Congestive Heart Failure

Development and external validation of an interpretable machine-learning model for HFpEF comorbidity risk in COPD patients.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) and Heart Failure with preserved Ejection Fraction (HFpEF) frequently coexist, leading to increased hospitalization, mortality, and healthcare burden. Early identification of HFpEF risk in COPD patients is critical for timely intervention. AIM: To develop and validate an interpretable machine learning (ML) model for predicting HFpEF risk in ...

Validation of a machine learning model for predicting early deterioration in the emergency department.

Early recognition of patients at risk for deterioration in the emergency department (ED) is critical for patient safety. Traditional early warning scores rely on structured triage data and often perform poorly in the dynamic ED environment. We developed and evaluated two machine learning models integrating structured triage data with transformer-based embeddings of free-text nursing triage notes t...

Advancements and challenges in blood pressure monitoring using pulse wave propagation: a comprehensive review and ISO 81060-2 based statistical analysis.

Cardiovascular diseases, particularly hypertension, remain a major global health burden, highlighting the need for accurate and accessible blood press...

PneumoScore: Risk Prediction Model for 90-Day Mortality After Lung Resection.

BACKGROUND: Lung resection is the gold-standard treatment for early stage lung cancer, but remains associated with significant mortality, highlighting...

Early prediction of in-hospital mortality in ICU patients with heart failure using an interpretable machine learning model: a retrospective study based on MIMIC-IV.

BACKGROUND: Heart failure (HF) remains a major cause of morbidity and mortality worldwide, and acute decompensation frequently necessitates intensive ...

Hybrid model based on fuzzy logic and classification trees for the prediction of mortality of critical trauma patients: Pre-hospital variables from the RETRAUCI registry.

BACKGROUND: In critically injured trauma patients, tools that stratify injury severity and estimate mortality are essential. Fuzzy logic (FL) enables ...

Prognostic Imaging Biomarkers in Diabetic Macular Edema Treated with Anti-VEGF: A Multicenter AI Perspective.

INTRODUCTION: This study aimed to identify optical coherence tomography (OCT) biomarkers at baseline and after the loading phase (LP) of antivascular ...

Hyperglycemia-driven metabolic memory signaling destabilizes METTL1 to trigger inflammatory hypertrophy and fibrosis in diabetic cardiomyopathy.

BACKGROUND: Diabetic cardiomyopathy (DCM) occurs in the context of coronary artery disease or pressure overload heart disease, characterized by altera...

Artificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya.

IMPORTANCE: Early detection of risk of heart failure with reduced ejection fraction remains challenging in resource-limited settings due to limited ac...

Testing Near-Field Radio-Frequency Sensors to Predict Respiratory Distress in Patients Living With Dementia: A Pilot Feasibility and Acceptability Study.

BackgroundPeople living with dementia (PLWD) with advanced illness are prone to respiratory distress yet often cannot self-report dyspnea, delaying re...

Artificial intelligence applications in OCT and OCTA for diabetic retinopathy: A systematic review.

PURPOSE: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide. Optical coherence tomography (OCT) and OCT angiography (OCTA) pr...

Use of Machine-Learning Models to Identify Clinical Features Associated With A Future Clinical Worsening Event in Patients With Pulmonary Arterial Hypertension.

Clinical worsening events are increasingly recognized as a meaningful outcome in pulmonary arterial hypertension (PAH). We applied machine-learning mo...

Deep learning model for genotype prediction from echocardiographic videos in non-ischaemic dilated cardiomyopathy.

AIMS: Non-ischaemic dilated cardiomyopathy (DCM) is frequently characterized by the presence of pathogenic germline variants, and genotype positivity ...

Diuretic Ultrasound Evaluation of Surgical Necessity in Pediatric Ureteropelvic Junction Obstruction.

RATIONALE AND OBJECTIVES: This study aimed to develop an interpretable machine learning (ML) model using diuretic ultrasonography to predict the neces...

Artificial intelligence-assisted peri-operative echocardiography: a multicentre observational study.

INTRODUCTION: Point-of-care transthoracic echocardiography performed by anaesthetists can influence peri-operative management but is constrained by ti...

Getting ahead of sudden cardiac arrest in pediatric hypertrophic cardiomyopathy patients.

INTRODUCTION: Sudden cardiac arrest (SCA) remains one of the most devastating complications of pediatric hypertrophic cardiomyopathy (HCM). Despite ma...

A Novel Nomogram for Predicting Meningioma Grade Based on Radiomics Features and Clinical Characteristics.

OBJECTIVE: This study aimed to develop a predictive model utilizing radiomics features and clinical characteristics to accurately differentiate low-gr...

Contemporary heart failure evidence in 2025: a joint summary of key trials from the European Journal of Heart Failure and ESC Heart Failure Journal.

Heart failure (HF) remains a major global health challenge, characterized by high morbidity, mortality, and healthcare costs despite substantial advan...

AI-echocardiography: an eagle eye for the prognostic staging of ATTR cardiomyopathy.

Prognostic Modeling Based on Post-Endovascular Thrombectomy Systolic Blood Pressure Trajectories Using Explainable Artificial Intelligence: A Secondary Analysis of the OPTIMAL-BP Trial.

Blood pressure (BP) management following successful reperfusion after endovascular thrombectomy (EVT) is critical in achieving favorable clinical outc...