older Mexican adults. Further examination of depressive symptoms as a component of multimorbidity and cognitive change is also warranted.

Mental Health Conditions of Adults with Chronic Obstructive Pulmonary Disease and Comorbidities

Although the association is not well understood, people with COPD are likely to suffer from mental health issues such as depression and anxiety. Due to this occurrence, the aim of the present study is to better understand the mental health experience and COPD comorbidities of U.S. adults with a COPD diagnosis. The final sample comprised 1,676 U.S. adults who were surveyed in the 2019 National Health Interview Survey adult public file. Logistic and ordinal regression were used to calculate adjusted odds ratios for comorbid conditions predicting depression, anxiety, and the number of mental health issues, respectively. Depressive symptoms were more common in adults with COPD who also suffered from diabetes, heart disease, stroke, or asthma. Anxiety symptoms were more common in adults with COPD who also suffered from diabetes, heart disease, stroke, asthma or hypertension. For people with COPD, there was a significant difference seen between the prevalence of diabetes, heart attack, stroke, and asthma and the total number of mental health issues. These findings have implications for how mental health and primary care professionals treat COPD and COPD-related comorbidities. Additionally, they have implications for providing individualized treatments for persons living with COPD and related comorbidities.

ANALYSIS OF THE USE OF ROY'S ADAPTATION NURSING THEORY IN HEART DISEASE PATIENTS

Background: The heart as an important organ cannot be protected from diseases that can attack it. One of the deadliest diseases in the world is heart attack. Objective: This research aims to determine the behavioral response of heart disease patients which gives rise to individual adaptive responses to maintain individual adaptation in heart disease patients. Method: This research uses a qualitative method with a qualitative descriptive approach. The sampling technique is purposive sampling. Data collection methods were in-depth interviews and observation sheets from Calista Roy. Data analysis using the content analysis method. The informants in this study with heart disease in the Siloam Hospital Palembang Inpatient Room were 4 people (2 men, 2 women) and 1 Key Informant. Results: The results of this study emerged 7 themes, namely lack of knowledge about heart disease, signs and symptoms felt by patients, heart patients' efforts to treat their disease, compliance with treatment regimens, healthy diet and lifestyle, obstacles in maintaining their health, and hospital facilities and actions for heart disease patients. The research results show that there is an adaptive response from heart disease patients which can improve patient integrity and be able to carry out activities that are favorable to survival. However, it was still found that participants lacked knowledge regarding their disease, so there was still non-compliance with the treatment regimen. Conclusions: The use of Roy's adaptive nursing theory in providing nursing care to patients with heart disease makes it easier for nurses to detect the patient's situation in adapting to heart disease.

A highly sensitive creatine kinase detection in human serum using 11-mercaptoundecanoic acid modified ITO-PET electrodes.

The enzyme creatine kinase (CK) is a biomarker that plays an extremely significant role in the early detection of cardiovascular disorders. Serum levels of CK are regularly monitored in patients with heart attacks, one of the most critical cardiovascular illnesses. In this study, a highly sensitive electrochemical immunosensor system was designed for the importance of early diagnosis of CK. This immunosensor system was developed by immobilizing 11- mercaptoundecanoic acid (11-MuA) on disposable indium tin oxide-polyethylene terephthalate (ITO-PET) electrodes. Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and single frequency impedance (SFI) techniques were utilized throughout the immobilization process during the construction of the immunosensor. In addition, the proposed CK immunosensor system involves thorough analytical research, which may include linear determination range, repeatability, reproducibility, square wave voltammetry, storage capability, and regeneration. The suggested immunosensor was also characterized using scanning electron microscopy (SEM). The proposed immunosensor system demonstrated a broad dynamic range (0.1pg/mL - 100pg/mL), as well as a low limit of detection (LOD) and a low limit of quantification (LOQ) of 0.018pg/mL and 0.0394pg/mL, respectively. Finally, the immunosensor was tested on human serum samples, proving that it could be utilized in clinical situations.

Utilization of Palliative Care in Cardiogenic Shock Patients: A Retrospective Analysis of the National Inpatient Sample Database, 2020.

Background: Cardiogenic shock (CS) is one of the leading causes of death in patients with myocardial infarction, myocarditis, and congestive heart failure. The utilization patterns of specialist palliative care (PC) consultation in these patients are currently unknown. Objectives: To determine the utilization of PC in patients with CS and the overall comorbidities of that population. Methods: Review of the 2020 National Inpatient Sample identified 6,471,165 hospitalizations of which 38,531 patients were hospitalized with CS via International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10 CM) code R57.0. Demographics and details of hospitalization were compared for patients who received PC evaluation (N = 8457) and those who did not (N = 30,074) as identified via ICD-10 CM code Z51.5. Results: Patients who received PC evaluation were older (≥65 years: 69.01% vs. 55.04%, p < 0.001), had shorter hospital stays (<14 days: 78.92% vs. 70.35% patients, p < 0.001), and higher in-hospital mortality (65.80% vs. 24.23%, p < 0.001) with higher Charlson Comorbidity Index (≥4, 55.22% vs. 48.09%, p < 0.001). Furthermore, the patients who received PC had significantly higher odds of death than those who did not (adjusted odds ratio = 6, p < 0.0001). Conclusion: Despite high mortality rates, specialist PC is not routinely involved in the care of those who die with CS, although does appear to be utilized among those most likely to die. This suggests preferential utilization of specialist PC for terminal patients; however, further research will be helpful to better understand current consult practices and increase PC utilization for this highly morbid population.

Harnessing Statistical Analysis and Machine Learning Optimization for Heart Attack Prediction

Artificial intelligence (AI) and the decisions derived from data are pivotal arms in almost any field nowadays; in consequence, statistical analysis and machine learning (ML) have been used in biomedical research, especially in the early diagnosis of complicated disorders such as heart diseases. Early-stage prediction of cardiovascular disease can guide physicians towards better and early treatment and improved outcomes. Here, we utilize ML tools for the enhancement of clinical decision-making based on the digital metadata of the patient. The dataset comes from UCI repository and Applied methods include descriptive statistics for better understanding, correlation/covariance to tell if there is a relationship between explanatory features such as chest pain and target values, classification analysis to explore the disease and evaluate the model, Risk factor analysis to mark the most significant inputs for algorithms, clustering techniques to group patients with similar profiles, logistic regression (LR) and comparing the results after optimization using grid search method, K-means clustering, random forest (RF), ROC curve and AUC to assess the model's ability to diagnose patients. Then applying perceptron algorithm. Suitably, with simple ML algorithms, we can predict the prognosis of the disease with high accuracy

Study on the protective mechanism of Xuemaitong Capsule against acute myocardial ischemia rat based on network pharmacology and metabolomics

BackgroundXuemaitong Capsule (XMT) is a widely recognized traditional Miao medicine extensively utilized in Chinese clinical settings. Previous studies have demonstrated XMT protective effects against acute myocardial ischemia (AMI). However, the mechanism by which XMT provides protection to AMI rats is yet to be fully understood. Aim of the studyThe purpose of this study was to investigate the protective mechanism of XMT on AMI rats through network pharmacology, traditional pharmacodynamics and metabolomics. Material and methodsThe components and potential targets of XMT were identified through the application of traditional Chinese medicine system pharmacology and traditional Chinese medicine molecular mechanism bioinformatics analysis tools. We constructed herb-composition-target networks and analyzed protein–protein interaction (PPI) networks. The potential mechanism was explored by pathway enrichment analysis. Subsequently, the AMI model was constructed by ligation of the anterior descending branch of the left coronary artery, and XMT protective effects on AMI rats were evaluated by analyzing the myocardial enzyme profiles, electrocardiograms(ECG), Triphenyltetrazolium chloride(TTC) staining, and Hematoxylin-Eosin (HE) staining in AMI rats. Metabolomics based on UHPLC-Q-Exactive Orbitrap MS was used to observe the protective effect of XMT on the serum metabolic profile of AMI, and multivariate statistical analysis further revealed the differential patterns of metabolites after XMT treatment. Finally, integrated pathway analysis was carried out to reveal the biological metabolic mechanism. ResultsA total of 392 active components of XMT acted with 624 targets for treating AMI. Pathway enrichment analysis revealed that XMT could treat AMI through TNF, MAPK and PI3K-Akt signaling pathways. Further, XMT could effectively prevent ST-segment elevation in the ECG, reduce the size of myocardial infarction, decrease cardiac weight index and cardiac enzyme levels, and mitigate histological damage in the hearts of AMI rats. In addition, XMT callback 117 metabolites and four metabolic pathways, including taurine and hypotaurine metabolism, phenylalanine metabolism, pyrimidine metabolism and retinol metabolism. Through integrating network pharmacology and metabolomics, we explored the biological mechanism by which XMT treats AMI. It was speculated that the mechanism of XMT is to regulate TNF signaling, PI3K-Akt pathway and MAPK signaling pathway, and participate in cell apoptosis, oxidative stress, immune and inflammatory reaction and other biological processes. ConclusionXMT plays a protective role in AMI rats by regulating multiple metabolic biomarkers, multiple targets and pathways. Therefore, XMT may provide a potential strategy for the treatment of AMI.

Evaluating the Severity Spectrum: A Hierarchical Analysis of Complications During Hip Fracture Admission Associated With Mortality.

To determine which in-hospital complications after the operative treatment of hip fractures are associated with increased in-patient, 30-day, and 1-year mortality. Retrospective study. A single academic medical center and a Level 1 Trauma Center. All patients who were operatively treated for hip fractures (Orthopaedic Trauma Association/AO 31A, 31B and Vancouver A, B, and C periprosthetic fractures) at a single center between October, 2014, and June, 2023. Occurrence of an in-hospital complication was recorded. Cohorts were based on mortality time points (during admission, 30 days, and 1 year) and compared with patients who were alive at those time points to determine which in-hospital complications were most associated with mortality. Correlation analysis was performed between patients who died and those who were alive at each time point. A total of 3134 patients (average age of 79.6 years, range 18-104 years and 66.6% women) met inclusion for this study. The overall mortality rate during admission, 30 days, and 1 year was found to be 1.6%, 3.9%, and 11.1%, respectively. Sepsis was the complication most associated with increased in-hospital mortality (odds ratio [OR] 7.79, 95% confidence interval [CI], 3.22-18.82, P < 0.001) compared with other in-hospital complications. Compared with other in-hospital complications, stroke was the complication most associated with 30-day mortality (OR 7.95, 95% CI, 1.82-34.68, P < 0.001). Myocardial infarction was the complication most associated with 1-year mortality (OR 2.86, 95% CI, 1.21-6.77, P = 0.017) compared with other in-hospital complications. Postoperative sepsis, stroke, and myocardial infraction were the 3 complications most associated with mortality during admission, 30-day mortality, and 1-year mortality, respectively, during the operative treatment of hip fractures. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Identifying Gaps in Detection of Heart Valve Disease in Australia: A Population Survey

Untreated severe heart valve disease (HVD) is associated with premature mortality. Research shows low population awareness of when to seek assessment and low focus on cardiac auscultation by physicians in primary care settings. We studied contemporary public knowledge of HVD and assessment by their general practitioner (GP) in Australia. A total of 1,041 Australians >60 years of age participated in an online survey via the YouGov platform. The results were compared with data collected in 2020 using a similar approach. In addition, in-depth online interviews with 10 GPs were conducted. The top health concerns were cancer (29.7%), heart attack (14.6%), and Alzheimer's disease (14.3%), and only 2.4% rated HVD as a major health concern (in 2020: 1.4%; p<0.001). HVD could be explained by 17.1% of respondents, with 29.3% being aware of aortic stenosis (in 2020: 17.3%; p<0.001). The majority of Australians >60 years of age reported being socially and physically active on a regular basis. A total of 41.6% of respondents had a cardiac auscultation rarely or never performed by their GP (in 2020: 37.1%; p<0.001). Although GPs were confident with detecting the presence of a murmur, they were not confident to diagnose HVD without further investigation by a cardiologist. The knowledge and concern about HVD are low among Australians >60 years of age. Large gaps remain including the need for simple, cost-effective strategies to improve patient education to seek routine medical care, promote regular auscultation within primary care, and request echocardiography if clinical suspicion of HVD is present.

Targeted nanozyme-enabled treatment of cardiovascular diseases

Cardiovascular diseases (CVDs) pose a significant threat to human health due to the high mortality and morbidity rates. Traditional drugs often have limited efficacy due to inherent constraints, such as low bioavailability and notable side effects. As a highly regarded therapeutic strategy, nanotechnology offers new perspectives and means for treating CVDs. Nanozyme-based targeted therapeutic drugs specifically address the biological processes in areas affected by CVDs, thereby achieving precise treatment. Compared to traditional drugs, targeted nanozymes offer advantages, such as high efficiency, specificity, controllability, and fewer side effects, showing great efficacy in treating CVDs. This paper first explores the design strategies and mechanisms of nanozyme-based targeted therapy, then introduces its application in key CVDs, such as ischemic stroke, myocardial infarction, and coronary heart disease. Finally, the paper discusses the challenges of introducing targeted nanozymes into clinical applications and the future development prospects in the treatment of CVDs.

The heart attack of the Polish health service: metaphors, arguments, and emotional appeals in political debates

The heart attack of the Polish health service: metaphors, arguments, and emotional appeals in political debates

A new intensity-modulated radiation therapy with deep learning heart rate prediction framework for smart health monitoring

&lt;p&gt;This research paper monitors the patient’s health using sensor data, cloud, and big data Hadoop tools and used to predict heart attack and related results were discussed in detail. The integration of big data, and wearable sensors in pervasive computing has significantly enhanced healthcare services. This proposal focuses on developing an advanced healthcare monitoring system tailored for tracking the activities of elderly individuals. The wearable sensors are placed on humans at a right angle, left arm, right arm, and chest to collect the data. The large data are split into smaller segments using the map and reduce process of big data Hadoop tools. The intensity-modulated radiation therapy (IMRT) approach is used for the mapping phase and deep convolutional neural network (DCNN), deep belief network (DBN), and long short-term memory (LSTM) and proposed deep learning heart rate prediction (DLHRP) algorithms are used for the combiner/reduce phase. The reduction process combines similar segments of data to predict identical classes to predict the severity of human conditions. The proposed IMRT-DLHRP system has improved performance of 96.34% accuracy compared with 84.25%, 89.47%, and 91.58% compared to DCNN, DBN, and LSTM respectively, therefore proposed framework has significant improvement over existing approaches.&lt;/p&gt;

Exploring the Relationship Between Sleep Apnea, Myocardial Infarct Size, and Coronary Collaterals in Acute Myocardial Infarction: A Multidisciplinary Study.

We designed a study investigating the cardioprotective role of sleep apnea (SA) in patients with acute myocardial infarction (AMI), focusing on its association with infarct size and coronary collateral circulation. We recruited adults with AMI, who underwent Level-III SA testing during hospitalization. Delayed-enhancement cardiac magnetic resonance (CMR) imaging was performed to quantify AMI size (percent-infarcted myocardium). Rentrop Score quantified coronary collateralization (scores 0-3, higher scores indicating augmented collaterals). Group differences in Rentrop grade and infarct size were compared using the Wilcoxon Rank-Sum test and Fisher's Exact test as appropriate, with a significance threshold set at p <0.05. Among 33 adults, mean age was 54.4±11.5 and mean BMI was 28.4±5.9. 8 patients (24%) had no SA, and 25 (76%) had SA (mild n=10, moderate n=8, severe n=7). 66% (n=22) underwent CMR, and all patients had Rentrop scores. Median infarct size in the no-SA group was 22% versus 28% in the SA group (p=0.79). While we did not find statistically significant differences, moderate SA had a trend toward a smaller infarct size (median 15.5%; IQR 9.23) compared to the other groups (no SA [22.0%; 16.8,31.8], mild SA [27%; 23.8,32.5], and severe SA [34%; 31.53], p=0.12). A higher proportion of moderate SA patients had a Rentrop grade >0, with a trend toward significance (moderate SA versus other groups: 62.5% versus 28%, p=0.08). Our study did not find statistically significant differences in cardiac infarct size and the presence of coronary collaterals by sleep apnea severity among patients with AMI. However, our results are hypothesis-generating, and suggest that moderate SA may potentially offer cardioprotective benefits through enhanced coronary collaterals. These insights call for future research to explore the heterogeneity in ischemic preconditioning by SA severity and hypoxic burden to guide tailored clinical strategies for SA management in patients with AMI.

Association between ambient temperatures and cardiovascular disease: A time series analysis using emergency ambulance dispatches in Chongqing, China, 2019-2021.

Cardiovascular disease (CVD) is one of the leading causes of death globally. Yet, further research is required into the relationship between CVD and extreme environmental temperatures. This study aims to explore the association between the incidence of CVD and extreme temperatures, and also to identify susceptible subgroups within the population. We collected cardiovascular emergency ambulance dispatch (CEAD) records from Chongqing Emergency Dispatch Center in the main urban areas of Chongqing from 2019 to 2021. Then, we used distributed lag nonlinear modeling (DLNM) with a quasi-Poisson distribution to evaluate the association between extreme temperatures and CEADs. Susceptibility subgroups were identified by stratified analysis according to gender, age and initial diagnosis. Finally, the attribution analysis was used to calculate the scores and counts of CEADs caused by low and high temperatures. Compared with the optimal temperature (23°C), the cumulative lagged risk of total CEADs was increased under extreme low-temperature conditions (CRR: 1.732, 95% CI: [1.157, 2.593]), with the lagged effect lasting for 8 days. Under extreme high-temperature conditions, it decreased (CRR: 0.752, 95% CI: [0.611, 0.926]) and a protective effect was observed. Compared to the group under 60, those over 60 were more sensitive to temperature changes, showing a higher risk of disease with cold exposure (RR: 1.087, 95% CI: [1.021, 1.157]). In addition, a reduction in risk of disease was observed just one day after heat exposure. There were also gender differences in the elderly group: males showed longer lagged effects after cold exposure, while females had higher dispatch risk in cold weather and less heat adaptation in hot weather than males. Ambient temperature is significantly associated with the risk of CVD, with elderly patients, especially females, being a high-risk subgroup. Governments need to formulate localized health policies that address regional climate patterns and population vulnerabilities. As one of the famous "Furnace Cities", Chongqing's measures for coping with hot environments can serve as a reference. Nonetheless, improving our understanding and preparation for cold weather is also crucial. Public warning systems should be improved, and local heating strategies for vulnerable groups should be developed to minimize the negative risk of extreme cold temperatures to the public.

Heart Attack Prediction

Cardiovascular diseases, including heart attacks, remain a leading cause of mortality globally. Early prediction and intervention play a critical role in preventing and managing such conditions. This project presents a comprehensive approach to heart attack prediction through the integration of machine learning techniques and a user-friendly graphical user interface (GUI) implemented in Python. The system leverages a dataset of relevant health parameters, including age, blood pressure, cholesterol levels, and other key factors. A machine learning model, trained on historical data, is employed to analyze and predict the likelihood of a heart attack based on these input features. The model's accuracy and efficiency are crucial to its effectiveness, and various algorithms are explored to identify the optimal solution. To enhance accessibility and usability, a GUI is developed using Python's Tkinter library. The GUI enables users to input their health parameters easily and receive an instant prediction regarding their potential risk of a heart attack. The visual representation of the prediction, along with additional informative features, aims to empower individuals to take proactive measures towards a healthier lifestyle. The project not only contributes to the field of cardiovascular health prediction but also serves as an educational tool for users to better understand the factors influencing their cardiovascular well-being. The integration of machine learning into a userfriendly GUI provides a practical and efficient solution for both healthcare professionals and individuals concerned about their heart health.

Predicting Heart Attack Risk Using Machine Learning: A Review

Abstract: Machine learning in the healthcare field involves developing predictive models for early disease detection, such as analyzing patient data and identify individuals at high risk of conditions like heart attacks, diabetes, or cancer. This paper explores the use of machine learning to predict the risk of heart attacks, helping to assess and classify the likelihood of occurrence. The study highlights its potential to improve early detection and support better healthcare decisions