Prevalent Cardiovascular Conditions Research Articles

Exploring hypertension-linked diseases: a comprehensive review of innovative drug combinations with enhanced therapeutic potential.

Hypertension, a prevalent cardiovascular condition affecting a substantial portion of the global population, remains a formidable health challenge associated with a multitude of complications. This review article provides a comprehensive examination of hypertension, its various complications, and the emergence of a novel management technique that shows promising potential in transforming the therapeutic landscape. Over the years, conventional treatment approaches, encompassing lifestyle modifications, dietary interventions, and pharmacotherapy, have been the mainstay in managing hypertension. However, these strategies fall short in achieving optimal blood pressure control and preventing complications in a significant number of patients. Consequently, the medical community has ventured into exploring innovative management techniques to tackle this unmet medical need. The focal point of this review centers on the emergence of a new management technique for hypertension that exhibits promise in preclinical and clinical studies. The latest research findings shed light on the efficacy and safety of this innovative approach, which encompasses pharmaceutical agents, medical devices, and non-invasive interventions. Through critical analysis and discussion, we explore the potential impact of these novel strategies on hypertension management and patient outcomes. In conclusion, this review article provides a comprehensive overview of hypertension, its complications, and the promising emergence of innovative management techniques. By acknowledging the complexity of hypertension and the potential of new therapeutic avenues, we aspire to pave the way for improved patient care, enhanced quality of life, and ultimately, the mitigation of hypertension-related morbidity and mortality.

Heart failure with preserved ejection fraction and atrial fibrillation: epidemiology, pathophysiology, and diagnosis interplay.

Heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF) are increasingly prevalent cardiovascular conditions, particularly among the elderly population. These two conditions share common risk factors and often coexist, leading to a complex interplay that alters the clinical course of each other. The pathophysiology of HFpEF is multifaceted and intricately linked, with atrial disease serving as a common pathophysiological pathway. Diagnosis of HFpEF in the setting of AF, and vice versa, can be challenging; thus, effective screening and diagnostic strategies are needed. Understanding the complex relationship between HFpEF and AF is crucial for optimal patient management by timely disease recognition and identification of therapeutic interventions or treatment strategies.

Urolithin B suppresses phenotypic switch in vascular smooth muscle cells induced by PDGF-BB via inhibiting the PI3K-AKT pathway.

Atherosclerosis (AS) is a prevalent cardiovascular condition, and the growth and phenotypic switch of vascular smooth muscle cells (VSMCs) play a crucial role in its development. Studies have revealed that the activation of certain transcription factors and signaling pathways can trigger these cellular changes. Consequently, targeting these pathways and pivotal molecules has emerged as a promising strategy for AS treatment. Drugs that can reverse the cellular changes in VSMCs may offer new therapeutic options for AS, marking a significant advancement. While previous research has suggested that urolithin B (Uro B) possesses anti-atherosclerotic properties, its exact mechanism remains to be fully understood, especially the effect of Uro B in VSMCs. This study discovered that Uro B can impede the proliferation and migration of VSMCs prompted by PDGF-BB, as well as their phenotypic changes, indicating that Uro B could potentially prevent AS by inhibiting the phenotypic switch of VSMCs.

Immediate Effect of Uddiyana Bandha on Heart Rate Variability in Patients with Hypertension: A Randomised Controlled Study.

Hypertension, a prevalent cardiovascular condition, is linked to autonomic nervous system dysregulation, often evidenced by reduced heart rate variability (HRV). Yoga, particularly Uddiyana Bandha, may influence autonomic control and improve HRV, thus offering potential benefits for hypertension management. This study intended to assess the impact of the regular use of Uddiyana Bandha practice on HRV among hypertensive patients. This randomised controlled study included 60 hypertensive patients aged between 40 and 60. Participants were randomised into an intervention group practicing Uddiyana Bandha (n = 30) and a control group with no intervention (n = 30). The outcome measures included time and frequency domain HRV variables, specifically the standard deviation of NN intervals (SDNN), the square root of the mean squared differences of successive NN intervals (RMSSD), high-frequency (HF) and low-frequency (LF) components in normalised units (n.u), and the LF/HF ratio. These were assessed both before and immediately after the intervention. The intervention group performed three rounds of Uddiyana Bandha under supervision, while the control group rested quietly for an equivalent duration. The Uddiyana Bandha group demonstrated significant (p < .05) improvements in HRV parameters post-intervention compared to the control group. Mean RR interval, SDNN and RMSSD increased immediately after Uddiyana Bandha practice. The HF component (n.u) increased, while the LF component (n.u) and LF/HF ratio decreased (p < .05), indicating enhanced parasympathetic activity. No significant changes were observed in the control group. Uddiyana Bandha's practice immediately has notable effects on HRV in hypertensive patients, showing better autonomic balance and cardiovascular performance. These results suggest Uddiyana Bandha as a possible non-pharmacological strategy for hypertension management. Future studies with larger sample sizes and longer durations are needed to support these results and explore long-term effects.

Prevalence and risk factors of cardiovascular health and diabetes among adults living in Lagos Island, Lagos State, Nigeria

Cardiovascular diseases and diabetes mellitus represent significant public health challenges in Nigeria. This cross-sectional study investigated the prevalence and risk factors of cardiovascular health and diabetes among adults in Lagos Island, Lagos State, Nigeria. The study included 297 participants aged 18 years and above, with data collected through structured questionnaires, physical measurements, and biochemical analyses. The findings revealed hypertension as the most prevalent cardiovascular condition (34.7%), followed by obesity (29.3%) and diabetes mellitus (26.6%). Medication adherence was notably low, with only 21.9% taking hypertension medication and 15.2% taking diabetes medication. Physical activity levels were generally insufficient, with 29.3% reporting no regular exercise. Sleep patterns showed that most participants got inadequate sleep, with 41.8% getting only 5-7 hours per night and 22.6% getting less than 5 hours. Biochemical measurements indicated borderline elevated means for various parameters, including fasting blood glucose (108.6 mg/dL) and total cholesterol (202.3 mg/dL). Knowledge levels regarding cardiovascular health and diabetes were predominantly fair to poor, although 81.8% of participants believed lifestyle changes could prevent or manage these conditions. Logistic regression analysis identified age (OR = 1.11, 95% CI: 1.05-1.17), BMI (OR = 1.15, 95% CI: 1.07-1.25), and family history (OR = 2.10, 95% CI: 1.42-3.09) as significant predictors of diabetes, while physical activity showed a protective effect (OR = 0.66, 95% CI: 0.49-0.89). The study concludes that there is a high prevalence of cardiovascular risk factors among adults in Lagos Island, coupled with poor medication adherence and limited health knowledge. These findings underscore the urgent need for comprehensive interventions focusing on health education, lifestyle modification, and improved healthcare access, particularly targeting high-risk groups such as older adults and those with family history of cardiovascular diseases or diabetes.

Effects of polyphenol-rich seed foods on lipid and inflammatory markers in patients with coronary heart disease: a systematic review.

Coronary heart disease (CHD) is a prevalent cardiovascular condition, with its incidence and mortality rates steadily rising over time, posing a significant threat to human health. Studies have indicated that polyphenols exhibit a certain degree of protective effect against coronary heart disease. However, the findings regarding the impact of polyphenol-rich seed foods on patients with CHD have yielded inconsistent results. This study investigated the effects of polyphenol-rich seed foods on blood lipids and inflammatory markers in patients with coronary heart disease. The China National Knowledge Network, China Science and Technology Journal Database, China Biomedical Literature Database, Wanfang Database, PubMed, Cochrane Library, Embase, and Web of Science were searched for articles from the self-built database until March 16, 2024. The quality of the included studies was assessed using Edition 2 of the Cochrane Randomized Trials Risk Bias Tool, and data analysis was conducted using RevMan 5.4. The study encompassed seven articles, with a total participation of 324 patients diagnosed with coronary heart disease. The study incorporated three seed foods abundant in polyphenols: Brazil nut, almond, and flaxseed. The meta-analysis findings revealed a significant reduction in triglyceride levels [MD = -20.03, 95% CI (-32.25, -17.44), p < 0.00001] among patients diagnosed with coronary heart disease who incorporated seed-based foods abundant in polyphenols into their diet regimen. Furthermore, a notable enhancement was observed in HDL cholesterol levels [MD = 3.14, 95% CI (1.55, 4.72), p = 0.0001]. Moreover, the type of intervention substance influenced the observed effects. The consumption of almonds has been demonstrated to significantly reduce total cholesterol [MD = -15.53, 95% CI (-21.97, -9.1), p < 0.00001] and LDL cholesterol [MD = -14.62, 95% CI (-20.92, -8.33), p < 0.00001] in patients diagnosed with coronary heart disease. Additionally, the incorporation of flaxseed into the diet has shown an enhanced effect on reducing C-reactive protein levels. The consumption of polyphenol-rich seed foods can moderately improve TG and HDL-C levels in patients with coronary heart disease, while incorporating flaxseed into their diet can effectively improve inflammatory markers.

Cross talk on therapeutic strategies: natriuretic peptides and inhibiting neprilysin in hypertension management.

Hypertension, a prevalent cardiovascular condition globally, remains a significant public health concern due to its association with increased cardiovascular morbidity and mortality. Despite the availability of various antihypertensive therapies, achieving optimal blood pressure control in patients remains a challenge. Valsartan/sacubitril (ARNi), marketed as Entresto by Novartis, combines valsartan, an angiotensin receptor blocker, with sacubitril, an inhibitor of neprilysin. Neprilysin is responsible for breaking down natriuretic peptides and other vasoactive substances. Inhibiting neprilysin prevents the degradation of natriuretic peptides, enhancing their beneficial effects on blood pressure regulation. Natriuretic Peptides, including atrial natriuretic peptide (ANP) and brain natriuretic peptides (BNP), play pivotal roles in regulating blood pressure and cardiovascular homeostasis by promoting vasodilation, natriuresis, and antagonizing the renin-angiotensin-aldosterone system. Therefore, this combo drug lessens sensitivity to natriuretic peptides and tackles the processes in hypertension that activate the renin-angiotensin-aldosterone system. This review provides an overview of how natriuretic peptides (NPs) contribute to blood pressure regulation for the treatment of hypertension through inhibiting neprilysin. It highlights the ARNi's dual action that works synergistically by blocking the harmful effects of angiotensin II on blood vessels while simultaneously increasing the levels of beneficial natriuretic peptides. Schematic representation of the mechanism of action of ARNi. Abbreviation: -Renin angiotensin aldosterone system (RAAS), Natriuretic peptides (NP), Atrial Natriuretic peptide (ANP), Brain natriuretic peptide (BNP), C-type natriuretic peptide (CNP), Angiotensin II (Ang II), Angiotensin receptor neprilysin inhibitor (ARNI).

Abstract 4113561: Efficacy and Outcomes of Empagliflozin in Acute Coronary Syndrome Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Sodium-glucose co-transporter 2 inhibitors (SGLT2i) have demonstrated mortality benefits in patients with heart failure (HF). Since acute coronary syndrome (ACS) is an increasingly prevalent cardiovascular condition that often leads to HF, SGLT2i might play a role in reducing mortality in these patients. Previous randomized controlled trials (RCTs) have demonstrated inconsistent efficacy of Empagliflozin, an SGLT2i, in patients with ACS. Methods: A comprehensive systematic literature search was conducted spanning the major bibliographic databases to retrieve RCTs comparing Empagliflozin to placebo in patients with ACS. Odds ratios (OR) and mean differences (MD) with 95% confidence intervals were pooled using the DerSimonian and Laird random-effects model with statistical significance set at p&lt;0.05. Results: 5 RCTs were included with 7229 ACS patients (3608 in the Empagliflozin group and 3621 in the placebo group). Empagliflozin treatment was not associated with a statistically significant difference in all-cause mortality [OR: 0.94; 95% CI: 0.76, 1.17; p=0.60], cardiovascular death [OR: 1.00; 95% CI: 0.79, 1.28; p=0.99], hospitalization for heart failure [OR: 0.86; 95% CI: 0.42, 1.74; p=0.67], serious adverse events [OR: 1.85; 95% CI: 0.29, 11.87; p=0.51], LVEF on follow-up [MD: 4.35; 95% CI: -4.52, 13.23; p=0.34], NT-ProBNP [MD: -126.02; 95% CI: -279.37, 27.32; p=0.11], estimated glomerular filtration rate [MD: 1.59; 95% CI: -1.35, 4.52; p=0.29], and HbA1c [MD: -0.17; 95% CI: -0.53, 0.20; p=0.38] compared to placebo. Conclusion: This study concludes no statistical difference in all-cause mortality, cardiovascular death, and hospitalization for heart failure with empagliflozin therapy in ACS patients compared to placebo, however, the largest study (EMPACT-MI) had a statistically significant difference in hospitalizations for HF. Further large multicentric RCTs are warranted to validate these findings.

Hypertension management: A comparative analysis of garlic, hibiscus, hawthorn, and olive leaf and a survey

Hypertension, a prevalent cardiovascular condition characterized by elevated blood pressure, poses a significant global health challenge. This paper reviews the potential of garlic, hibiscus, hawthorn, and olive leaf in reducing hypertension and improving one’s health. A survey was conducted among first year pharmacy students. The survey included 5 knowledge-based and 5 opinion-based questions, focusing on the four herbal remedies. The knowledge-based questions showed a correct answer rate of 61.1%, with the statement that garlic is generally safe to include in one’s diet, but excessive consumption can lead to adverse effects garnering a 91.1% correct response rate. 58.3% of the respondents failed to know the simple definition of hypertension. Opinion-based questions revealed a positive attitude towards herbal remedies with an average of 81% showing agreement on the potential benefits. Nearly 83% believed in consulting health professionals before integrating olive leaf extract, and about 89% agreed that garlic should be used as an approach to manage hypertension.

Evaluating the Impact of Windowing Techniques on Fourier Transform-Preprocessed Signals for Deep Learning-Based ECG Classification

(1) Background: Arrhythmias, or irregular heart rhythms, are a prevalent cardiovascular condition and are diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends greatly on the quality of signal preprocessing. This study evaluated the impact of different windowing techniques applied to Fourier transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A one-dimensional convolutional neural network was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal. (3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications.

Spectrum of Non-Obstructive Coronary Artery Disease and Its Relationship with Atrial Fibrillation.

This article aims to analyze the relationship between non-obstructive coronary artery disease (NOCAD) and atrial fibrillation (AF), exploring the underlying pathophysiological mechanisms and implications for clinical management. NOCAD and AF are prevalent cardiovascular conditions that often coexist, yet their interrelation is not well understood. NOCAD can lead to ischemic necrosis of cardiomyocytes and their replacement with fibrous tissue, sustaining focal ectopic activity in atrial myocardium. Atrial fibrillation, on the other hand, the most common sustained cardiac arrhythmia, is able to accelerate atherosclerosis and increase oxygen consumption in the myocardium, creating a mismatch between supply and demand, and thus promoting the development or worsening of coronary ischemia. Therefore, NOCAD and AF seem to be a complex interplay with one begets another.

Comprehensive macro and micro views on immune cells in ischemic heart disease.

Ischemic heart disease (IHD) is a prevalent cardiovascular condition that remains the primary cause of death due to its adverse ventricular remodelling and pathological changes in end-stage heart failure. As a complex pathologic condition, it involves intricate regulatory processes at the cellular and molecular levels. The immune system and cardiovascular system are closely interconnected, with immune cells playing a crucial role in maintaining cardiac health and influencing disease progression. Consequently, alterations in the cardiac microenvironment are influenced and controlled by various immune cells, such as macrophages, neutrophils, dendritic cells, eosinophils, and T-lymphocytes, along with the cytokines they produce. Furthermore, studies have revealed that Gata6+ pericardial cavity macrophages play a key role in regulating immune cell migration and subsequent myocardial tissue repair post IHD onset. This review outlines the role of immune cells in orchestrating inflammatory responses and facilitating myocardial repair following IHD, considering both macro and micro views. It also discusses innovative immune cell-based therapeutic strategies, offering new insights for further research on the pathophysiology of ischemic heart disease and immune cell-targeted therapy for IHD.

Causal Effect of Atrial Fibrillation on Heart Failure Risk in East Asian Ancestry: ABidirectional Mendelian Randomization Study Using Genome-wide Association Data.

Atrial fibrillation (AF) and heart failure (HF) are prevalent cardiovascular conditions in East Asia, with a complex interrelationship. The directionality of the causal impact of AF on HF risk remains uncertain. This study employs Mendelian randomization (MR) to investigate the potential causal effect of AF on HF. Utilizing summary data from genome-wide association studies (GWAS) within the Medical Research Council Integrative Epidemiology Unit open GWAS database, we analyzed 8180 AF cases and 28 612 controls, alongside 9413 HF cases and 203 040 controls, all of East Asian descent. We conducted MR analysis using the inverse variance weighted (IVW) method, complemented by various sensitivity analyses, including bidirectional MR to assess causality in the reverse direction. Genetically predicted AF was found to be causally associated with an increased risk of HF in East Asian populations (odds ratio = 1.14, 95% CI: 1.10-1.19, P <.001) as per the IVW method. These findings were consistent across multiple MR methods. Sensitivity analyses revealed no significant heterogeneity or pleiotropy. Notably, bidirectional MR analysis showed no causal effect of HF on the risk of developing AF. The MR analysis supports a unidirectional causal relationship between AF and increased HF risk in East Asian individuals. The absence of a reverse causal effect reinforces the importance of maintaining sinus rhythm to mitigate HF risk. Further research is warranted to corroborate these findings and to explore their clinical implications in depth.

Comparative Analysis of Pharmacological and Non-Pharmacological Interventions in Hypertensive Patients at Cumilla Medical College Hospital

Background: Hypertension is a prevalent cardiovascular condition requiring effective management to reduce associated morbidity and mortality. Both pharmacological and non-pharmacological interventions play crucial roles in its treatment. This study aims to compare the effectiveness of these interventions in hypertensive patients at Cumilla Medical College Hospital, Bangladesh. Objective: To conduct a comparative analysis of pharmacological and non-pharmacological interventions in hypertension management. Method: A prospective study was conducted using data from the Department of Medicine at Cumilla Medical College Hospital, Bangladesh, from January 2023 to December 2023. A total of 184 patients were included in the study. Data on pharmacological and non-pharmacological interventions were collected, and blood pressure measurements were recorded. Result: Among the 184 patients, 65% received pharmacological interventions, while 35% received non-pharmacological interventions. Of those receiving pharmacological interventions, 82% achieved blood pressure control, compared to 73% in the non-pharmacological group. The mean reduction in systolic blood pressure was 20 mmHg in the pharmacological group and 15 mmHg in the non-pharmacological group. Regarding lifestyle modifications, 45% of patients in the non-pharmacological group reported improved dietary habits, and 55% reported increased physical activity. Conclusion: Both pharmacological and non-pharmacological interventions effectively manage hypertension, with pharmacological interventions showing slightly higher blood pressure control rates. However, non-pharmacological interventions are valuable for improving lifestyle factors. A combination of both approaches may be optimal for hypertension management.

Precision Medicine in Congenital Heart Disease, Rheumatic Heart Disease, and Kawasaki Disease of Children: An Overview of Literature.

Congenital heart disease and common acquired heart diseases like Kawasaki disease and rheumatic heart disease are prevalent cardiovascular conditions in children worldwide. Despite the availability of treatment options, they continue to be significant contributors to morbidity and mortality. Advancements in early diagnosis, improvements in treatment approaches, and overcoming resistance to available treatments are crucial to reduce morbidity. Researchers have turned to precision medicine to tackle these challenges. We aimed to analyze the existing literature concerning the utilization of precision medicine in congenital heart disease, rheumatic heart disease, and Kawasaki disease. The emphasis is placed on comprehending the key themes explored in these studies and evaluating the present state of their clinical integration. The central theme of most studies revolves around the examination of genetic factors. Despite promising research outcomes, limitations in these studies indicate that the clinical implementation of precision medicine in these conditions remains a distant prospect, necessitating additional exploration and attention to confounding factors.

Alterations in the gut mycobiome with coronary artery disease severity

BackgroundCoronary artery disease (CAD) is a prevalent cardiovascular condition, and numerous studies have linked gut bacterial imbalance to CAD. However, the relationship of gut fungi, another essential component of the intestinal microbiota, with CAD remains poorly understood. MethodsIn this cross-sectional study, we analyzed fecal samples from 132 participants, split into 31 healthy controls and 101 CAD patients, further categorized into stable CAD (38), unstable angina (41), and acute myocardial infarction (22) groups. We conducted internal transcribed spacer 1 (ITS1) and 16S sequencing to examine gut fungal and bacterial communities. FindingsBased on ITS1 analyses, Ascomycota and Basidiomycota were the dominant fungal phyla in all the groups. The α diversity of gut mycobiome remained unaltered among the control group and CAD subgroups; however, the structure and composition of the mycobiota differed significantly with the progression of CAD. The abundances of 15 taxa gradually changed with the occurrence and progression of the disease and were significantly correlated with major CAD risk factor indicators. The mycobiome changes were closely linked to gut microbiome dysbiosis in patients with CAD. Furthermore, disease classifiers based on gut fungi effectively identified subgroups with different degrees of CAD. Finally, the FUNGuild analysis further categorized these fungi into distinct ecological guilds. InterpretationIn conclusion, the structure and composition of the gut fungal community differed from healthy controls to various subtypes of CAD, revealing key fungi taxa alterations linked to the onset and progression of CAD. Our study highlights the potential role of gut fungi in CAD and may facilitate the development of novel biomarkers and therapeutic targets for CAD. FundingThis work was supported by the grants from the National Natural Science Foundation of China (No. 82170302, 92168117, 82370432), National clinical key specialty construction project- Cardiovascular Surgery, 2024 Reform and Development Program of Beijing Institute of Respiratory Medicine, the Beijing Natural Science Foundation (No. 7222068); the Reform and Development Program of Beijing Institute of Respiratory Medicine (No. Ggyfz202308); and the Clinical Research Incubation Program of Beijing Chaoyang Hospital Affiliated to Capital Medical University (No. CYFH202209).

VALUE OF THE PROGNOSTIC MODEL FOR THE DIAGNOSIS OF HYPERTENSION USING AI ALGORITHMS

Objective: Hypertension is a prevalent cardiovascular condition associated with significant morbidity and mortality. While various prognostic models exist for predicting hypertension, incorporating artificial intelligence (AI) algorithms may enhance diagnostic accuracy and prognostication. However, the value of such AI-based prognostic models in hypertension diagnosis and risk stratification remains to be fully elucidated. This study aimed to evaluate the value of a prognostic model for the diagnosis of hypertension using AI algorithms by assessing biomarkers, predictors for worse prognosis, and clinical outcomes with Kaplan-Meier analysis and Cox regression models. Design and method: A retrospective cohort study was conducted on 256 smoker individulas with a mean age of 56.2 ± 12.8 years. Participants were followed up for a median duration of 6.5±2.6 years. Clinical and laboratory data, including cardiovascular biomarkers and predictors of hypertension, were collected at baseline. An AI-based prognostic model was developed using machine learning algorithms to predict hypertension diagnosis and prognosis. Kaplan-Meier analysis was performed to evaluate the cumulative incidence of hypertension, while Cox regression models were utilized to assess predictors of worse prognosis. Results: During the follow-up period, 42% of patients developed hypertension. The AI-based prognostic model demonstrated excellent diagnostic accuracy in predicting hypertension development, with 78% sensitivity and 75% specificity. Additionally, several predictors were identified for worse prognosis in hypertensive patients, including abdominal obesity, glucose intolerance, dyslipidemia. Cox regression models confirmed the independent association between high-risk classification by the prognostic model and cumulative incidence of hypertension (hazard ratio [HR], [1.15]; 95% confidence interval [CI], [1.05-1.26]; p &lt; 0.05). Conclusions: Our findings suggest that an AI-based prognostic model holds promise for the diagnosis of hypertension and risk stratification. Incorporating AI algorithms into prognostic models may enhance diagnostic accuracy and improve outcomes in patients with hypertension. Further research is warranted to validate these findings in larger cohorts and optimize the AI-based prognostic model for clinical implementation.

Association between Mitral Valve Mean Pressure Gradient and Atrial Fibrillation in Patients with Rheumatic Mitral Stenosis: A Cross-Sectional Study

Plain Language SummaryRheumatic mitral stenosis, particularly for women and young patients, is one of the most prevalent cardiovascular conditions in the world. The disease has become a global burden in recent decades. Common complications of rheumatic mitral stenosis include atrial fibrillation and stroke. Individuals with rheumatic mitral stenosis who develop AF are at increased risk of heart failure and thromboembolic events. Echocardiography is the preferred method for diagnosis, assessment of severity, and hemodynamic consequences of mitral stenosis. The mitral valve mean pressure gradient, a well-established echocardiographic parameter representing the average pressure difference between the left atrial and the left ventricle during diastole, can reflect the consequences of mitral stenosis and have a prognostic role. Individuals with high mitral valve mean pressure gradient warrant greater attention to prevent atrial fibrillation.

Heart Disease Prediction: A Machine Learning Approach with Higher Accuracy

Heart disease, a prevalent cardiovascular condition, poses significant health risks and affects millions worldwide. The alarming rise in heart disease cases in recent years demands proactive measures, making early prediction of these conditions crucial and concerning. By employing machine learning techniques, this study aims to identify patients who are more susceptible to heart disease based on diverse medical attributes. The Heart Disease Dataset from Kaggle, consisting of 1025 samples and 14 features, was incorporated into this investigation. And after preprocessing the dataset by removing duplicate and null values and implementing statistical imputation and several data graphs, like a scatter plot, box plot, histogram, etc., we split it into training and testing datasets and apply SMOTE technique on the training one. Various machinelearning approaches were used in this study, out of which the optimized decision tree gave the best accuracy of 98.96%.

Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating photoplethysmogram (PPG) and electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls, our approach focuses on continuous, non-invasive monitoring. Key features, including the QRS interval, RR interval, augmentation index, heart rate, systolic pressure, diastolic pressure, and peak-to-peak amplitude, were carefully selected for their clinical relevance and ability to capture cardiovascular dynamics. This feature selection not only highlighted important physiological indicators but also helped reduce computational complexity and the risk of overfitting in machine learning models. The use of these features in training machine learning algorithms led to a model with impressive accuracy (98%), sensitivity (97.60%), specificity (96.90%), and precision (97.20%). Our integrated approach, combining PPG and ECG signals, demonstrates superior performance compared to single-signal strategies, emphasizing its potential in early and precise heart failure diagnosis. The study also highlights the importance of continuous monitoring with wearable technology, suggesting a significant stride forward in non-invasive cardiovascular health assessment. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.