Congestive Heart Failure

Low-Field Magnetic Resonance Imaging in Critical Care of Stroke.

Neurological deterioration is a frequent and clinically significant challenge in patients with acute stroke admitted to neurocritical care units, where timely neuroimaging is essential but access to advanced imaging may be limited by patient instability and logistical constraints. Low-field magnetic resonance imaging (LF-MRI) has recently emerged as a novel approach to extend MRI capability into c...

Machine learning algorithms for predicting arrhythmic events in Hypertrophic Cardiomyopathy: limited enhancement beyond late gadolinium enhancement.

We aimed to develop and assess the performance of a Machine learning (ML) model integrating common clinical features to predict arrhythmic events in patients with Hypertrophic Cardiomyopathy (HCM). Post-hoc analysis of an international multicenter registry of 531 HCM patients (49 years (IQR 35-61), 57% male) who underwent cardiac magnetic resonance (CMR). The dataset comprised clinical, echocardio...

Interpretable machine learning for cardiovascular disease risk prediction in cancer survivors: development and internal validation.

BACKGROUND: Cardiovascular disease (CVD) is a major concern among cancer survivors. However, the intersection of cancer and CVD has only recently gain...

Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection.

OBJECTIVE: To develop and validate a machine learning model for predicting ICU mortality in CHF patients with pulmonary infection. METHODS: Clinical d...

Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.

BACKGROUND: Pre-participation screening (PPS) in competitive athletes aims to identify cardiovascular diseases associated with sudden cardiac death (S...

An explainable real-time artificial intelligence -assisted system to reduce intradialytic hypotension.

BACKGROUND: Intradialytic hypotension (IDH) is a frequent complication in hemodialysis and is associated with adverse cardiovascular and neurological ...

Understanding HFpEF Through Imaging: Strengths, Limitations, and Current State of the Art.

Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome characterized by dyspnea caused by hemodynamic congestion, which develop...

Personalised approach to hypertension treatment: protocol for the HYPERMARKER randomised controlled trial.

INTRODUCTION: Blood pressure treatment response is variable in individual patients, and the choice of medical therapy is often dependent on clinician ...

Machine learning-driven integration of 24-hour ambulatory blood pressure and its variability.

Twenty-four hour ambulatory blood pressure (BP) monitoring (24-hour ABPM) is considered the best out-of-office BP measurement to assess hypertension. ...

Machine learning prediction of physician-assigned treatment categories from pre-procedural electronic medical record data in cardiac patients: A multiclass classification study.

Cardiovascular diseases (CVDs) remain a leading source of morbidity, mortality, and healthcare burden worldwide. In patients with coronary artery dise...

Machine Learning-Based Early Risk Stratification for Sepsis-Related Troponin-Defined Myocardial Injury Using Routine Metabolic Indicators.

BACKGROUND: Early recognition of sepsis-related myocardial injury during sepsis remains difficult, partly because harmonized echocardiographic phenoty...

Biomarkers in predicting mortality in hemodialysis patients: recent advances.

Patients undergoing maintenance hemodialysis face annual mortality rates of 15-27%, with cardiovascular causes accounting for more than half of all de...

Non-invasive arterial blood pressure waveform generation in critically ill patients: A sensor-based deep learning approach.

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of t...

Explainable machine learning for 10-year prediction of cognitive impairment in a rural Chinese population.

Cognitive impairment (CI) is an emerging public health challenge in rural aging populations, where access to formal cognitive testing is limited. Usin...

Arterial blood pressure waveform reconstruction estimation from PPG using TCN-BiLSTM.

Photoplethysmography PPG is a non-invasive and effective method for continuously measuring blood pressure BP without the use of a cuff. However, its a...

Association Between the Hemoglobin-to-Red Blood Cell Distribution Width Ratio and Perioperative Mortality Risk in Myasthenia Gravis Patients with Sepsis: A Retrospective Cohort Study.

BackgroundThis study aims to explore the association between the Hemoglobin/Red Cell Distribution Width Ratio (HRR) and perioperative mortality in mya...

Deep Learning Models Capture Umbrella Sampling-Derived Energetic Trends: A Troponin C Case Study.

Deep learning models have transformed several fields lately. In the past, capturing thermodynamic trends from free energies has relied on computationa...

Disparities in severe infection risk associated with systemic lupus erythematosus: evidence from a causal forest analysis.

OBJECTIVES: Severe infections are a primary cause of morbidity and premature mortality in patients with Systemic Lupus Erythematosus (SLE). Although S...

Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease.

Portable, scalable, and accessible artificial intelligence (AI)-enabled smartwatch technology shows promise as a cardiovascular risk stratification st...

Phenotypic patterns in feline heart failure: A natural model for understanding variable disease severity in humans.

Cats frequently develop myocardial remodeling, for example, hypertrophic cardiomyopathy, affecting 14.7% of domestic cats compared to 0.2% of humans, ...