Hypertension

Echocardiography-Based Machine Learning Model for Atrial Fibrillation Risk Assessment in Hypertension.

BACKGROUND: Early identification of atrial fibrillation (AF) allows for timely interventions to reduce cardiovascular complications. Risk scores including C2HEST and CHA2DS2-VASc have limitations in capturing cardiovascular remodeling. Given the association between hypertension, cardiovascular remodeling, and AF, integrating echocardiographic and demographic parameters may improve AF risk assessme...

Measuring multi-site pulse transit time with an AI-enabled mmWave radar.

Pulse Transit Time (PTT) is a measure of arterial stiffness and a physiological marker associated with cardiovascular function, with an inverse relationship to diastolic blood pressure (DBP). We present an AI-enabled mmWave system for contactless multi-site PTT measurement using a single radar. By leveraging radar beamforming and deep learning algorithms our system simultaneously measures PTT and ...

Diagnostic value of machine-learning using conventional magnetic resonance imaging markers for pediatric idiopathic intracranial hypertension: a retrospective study.

BACKGROUND: Pediatric idiopathic intracranial hypertension can be challenging to diagnose; magnetic resonance imaging (MRI) signs are considered suppo...

Integrative multi-omics and machine learning reveal glycolysis-related biomarkers driving vascular remodeling in pulmonary arterial hypertension.

Metabolic reprogramming toward aerobic glycolysis, a phenomenon analogous to the Warburg effect, is increasingly recognized as a hallmark of pulmonary...

A Mechanistic Framework Integrating Renal QSP-PK-PD and Machine Learning for Baseline-Informed Stratification of Diuretic Resistance.

Diuretic resistance represents a major source of heterogeneity in loop diuretic response and remains a key barrier to effective decongestion in heart ...

The role of nitrogen dioxide in the prevalence of adverse cardiovascular and cerebrovascular diseases in China: a national multi-pollutant geospatial analysis.

INTRODUCTION: Cardiovascular and cerebrovascular diseases (CCVDs) pose a severe global health threat, particularly among middle-aged and elderly popul...

Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI.

Delirium is a frequent and clinically consequential complication among patients admitted to the intensive care unit (ICU). Early risk stratification i...

Combined effects of cumulative triglyceride-glucose and blood pressure on stroke in middle-aged and older Chinese adults: a longitudinal analysis.

BACKGROUND: The triglyceride-glucose (TyG) index has increasingly been recognised an indicator for stroke risk. We aimed to explore the relationship b...

Machine learning based hepatic safety score predicts decompensation in hepatocellular carcinoma systemic therapy.

Hepatocellular carcinoma (HCC) frequently coexists with portal hypertension, significantly increasing the risk of hepatic decompensation (HD) and vari...

Integrative transcriptomic analysis identifies immune-associated candidate genes and altered immune cell infiltration in pulmonary arterial hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a progressive vascular disease characterized by immune dysregulation and pulmonary vascular remod...

Machine Learning Applications Within the Earlier Medicine Framework for Stroke: A Scoping Review.

This scoping review explores how machine learning (ML) has been applied to stroke research within the Earlier Medicine framework, which promotes proac...

Integrating Nutritional Status in Machine Learning Predictive Models for Cardiovascular Risk: A Pilot Study.

This study explored the use of machine learning (ML) models for cardiovascular risk stratification in an elderly Thai population. A cross-sectional an...

NATURal history of coronary PlaquE on cardiac computed tomography in individuals without MACE or lipid-lowering therapy: NATURE-CTstudy.

BACKGROUND: Coronary artery disease (CAD) progression has been examined mainly in cohorts enriched for major adverse cardiovascular events (MACE), a h...

A Simplified, Tier-Based Method for Clinical Evaluation of Diastolic Function.

Assessment of left ventricular diastolic function is inherently complex, yet it must be sufficiently simplified for consistent application in clinical...

Optimization of artificial intelligence models for prediction of new-onset cardiovascular disease in patients with arterial hypertension.

Advanced preventive strategies are needed to decrease the burden of cardiovascular disease (CVD). We aimed to develop a predictive tool to identify in...

Predictive performance of CT-based artificial intelligence for predicting variceal bleeding in portal hypertension: a systematic review and meta-analysis.

OBJECTIVES: To systematically evaluate the predictive accuracy of computed tomography (CT)-based artificial intelligence (AI) for predicting variceal ...

Extracting Signs and Symptoms of Hypertensive Disorders in Pregnancy from Clinical Notes Using Natural Language Processing.

PURPOSE: Hypertensive disorders in pregnancy (HDP) affect 16% of births in the United States. In this pilot study, we conducted a preliminary evaluati...

Alignment Between Cardiologists and AI-Driven Diagnostic Systems: Mixed Methods Study.

BACKGROUND: The clinical value of artificial intelligence (AI)-based diagnostic systems depends not only on their accuracy but also on how well their ...

Optimizing single-lead ECG axis for AI-based detection of myocardial diseases.

Wearable devices enable electrocardiograms (ECGs) outside traditional healthcare settings. While these devices are usually equipped with single-lead E...

Machine learning-based risk prediction model for postoperative acute kidney injury in surgical patients with chronic kidney disease (CKD): development, validation, and SHAP-based explainability.

This study aimed to develop machine learning models to predict postoperative acute kidney injury (AKI) in surgical patients with pre-existing chronic ...