Congestive Heart Failure

Machine learning models for predicting futile recanalisation after endovascular treatment in patients with large core infarction.

BACKGROUND: Predicting futile recanalisation following endovascular treatment (EVT) in patients with large core infarctions is crucial for guiding clinical decisions, optimising perioperative management and improving healthcare resource allocation. This study aimed to compare four machine learning (ML) algorithms and identify the most effective model for preinterventional prediction of futile reca...

Proof-of-Concept Automated Framework for Intraoperative Transesophageal Echocardiography: View Classification and Biventricular Function Assessment.

OBJECTIVE: To develop a proof-of-concept automated framework for integrated intraoperative transesophageal echocardiography (TEE) interpretation, encompassing view classification, left ventricular ejection fraction (LVEF) and right ventricular systolic function (RVSF) assessment, and tricuspid regurgitation (TR) grading. DESIGN: This cross-sectional study used TEE video clips and reports from pati...

Predicting Return Home After Moderate-to-Severe Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Days alive and at home (DAH) is a validated outcome measure that captures health care transitions between time spent at hom...

Evaluation of diastolic function: Machine learning improves classification of left ventricular filling pressure.

AIMS: Current recommendations for echocardiography-based classification of left ventricular filling pressure (LVFP) as normal or elevated, are based o...

Integrated CT pipeline for automatic intracranial hemorrhage evaluation with GPT-enhanced clinical decision support.

OBJECTIVES: Intracranial hemorrhage (ICH) is a time-critical neurological emergency in which rapid CT-based assessment directly informs treatment deci...

Fast cardiac magnetic resonance (CMR) protocol for biventricular functional assessment and tissue characterisation.

BACKGROUND: Clinical use of cardiovascular magnetic resonance (CMR), reference tool for cardiac function and myocardial tissue assessment, is frequent...

Machine learning-based prediction of three-year mortality in elderly inpatients with coronary artery disease combined with heart failure.

OBJECTIVE: Accurate prediction of survival outcome is essential for early intervention and treatment optimization. This study aimed to develop a model...

Prognostic value of the triglyceride-glucose index combined with atherogenic index of plasma for all-cause mortality in critically ill patients with chronic heart failure: a machine learning-driven retrospective cohort study.

BACKGROUND: The triglyceride-glucose index (TyG) and atherogenic index of plasma (AIP) are emerging metabolic biomarkers associated with cardiovascula...

Novel bayesian nonparametric unsupervised learning approach to precision symptom management in cancer survivors: a re-analysis of a comparative effectiveness trial.

Cancer survivors often experience multiple cooccurring symptoms such as insomnia, pain, fatigue, and anxiety; yet conventional analyses in symptom sci...

An integrated machine learning framework for developing a transcriptomic analysis and machine learning-based diagnostic model of gout based on sleep disorder-related genes.

Gout, often comorbid with sleep disorders (SDs), is prevalent in inflammatory arthritis. This comorbidity is notable in patients with impaired renal f...

Proteomics Profiling Reveals Circulating Biomarkers and Dysregulated Pathways in Transthyretin Amyloid Cardiomyopathy.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) causes a restrictive cardiomyopathy resulting in heart failure (HF). Signaling pathways ass...

Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU.

BACKGROUND: Mechanical ventilation is a critical life support technology in the intensive care unit. However, the weaning process remains complex, mak...

A multimarker panel for diagnosis and prognosis prediction of fungal infections.

To develop and validate a routine indicator-based multimarker panel for fungal infection (FI) diagnosis and prognosis prediction. A retrospective coho...

Artificial Intelligence-Enhanced Electrocardiogram Models for Detection of Left Ventricular Dysfunction: A Comparison Study.

BACKGROUND: Several artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown promise in detecting left ventricular systolic dysfu...

[Potential of imaging in the clinical routine : Focus on cardiovascular MRI].

Cardiac magnetic resonance imaging (CMR) provides decisive advantages, particularly in coronary heart disease, myocarditis and cardiomyopathy. It accu...

Retinal Coronary Artery Calcium Scoring - A Scalable Tool for Life Insurers.

BACKGROUND.—: Cardiovascular risk estimation for life insurance underwriting relies on risk estimation from conventional metrics: age, sex, smoking st...

Early management of acute heart failure.

PURPOSE OF REVIEW: Acute heart failure (AHF) is a frequent, high-risk emergency department presentation in which early diagnostic and therapeutic deci...

Systemic Microvasculature Frailty: Brain frailty score predicts contrast-associated acute kidney injury after thrombectomy for stroke.

BACKGROUND: Contrast-associated acute kidney injury (CA-AKI) is a frequent complication after mechanical thrombectomy (MT). Cerebral small vessel dise...

From Clusters to Outcomes: Machine Learning-Based Phenotyping in Intermediate-High-Risk Acute Pulmonary Embolism.

Intermediate-high-risk (IHR) pulmonary embolism (PE) represents a heterogeneous group in whom guideline-based criteria may insufficiently capture biol...

Prediction of left ventricular systolic dysfunction in left bundle branch block using a fine-tuned ECG foundation model.

Left bundle branch block (LBBB) is an important electrocardiographic (ECG) finding strongly associated with left ventricular systolic dysfunction (LVS...