Congestive Heart Failure

Machine Learning-Based Risk Stratification Model to Guide Cardiopulmonary Exercise Training in Cardiovascular Patients.

PURPOSE: In cardiovascular patients, precise risk stratification is crucial for minimizing exercise-related risks and optimizing treatment efficacy during cardiac rehabilitation. Existing classification criteria vary among organizations, necessitating a comprehensive assessment of the underlying conditions and medical tests. We developed machine-learning models to assist in standardizing risk clas...

Development and Validation of an Interpretable Machine Learning-Based Clinical Prediction Model for Short-Term Mortality in Intracerebral Hemorrhage With Thrombocytopenia: A Multicenter Study.

BACKGROUND: Intracerebral hemorrhage (ICH) with thrombocytopenia is associated with poor outcomes, but early risk prediction tools for this subgroup are limited. We aimed to develop and externally validate an interpretable machine learning model for predicting 28-day all-cause mortality after ICU admission. METHODS: Internal data were derived from MIMIC-III/IV, eICU, and NWICU, and external valida...

Prediction model for vasospasm via snuffbox distal radial artery approach.

OBJECTIVE: To integrate demographic, preoperative ultrasonographic (anatomical and hemodynamic), and intraoperative variables for developing and valid...

Differential cardiac remodeling: patterns of cardiomyocyte morphometry in multiple patient conditions.

AIMS: Cardiac remodeling drives heart failure (HF), yet microstructural drivers in human tissue remain poorly defined due to the limited resolution of...

Educational Artificial Intelligence Software to Support Assessment of Atopic Dermatitis Severity.

Accurate assessment of the severity of atopic dermatitis is crucial for guiding treatment. However, the Eczema Area and Severity Index (EASI), a widel...

Cardiovascular risks of plastic-associated chemicals: Integrating mixture modeling and SHAP-explained machine learning to unravel linear and non-linear associations.

INTRODUCTION: Plastic-associated chemicals (PACs) are widely detected environmental contaminants, yet their cardiovascular relevance in the context of...

The Role of Circulating Polyunsaturated Fatty Acids in Atrial Fibrillation and Atrial Cardiomyopathy: A Machine Learning-Informed Structural Modelling Approach.

AIMS: Beyond their cardioprotective effects, polyunsaturated fatty acids (PUFAs) have also been linked to arrhythmogenicity, raising concern among ind...

High Resolution Isotropic 'Pseudo' 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning.

BACKGROUND: Conventional cardiovascular magnetic resonance (CMR) in pediatric and congenital heart disease uses 2D, breath-hold (BH), balanced steady ...

The role of artificial intelligence in the diagnosis of pericardial constriction.

PURPOSE OF REVIEW: Constrictive pericarditis is a complex disease whose distinction from clinical mimickers remains challenging yet critically importa...

Soft, Multi-Wavelength Photoplethysmography Enables Reliable Neonatal Blood Pressure Monitoring Via Error Stratification.

Continuous noninvasive blood pressure monitoring is important for neonatal hemodynamic management. However, cuffless photoplethysmography (PPG) is sti...

Association Between Inflammatory Biomarkers And Carotid Ultrasound Parameters In The Northern Manhattan Stroke Study.

Inflammation contributes to atherosclerosis and vascular remodeling, but the immune pathways associated with distinct carotid ultrasound phenotypes, i...

Artificial intelligence for the assessment of diastolic function.

PURPOSE OF REVIEW: Assessment of left ventricular diastolic function remains one of the most challenging aspects of echocardiography. Artificial intel...

Large-Scale Plasma Proteomics Profiles for Predicting Atrial Fibrillation Associated Stroke Risk : Type of manuscript: Original Research.

BACKGROUND: Stroke is a major complication of atrial fibrillation (AF), and risk prediction using the congestive heart failure, hypertension, age, dia...

Identifying treatment-responsive patient subgroups in a neutral clinical trial of Intensive blood pressure reduction in acute intracerebral hemorrhage: A post hoc explainable machine learning analysis.

The Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH-2) trial reported no overall benefit from intensive blood pressure (BP) reduction i...

Machine Learning in Diastolic Function: From Classification to Context-Aware Decision Support.

The Future of Imaging in Heart Failure: Toward Precision Phenotyping, Integration, and Intelligence.

PURPOSE OF REVIEW: Heart failure (HF) is increasingly understood not as a single, uniformly treated diagnosis but as a heterogeneous syndrome requirin...

[Sarcoidosis-associated uveitis: Current status and future prospects].

Sarcoidosis-related uveitis is the most common manifestation of ocular sarcoidosis. It can occur at any age but preferentially affects adults aged 30 ...

Limited impact of CT-based breast tissue composition on skin toxicity, fibrosis, edema and liponecrosis prediction after moderately hypofractionated whole-breast radiotherapy.

BACKGROUND: The study assessed the role of CT-based breast tissue composition in predicting toxicities after moderately hypofractionated whole-breast ...

Interpretable Machine Learning Model for Predicting Sepsis and Septic Shock Among Patients with Documented Fever at Emergency Department Triage Using Patients' Historical Data.

This study aimed to develop an interpretable machine learning-based scoring system for predicting sepsis and septic shock among febrile patients at em...

Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact.

Atrial fibrillation (AF) is the most prevalent sustained arrhythmia worldwide. Acute myocardial infarction (AMI) is closely intertwined with AF throug...