Strokes

Clinically interpretable prediction models of stroke functional outcomes: A national cohort study of adults in inpatient rehabilitation facilities in the US.

OBJECTIVE: To predict self-care and mobility function at discharge from inpatient rehabilitation for adults with stroke using only variables from the Inpatient Rehabilitation Facility Patient Assessment Instrument (IRF-PAI), which are mandated in the United States by the Centers for Medicare and Medicaid Services. DESIGN: Retrospective cohort study. SETTING: Inpatient rehabilitation facilities (IR...

Association of Accelerometer-Measured Light-Intensity Physical Activity with Cardiovascular Diseases: A Large Cohort Study.

Background: Light-intensity physical activity (LPA) constitutes a substantial portion of daily activities; however, it is more challenging to record and measure accurately compared to moderate- to vigorous-intensity physical activity (MVPA). Consequently, the health benefits associated with LPA are frequently overlooked. Our study aimed to investigate the associations of LPA pattern including obje...

Digital and Smart Technologies for Early Pressure Injury Detection and Prevention in Stroke and Mobility-Impaired Patients: A Scoping Review.

BACKGROUND: Pressure injuries remain a major complication among stroke and other mobility-impaired populations. Digital and smart technologies are inc...

The Pediatric Pulmonary Hypertension International Risk Score: A Prediction Model for Outcomes Using Machine Learning.

BACKGROUND: Risk prediction is fundamental to pulmonary hypertension (PH) guideline-based care, yet pediatric-specific risk prediction models remain l...

Multicenter Validation of an AI-Based CTA Tool for Anterior Circulation Large Vessel Occlusion Detection.

BACKGROUND: Rapid identification of anterior circulation large vessel occlusions (LVOs) is critical for timely mechanical thrombectomy in acute ischem...

Network Analysis-Driven Machine Learning Model for Identifying High-Cost Stroke Inpatients Using Hospital Discharge Data: Retrospective Study.

BACKGROUND: The medical burden caused by stroke is increasingly severe, and a small minority of high-cost patients consume the majority of medical exp...

Protein-protein interaction-mediated signaling networks in ischemic stroke: from molecular mechanisms to therapeutic strategies.

The pathological progression of ischemic stroke is driven by a dynamic signaling network mediated by protein-protein interactions (PPI) that involves ...

Adding Artificial Intelligence to the PERT Improves Time to Diagnosis and Treatment of Pulmonary Embolism.

OBJECTIVES: There is limited data demonstrating the benefit of artificial intelligence technology in the diagnosis and triage of pulmonary embolism. O...

Using Mobi-L RYMO to target spasticity in patient with chronic stroke: A case series study.

BACKGROUND/OBJECTIVES: Stroke represents one of the main causes of permanent disability, with persistent upper limb motor deficits in 50% of patients;...

Left Atrial Myopathy Across the Spectrum of Pediatric Heart Disease: Imaging, Mechanobiology, and Clinical Consequences.

Left atrial (LA) function has emerged as a critical determinant of cardiovascular performance and disease progression across the spectrum of pediatric...

Conversational artificial intelligence HeAlth supporT in Atrial Fibrillation Self-Management (CHAT-AF-S): rationale and randomised controlled trial design.

INTRODUCTION: Atrial fibrillation (AF), a common arrhythmia, is associated with impaired quality of life and increased stroke risk and mortality. Clin...

AI-Based Phenotyping of Atrial Fibrillation Through Generative Topographic Mapping: Prospective Murcia Atrial Fibrillation Project III Cohort Study.

BACKGROUND: The clinical heterogeneity of atrial fibrillation (AF) challenges current classifications and risk scores, limiting their real-world appli...

Prediction of Incident Atrial Fibrillation and Association With Outcomes Using Routine Electronic Health Records in a Western Pacific Population.

BACKGROUND AND AIM: Atrial fibrillation (AF) affects over 37 million people internationally and confers increased risk of cardiovascular conditions. P...

Machine Learning Classification of Prevalent Chronic Disease Using Multidimensional Social, Behavioral, and Psychological Determinants: A Cross-Sectional Analysis of the 2024 National Health Interview Survey.

BACKGROUND: Chronic diseases account for approximately 90% of the $4.5 trillion annual healthcare expenditure in the United States. While traditional ...

Machine learning-based screening tool for predicting the risk of oropharyngeal dysphagia in patients with ischemic stroke.

PURPOSE: Dysphagia frequently complicates recovery after stroke, necessitating effective predictive tools. This study aimed to develop supervised mach...

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and valida...

Deep Learning-Driven Computational Imaging for Noninvasive Monitoring System of Brain Temperature and Metabolism: A Hypothermia Validation for Acute Ischemic Stroke.

Brain temperature (BT) is a critical physiological indicator closely associated with neurological function and disease progression. However, real-time...

Beyond the guidelines: Vascular function as the central determinant and therapeutic target in hypertension.

Hypertension has traditionally been defined and managed according to brachial blood pressure levels. Although this pressure-centric strategy has signi...

A Generalized Approach to Solving Deep Learning-Based Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level Dependent Magnitude (QSM + qBOLD or QQ) for Oxygen Extraction Fraction (OEF) Mapping Across Diverse Acquisition Schemes.

PURPOSE: QQ, a recently proposed oxygen extraction fraction (OEF) mapping technique combining quantitative susceptibility mapping (QSM) and quantitati...

The FERM guild: a differentially correlated microbial module drives hypertension via metabolic flux perturbations.

UNLABELLED: Hypertension is a major risk factor for cardiovascular diseases, with changes in gut microbiota composition and function being closely ass...