Metabolic Syndrome

Statin adherence among patients with hypertension in Swedish primary health care: a QregPV cohort study using explainable machine learning.

AIMS: Evidence regarding statin adherence among patients with hypertension in primary care is limited. We assessed statin adherence in Swedish primary care and examined associations between patient characteristics and adherence. METHODS AND RESULTS: In this retrospective register-based cohort study from a large Swedish region, data from all primary health care centres were linked to data on comorb...

Echocardiographic assessment in arterial hypertension: a contemporary narrative review.

Arterial hypertension remains the leading modifiable cause of cardiovascular morbidity and mortality resulting in characteristic changes in cardiac structure and function. Contemporary hypertension clinical guidelines increasingly emphasise prevention and early detection of hypertension-mediated organ damage, yet they differ in how blood pressure is categorised and when further investigation is re...

Prediction of hypertension and restenosis under guideline-directed management in aortic coarctation: development and validation of machine-learning models.

BACKGROUND: Aortic coarctation (CoA) is a major cause of arterial hypertension in young individuals, with recurrence occurring in up to one-third of p...

An interpretable predictive model for depression risk in diabetic patients: A web-based application using NHANES data.

Depression is a common comorbidity in individuals with diabetes and is associated with adverse clinical outcomes. Early identification of high-risk in...

Machine learning-based early kidney stone risk classification in diabetic-hypertensive patients using obesity composite indices.

Kidney stone disease (KSD) is increasingly prevalent among patients with diabetes mellitus and hypertension. Obesity-related metabolic abnormalities m...

First-Pass Reperfusion After Endovascular Thrombectomy: A Real-World Analysis with Explainable Machine Learning for Intra-Procedural Decision Support.

OBJECTIVE: To evaluate determinants of first-pass reperfusion and to develop an explainable machine learning framework for intra-procedural decision s...

Interpretable machine learning reveals the synergistic impact of essential hypertension on early recurrence in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) patients often ential hypertension, yet how systemic vascular stress synergizes with tumor aggressiveness to driv...

Repurposing Non-oncologic Drugs via Targeted Nanocarriers for Cancer Therapy: Mechanisms, Synergistic Combinations, and Clinical Translation.

The urgent need for innovative cancer therapies has driven increasing interest in repurposing drugs originally developed for non-oncological diseases....

Maternal circulating lipid metabolic heterogeneity in preeclampsia: machine learning identifies clinically distinct subtypes.

BACKGROUND: Preeclampsia is associated with dyslipidemia. Maternal circulating lipid profiles may reflect underlying placental-metabolic interactions....

Artificial intelligence-based prediction of diseases among homeless populations in Bogotá: Implications for targeted interventions.

BACKGROUND: The homeless population in Bogotá exhibits complex social and health vulnerabilities, with a high prevalence of chronic and communicable d...

Serial OCT-based coronary physiology and plaque composition in vessels with nonobstructive coronary lesions following intensive lipid-lowering therapy: YELLOW III sub-study.

BACKGROUND AND AIMS: Non-obstructive coronary plaques are a major source of future coronary events, particularly when characterized by high lipid burd...

An Explainable AI Framework for Continuous Monitoring, Risk Stratification, and Clinical Decision Support in Primary Biliary Cholangitis: Protocol for a Multiphase Development and Validation Study.

BACKGROUND: Primary biliary cholangitis (PBC) management remains limited by reliance on static biochemical markers, fragmented assessment of symptom b...

Correlation between SHR and 28-day all-cause mortality risk in patients with spontaneous intracerebral hemorrhage.

BACKGROUND: The stress hyperglycemia ratio (SHR) has recently been suggested as a dependable indication for predicting adverse outcomes in critically ...

Right Ventricular Metrics as End Points in Clinical Trials: A Review.

IMPORTANCE: Clinical trials in cardiovascular medicine aim to deliver high-quality evidence with greater efficiency, including smaller sample sizes an...

Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.

BACKGROUND: Hypertension serves as a prevalent health issue, particularly in South Asia, where it is also a risk factor and comorbidity that affects t...

Cardiometabolic multimorbidity and atrial fibrillation: insights from traditional statistical and artificial intelligence approaches.

BACKGROUND: Cardiometabolic multimorbidity (CMM) is increasingly prevalent among patients with atrial fibrillation (AF), yet its independent impact on...

Super learner model for predicting carotid plaque regression in type 2 diabetes on PCSK9 inhibitors.

OBJECTIVES: Evidence regarding the effects of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on carotid plaque regression in patient...

Age-related metabolomic signatures and stroke susceptibility in a population-based cohort.

Aging-related metabolic dysregulation and vascular vulnerability contribute substantially to stroke susceptibility, yet subtype-specific metabolic sig...

An interpretable combinatorial data-mining framework for predicting new-onset hypertension in the general population.

We previously established an interpretable combinatorial data-mining framework to identify combinations of clinical factors predictive of heart failur...

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study.

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage increases risk of vascular events...