Heart Stroke Research Articles

Help‐seeking and access to care for stroke and heart attack during the COVID‐19 pandemic: A qualitative study

Help‐seeking and access to care for stroke and heart attack during the COVID‐19 pandemic: A qualitative study

Colchicine: A New Dawn for Preventing Complications After Heart Attack? Unveiling its Network Pharmacology and the COLD-MI Trial

Colchicine: A New Dawn for Preventing Complications After Heart Attack? Unveiling its Network Pharmacology and the COLD-MI Trial

Correction: An efficient stacking-based ensemble technique for early heart attack prediction

Correction: An efficient stacking-based ensemble technique for early heart attack prediction

Sleep Health among Community-Recruited Opioid-Using People Who Inject Drugs in Los Angeles, CA and Denver, CO

AbstractChronic insufficient and poor-quality sleep are linked to hypertension, diabetes, depression, heart attack, and stroke. While studies on substance use and sleep typically focus on people in or entering treatment, there is a lack of research on sleep health among community-recruited people who inject drugs (PWID). To address this literature gap, we examined factors associated with insufficient and poor-quality sleep among community-recruited PWID. We recruited and interviewed 472 active opioid-using PWID (injected within the last 30 days) in Los Angeles, CA and Denver, CO between 2021 and 2022. Participants completed computer-assisted interviews covering demographics, subsistence measures, drug use patterns, injection-related behaviors, health risks, and sleep duration and quality in the last 3 months. Descriptive statistics were used to analyze all variables for subjects with complete responses to sleep items (n = 464). Bivariate analyses determined factors associated with sleep measures using chi-square and t-tests. Collinear variables were removed, and binomial linear multivariable regression calculated risk ratios (RR) for insufficient and poor-quality sleep in the last 3 months. Participants exhibited low sleep duration (mean = 4.99, standard deviation (SD) = 2.70), with 76% reporting insufficient sleep and 62% poor-quality sleep. Bivariate analyses associated both sleep measures with drug use, high subsistence scores, violent victimization, and poor health outcomes. Multivariable analyses showed a high subsistence score predicting insufficient (RR = 1.31) and poor-quality sleep (RR = 1.69) compared to low subsistence. Poor sleep health is common among structurally vulnerable community-recruited PWID, as measured by subsistence index associated with adverse sleep outcomes. Further research on structural interventions to address sleep and subsequent health outcomes among PWID is imperative.

Akira Endo, who discovered a “penicillin” for heart attacks (1933 to 2024)

Akira Endo, who discovered a “penicillin” for heart attacks (1933 to 2024)

CPR Memory and Skill Degradation Among Non-Allied Healthcare Professionals

Cardiopulmonary Resuscitation is defined by the Mayo Clinic as a lifesaving technique that is useful in many emergencies, such as a heart attack, stroke, choking, or drowning, in which someone’s breathing and heartbeat have stopped.Under the guidelines put forth by the International Liaison Committee on Resuscitation (ILCOR) and the Emergency Cardiovascular Care Organization (ECC), the American Red Cross and American Heart Association currently require that an individual’s CPR certification be renewed every two years. However, data has suggested that non-allied healthcare providers, such as school-based personnel, may require more regular training to maintain required CPR proficiency levels. Four hundred non-allied healthcare professionals from a school district in central Georgia were CPR-certified under the American Heart Association (AHA) Heart-Saver curriculum. Participants included 153 males and 247 females from elementary, middle, and high school settings. Participants included administrators, teachers, coaches, and staff. Two tenured college professors from the School of Health and Human Performance were responsible for teaching and certifying study participants. Each of these professors possessed a minimum of five years of CPR certification experience with the American Heart Association. Course sizes were limited to 12-15 participants. All CPR certification courses were implemented under standardized, controlled circumstances. Following the initial certification, twenty-five percent of participants were randomly selected to be reassessed at either three, six, nine, or twelve months, respectively, to determine memory and skill degradation rates and patterns. Degradation of declarative and procedural knowledge was observed during subsequent retest sessions at each of the three-, six-, nine-, and twelve-month marks. Scores depreciated approximately 10% each three-month retesting period, to 88%, 79%, 69%, and 57%, respectively. Data suggest that after six months, participants failed to meet the AHA standards for demonstrating acceptable declarative and procedural knowledge related to CPR implementation. Due to the observed degradation in both declarative and procedural knowledge, it is apparent that a more periodic training and recertification process in CPR is required for school-based personnel. A more consistent and periodic approach to preparing school-based first responders may prove beneficial in maintaining CPR proficiency rates, which very well could save lives.

Comparison of liberal versus restrictive transfusion strategies after hip surgery in patients with coronary artery disease: a post hoc analysis of the FOCUS trial

BackgroundThere are no clear recommendations for optimal transfusion thresholds for patients with coronary artery disease who undergo noncardiac surgery. By comparing restrictive and liberal transfusion strategies for coronary artery disease combined with hip surgery, this study hopes to provide recommendations for transfusion strategies in this special population.MethodsA total of 805 patients from the FOCUS trial (Transfusion Trigger Trial for Functional Outcomes in Cardiovascular Patients Undergoing Surgical Hip Fracture Repair) with coronary artery disease combined with hip surgery were divided into two groups based on transfusion thresholds: restricted transfusion (a hemoglobin level of 8 g/deciliter) and liberal transfusion (a hemoglobin threshold of 10 g/deciliter). The primary outcome of this study was a composite endpoint including in-hospital death, myocardial infarction, unstable angina, and acute heart failure. The secondary endpoints included other in-hospital adverse events and 30- and 60-day follow-up events. Analyses were performed by intention to treat.ResultsExcept for the proportion of congestive heart failure patients, the baseline levels of the two groups were comparable. The median number of transfusion units in the liberal transfusion group was 2 units, and the median transfusion volume in the restricted transfusion group was 0 units. The primary outcome was not significantly different between the two groups (9.2% vs. 9.4%, p = 0.91). The incidence of in-hospital myocardial infarction events was lower in the liberal transfusion group than in the restricted transfusion group (3.2% vs. 6.2%) (OR = 0.51, P = 0.048). The remaining in-hospital endpoint events, except for myocardial infarction, were not significantly different between the two groups. The 30-day and 60-day endpoints of death and inability to walk independently were not significantly different between the two groups, with ORs (95% CI) of 1.00 (0.75–1.31) and 1.06 (0.80–1.41), respectively. We also found no interaction between transfusion strategies and factors such as age, sex, or multiple underlying comorbidities at the 60-day follow-up.ConclusionsThere was no significant difference in the in-hospital, 30-day or 60-day outcome endpoints between the two groups. However, this study demonstrated that a liberal transfusion strategy tends to reduce the incidence of in-hospital myocardial infarction events in patients with coronary artery disease combined with hip surgery compared to a restrictive transfusion strategy. More high-quality studies should be designed to investigate the optimal transfusion threshold in patients with coronary artery disease treated without cardiac surgery.

Post-myocardial infarction heart failure and long-term high-fat diet: Cardiac endoplasmic reticulum stress and unfolded protein response in Sprague Dawley rat model.

Myocardial infarction (MI) significantly contributes to the global mortality rate, often leading to heart failure (HF) due to left ventricular remodeling. Key factors in the pathomechanism of HF include nitrosative/oxidative stress, inflammation, and endoplasmic reticulum (ER) stress. Furthermore, while a high-fat diet (HFD) is known to exacerbate post-MI cardiac remodeling, its impact on these critical factors in the context of HF is not as well understood. This study aimed to assess the impact of post-MI HF and HFD on inflammation, nitro-oxidative stress, ER stress, and unfolded protein response (UPR). The study was performed on fragments of the left ventricle harvested from 30 male adult Sprague Dawley rats, which were divided into four groups based on diet (normal-fat vs. high-fat) and surgical procedure (sham operation vs. coronary artery ligation to induce MI). We assessed body weight, NT-proBNP levels, protein levels related to nitrosative/oxidative stress, ER stress, UPR, apoptosis, and nitric oxide synthases, through Western Blot and ELISA. HFD and MI significantly influenced body weight and NT-proBNP concentrations. HFD elevated 3-nitrotyrosine and myeloperoxidase levels and altered nitric oxide synthase levels. HFD and MI significantly affected ER stress markers and activated or inhibited UPR pathways. The study demonstrates significant impacts of post-MI HF and dietary fat content on cardiac function and stress markers in a rat model. The interaction between HFD and MI on UPR activation suggests the importance of dietary management in post-MI recovery and HF prevention.

Novel Metal-Free Nanozyme for Targeted Imaging and Inhibition of Atherosclerosis via Macrophage Autophagy Activation to Prevent Vulnerable Plaque Formation and Rupture.

Atherosclerosis is a primary cause of cardiovascular and cerebrovascular diseases, with the unpredictable rupture of vulnerable atherosclerotic plaques enriched with lipid-laden macrophages being able to lead to heart attacks and strokes. Activating macrophage autophagy presents itself as a promising strategy for preventing vulnerable plaque formation and reducing the risk of rupture. In this study, we have developed a novel metal-free nanozyme (HCN@DS) that integrates the functions of multimodal imaging-guided therapy for atherosclerosis. HCN@DS has demonstrated high macrophage-targeting abilities due to its affinity toward scavenger receptor A (SR-A), along with excellent photoacoustic and photothermal imaging capabilities for guiding the precise treatment. It combines mild photothermal effects with moderate reactive oxygen species (ROS) generation to treat atherosclerosis. This controlled approach activates autophagy in atherosclerotic macrophages, inhibiting foam cell formation by reducing the uptake of oxidized low-density lipoproteins (oxLDL) and promoting efferocytosis and cholesterol efflux in macrophages. Additionally, it prevents plaque rupture by inhibiting apoptosis and inflammation within the plaque. Therefore, this metal-free nanozyme holds great potential for reducing the risk of atherosclerosis due to its high biosafety, excellent targeting ability, dual-modality imaging capability, and appropriate modulation of autophagy.

To Harmonize or to Hinder... Do We Need Two Sets of European Guidelines in 2024?

Hypertension control remains a significant challenge globally.1 Even in Europe, a region that has produced some of the best science on managing hypertension, the control rates of hypertension is poor, ranging between ~20% and 40% for most countries.2 If this was a simple issue to solve, one could ask - what do we need to ensure that the 60% to 80% of people with hypertension who are at high risk of a stroke, heart attack and other consequences, are not left behind? The first logical response would be that the ~500,000 general practitioners (GPs) in Europe3 should be clear on what the best approach is to diagnose and manage patients with hypertension. With the workload of GPs ever increasing, this guidance should be as simple and clear as possible. In 2018 the European Society of Cardiology (ESC) and the European Society of Hypertension (ESH) collaborated to develop a unified guideline for managing hypertension in Europe. These 2018 ESC/ESH Guidelines were well received by the scientific community to judge from the >7,1004 and >2,1005 citations (Scopus) in the two journals in which it was copublished. However, more recently these organizations developed two independent guidelines in 2023 and 2024. The 2024 ESC guidelines have had the benefit of new evidence published since the 2023 ESH guidelines, which have been incorporated into the 2024 ESC guidelines. Most importantly, though, is that the real-world impact of the 2018 guidelines on improving hypertension control rates is not clear. To make new guidelines more relevant, and to make sure they are implemented, guideline developers have become much more vigilant in including GPs and end users on guideline committees and liaising closely with patients in ensuring the implementation of new guidelines are feasible and acceptable for implementation by both providers and patients. [see the full article for more].

Hybrid LSTM models-based detection of left ventricular hypertrophy in electrocardiogram signals

This research investigates various deep learning techniques to automatically classify Left Ventricular Hypertrophy (LVH) from electrocardiogram (ECG) signals. LVH frequently results from persistently high blood pressure, causing the heart pump harder and thicken the ventricular walls. It is associated with an increased risk of heart attacks, heart failure, stroke, and sudden cardiac death. The significance of this research lies in the early and precise detection of LVH, facilitating timely interventions and ultimately improving patient health. The non-invasive nature of ECG monitoring, integrated with the efficiency of deep learning models, contributes to faster and more accessible to enhance diagnostic accuracy and efficiency in identifying LVH. The objective of this research is to assess and compare the performance of GRU3Net, Double-Bilayer LSTM, and Conv2LSTM, Dual-LSTM models in the classification of Left Ventricular Hypertrophy (LVH) based on electrocardiogram (ECG) signals, utilizing a dataset sourced from the PTB Diagnostic ECG Database. The implemented deep learning models yielded noteworthy results. Specifically, the GRU3Net model achieved a high accuracy of 96.1%, showcasing an optimal configuration for overall accuracy. The Double-Bilayer LSTM model followed with an accuracy of 91.7%. However, a decline in accuracy was observed in both the Dual-LSTM and Conv2LSTM models, with the former registering an accuracy of 90.8% and the latter decreasing further to 87.3%.

Intestinal fatty acid binding protein is associated with infarct size and cardiac function in acute heart failure following myocardial infarction

BackgroundIn acute heart failure (HF), reduced cardiac output, vasoconstriction and congestion may damage the intestinal mucosa and disrupt its barrier function. This could facilitate the leakage of bacterial products into...

Heart Attack Analysis and Prediction with Machine Learning Techniques

This study explores the use of machine learning algorithms to analyze and predict heart attacks, focusing on genetics, lifestyle, medical history, and biometric factors. The data was analyzed using logistic regression, support vector machines, decision trees, and random forests. Support vector machines were found to be the most effective model for predicting heart attack risk, with a high accuracy rate and low error rate. The study highlights the potential of machine learning in assisting healthcare professionals and individuals in determining heart attack risk and taking preventive measures.

What can naked mole-rats teach us about ameliorating hypoxia-related human diseases?

Ameliorating the deleterious impact of systemic or tissue-level hypoxia or ischemia is key to preventing or treating many human diseases and pathologies. Usefully, environmental hypoxia is also a common challenge in many natural habitats; animals that are native to such hypoxic niches often exhibit strategies that enable them to thrive with limited O2 availability. Studying how such species have evolved to tolerate systemic hypoxia offers a promising avenue of discovery for novel strategies to mitigate the deleterious effects of hypoxia in human diseases and pathologies. Of particular interest are naked mole-rats, which are among the most hypoxia-tolerant mammals. Naked mole-rats that tolerate severe hypoxia in a laboratory setting are also protected against clinically relevant mimics of heart attack and stroke. The mechanisms that support this tolerance are currently being elucidated but results to date suggest that metabolic rate suppression, reprogramming of metabolic pathways, and mechanisms that defend against deleterious perturbations of cellular signaling pathways all provide layers of protection. Herein, we synthesize and discuss what is known regarding adaptations to hypoxia in the naked mole-rat cardiopulmonary system and brain, as these systems comprise both the primary means of delivering O2 to tissues and the most hypoxia-sensitive organs in mammals.

Development and external validation of a novel modality for rapid recognition of aortic dissection based on peripheral pulse oximetry waveforms.

Aortic dissection (AD) is a life-threatening cardiovascular emergency that is often misdiagnosed as other chest pain conditions. Physiologically, AD may cause abnormalities in peripheral blood flow, which can be detected using pulse oximetry waveforms. This study aimed to assess the feasibility of identifying AD based on pulse oximetry waveforms and to highlight the key waveform features that play a crucial role in this diagnostic method. This prospective study employed high-risk chest pain cohorts from two emergency departments. The initial cohort was enriched with AD patients (n=258, 47% AD) for model development, while the second cohort consisted of chest pain patients awaiting angiography (n=71, 25% AD) and was used for external validation. Pulse oximetry waveforms from the four extremities were collected for each patient. After data preprocessing, a recognition model based on the random forest algorithm was trained using patients' gender, age, and waveform difference features extracted from the pulse oximetry waveforms. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). The importance of features was also assessed using Shapley Value and Gini importance. The model demonstrated strong performance in identifying AD in both the training and external validation sets. In the training set, the model achieved an area under the ROC curve of 0.979 (95% CI: 0.961-0.990), sensitivity of 0.918 (95% CI: 0.873-0.955), specificity of 0.949 (95% CI: 0.912-0.985), and accuracy of 0.933 (95% CI: 0.904-0.959). In the external validation set, the model attained an area under the ROC curve of 0.855 (95% CI: 0.720-0.965), sensitivity of 0.889 (95% CI: 0.722-1.000), specificity of 0.698 (95% CI: 0.566-0.812), and accuracy of 0.794 (95% CI: 0.672-0.878). Decision curve analysis (DCA) further showed that the model provided a substantial net benefit for identifying AD. The median mean and median variance of the four limbs' signals were the most influential features in the recognition model. This study demonstrated the feasibility and strong performance of identifying AD based on peripheral pulse oximetry waveforms in high-risk chest pain populations in the emergency setting. The findings also provided valuable insights for future human fluid dynamics simulations to elucidate the impact of AD on blood flow in greater detail.

Surface-enhanced Raman spectroscopy (SERS) for the diagnosis of acute myocardial infarction (AMI) using blood serum samples.

Acute myocardial infarction (AMI) is a serious medical condition generally known as heart attack, which is caused by the decreased or completely blocked blood flow to a part of the heart muscle. It is a significant cause of both mortality and morbidity throughout the world. Cardiac troponin-I (cTnI) is an important biomarker at different stages of AMI and is one of the most specific and widely used cardiac skeletal muscle proteins. Delays in medical treatment and inaccurate diagnosis might be the main cause of death of AMI patients. To overcome the death rate of AMI patients, early diagnosis of this disease is crucial. In the current study, surface-enhanced Raman spectroscopy (SERS) is employed for the characterization and diagnosis of this disease using blood serum samples from 49 clinically confirmed acute myocardial infarction (AMI) patients and 17 healthy persons. Silver nanoparticles (AgNPs) are used as the SERS substrate for the recognition of characteristic SERS spectral features, differentiating between healthy and AMI-positive samples. The acute myocardial infarction-positive blood serum samples reveal remarkable differences in spectral intensities at 534, 697, 744, 835, 927, 941, 988, 1221, 1303, 1403, 1481, 1541, 1588 and 1694 cm-1. For the differentiation and quantitative analysis of the SERS spectra, multivariate chemometric tools (including principal component analysis (PCA) and partial least squares regression (PLSR)) are employed. A PLSR model established on the basis of differentiating the SERS spectral features is found to be helpful in the prediction of the levels of cardiac troponin-I (cTnI) in the blood serum samples with the root mean square error of calibration (RMSEC) value of 2.98 ng mL-1 and root mean square errors of prediction (RMSEP) value of 3.98 ng mL-1 for S7.

Killip and Kimball classification in the Ultrasound era: Is it time to redefine?

Lung ultrasound is a tool that is increasingly gaining strength in the initial evaluation of the patient in the emergency department and in critical care areas, making it particularly useful for cardiologists. In patients with ST elevation and acute myocardial infarction it has been observed that 25-45% of patients are wrongly classified as Class I in the Killip and Kimball classification after lung ultrasound (subclinical congestion). The clinical relevance of this finding lies in the fact that the greater the number of B lines, the greater short- and long-term the mortality is. An important advantage is that no prolonged time for learning the technique is required. More studies are needed to evaluate the role and importance of subclinical congestion in patients with acute myocardial infarction. Unfortunately, ultrasound is not widely available in developing countries, so the physical examination will continue to play an important role in the initial evaluation of patients with acute myocardial infraction.

Improving Cardiopulmonary Resuscitation skills for layperson in cases of heart attack: a scoping review

The proficiency of trained individuals in effectively administering Cardiopulmonary Resuscitation (CPR) is paramount in mitigating the impact of out-of-hospital cardiac arrest incidents. Inadequate CPR skills among laypersons can result in missed opportunities to save lives. Therefore, it is imperative to prioritize unified efforts to enhance CPR competencies within the general populace. This scoping review aims to consolidate literature discussing the enhancement of laypeople's CPR skills using various methodologies.This scoping review employed the PRISMA methodology and encompassed an extensive search across four critical databases (Science Direct, Proquest, Pubmed, and Google Scholar) for literature published between 2013 and 2023. The search utilized the keywords "CPR Teaching," "Skill," and "Layperson." Out of 487 collected articles, 20 were deemed relevant. The findings of all relevant articles consistently indicated an improvement in laypeople's CPR skills following training.Incorporating concise CPR instructional videos and smartphone applications has shown potential for enhancing CPR knowledge and improving emergency responsiveness. Nonetheless, integrating these modern technological approaches with traditional simulation methods has demonstrated greater efficacy in CPR training, resulting in enhanced compression quality and depth.In conclusion, Integrating traditional and technology-based learning methodologies improves the capability of individuals without specialized medical training to administer CPR effectively.

Electrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate

ObjectivesElectrocardiogram (ECG) signals are beneficial for diagnosing cardiac diseases. The cardiac patients' life quality likely increases with continuous or long-period recording and monitoring of ECG signals, leading to better and early diagnosis of disease and heart attacks. However, continuous ECG recording necessitates high data rates and storage, which means high costs. Therefore, ECG compression is a handy concept that facilitates continuous monitoring of ECG signals. Deep neural networks open up new horizons for compression and also for ECG compression by providing high compression rates and quality. Although they bring constant compression ratios with better average quality, the compression quality of individual samples is not guaranteed, which may lead to misdiagnoses. This study aims to investigate the effect of compression quality on the diagnoses and to develop a deep neural network-based compression strategy that guarantees a quality-bound in return for varying compression ratios.Materials and methodsThe effect of the compression quality on the arrhythmia diagnoses is tested by comparing the performance of the deep learning-based ECG classifier on the original ECG recordings and the distorted recordings using a lossy compression algorithm with different compression error levels. Then, a compression error upper limit is calculated in terms of normalized percent root mean square difference (PRDN) error, which also coincides with the findings of the previous studies in the literature. Lastly, to enable deep learning in ECG compression, a single encoder-multi-decoder convolutional autoencoder architecture, and multiple quantization levels are proposed to guarantee a desired upper limit on the error rate.ResultsThe efficiency of the proposed method is demonstrated on a popular benchmark data set for ECG compression methods using a transfer learning approach. The PRDN error is fixed to various values, and the average compression rates are reported. An average of 13.019:1 compression is achieved for a 10% PRDN error rate, assessed as a fair quality threshold for reconstruction error. It has also been shown that the compression model has a runtime that can be run in real-time on wearable devices such as commercial smartwatches.ConclusionThis study proposes a deep learning-based ECG compression algorithm that guarantees a desired upper limit on the compression error. This model may facilitate an eHealth solution for continuous monitoring of ECG signals of individuals, especially cardiac patients.

Hubungan Pendidikan dan Level Pk Dengan Ketepatan Perawat dalam Melakukan Tindakan Resusitasi Jantung Paru (RJP) di Ruangan ICU dan IGD RS Prikasih Tahun 2024

Cardiopulmonary Resuscitation (CPR) or CPR is help for patients experiencing a heart attack where basic living rock is needed by applying pressure to the chest. Nurses have an important role in implementing cardiopulmonary resuscitation (CPR), especially in the emergency room. Skills in carrying out actions can have a direct impact on patient safety. Steps can be taken to improve the overall quality of medical treatment by increasing the education, knowledge and skills of attached nurses. The aim of this research is to find out the relationship between education and PK level with the accuracy of nurses in carrying out cardiopulmonary resuscitation measures in the ICU and emergency rooms at Prikasih Hospital in 2024. The research method that will be used is an analytical research design with a correlational design with a cross-sectional approach, a population of 40 technical respondents total sample and sample size of 40 respondents. Using the non-parametric Rank Spearman Rho test. The results show a relationship between education and the accuracy of nurses in carrying out cardiopulmonary resuscitation (CPR) in the ICU and ER with a P-Value of 0.026 < 0.05. And there is a relationship between the PK level and the accuracy of nurses in carrying out cardiopulmonary resuscitation (CPR) in the ICU and ER with a P-Value of 0.002 < 0.05. The conclusion is that education and level PK are very important in carrying out actions, because a person's knowledge can be seen from the abilities or skills they have, therefore education and level PK greatly influence the accuracy of cardiac resuscitation actions.