Congestive Heart Failure

Optimizing heart failure care: A machine learning-based prediction of hospital length of stay for heart failure patients.

BACKGROUND: Heart failure represents a significant global health burden, with prolonged length of stay (LoS) tied to increased mortality and costs. Accurate prediction of hospital LoS is crucial for improving resource allocation, lowering mortality and readmission rates, and enhancing patient care. OBJECTIVES: This study leverages machine learning (ML) models to predict LoS categories (Short: 1-3 ...

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 failure. A key clinical challenge is the early identification of patients at high risk of an inadequate response to standard-dose furosemide in order to inform timely treatment intensification or alternative decongestive strategies. However, current ...

Interpretable machine learning to predict postoperative adverse outcomes in cardiac surgery.

BACKGROUND: Cardiac surgery is associated with significant mortality and complication risks. This study aims to develop an interpretable machine learn...

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...

Disorganization of inner retinal layers and disruption of outer retinal layers as predictors of anti-vascular endothelial growth factor treatment outcomes in diabetic macular edema: A scoping review.

We systematically map the evidence on optical coherence tomography (OCT) biomarkers-mainly disorganization of the retinal inner layers (DRIL), disrupt...

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...

A proteomic atlas phenotyping Fabry disease identifies a precise cardiovascular risk signature that integrates mitochondrial and lysosomal pathways.

Fabry disease is an X-linked lysosomal storage disorder caused by α-galactosidase A deficiency, leading to progressive accumulation of Gb3 and lyso-Gb...

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...

The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.

PURPOSE OF REVIEW: Artificial intelligence (AI) is poised to transform heart failure (HF) care across the clinical continuum, yet a substantial gap re...

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...

Quantitative Analysis of Retinal Fluid by a Deep Learning Model in Uveitic Macular Edema.

OBJECTIVE: To assess whether artificial intelligence (AI)-derived fluid volume provides prognostic value for visual outcomes in uveitic macular edema ...

Gene expression integration and similarity score based modeling improve risk stratification in idiopathic venous thrombophilia.

BACKGROUND: Idiopathic venous thromboembolism (VTE) occurs in the absence of provoking factors, limiting the efficacy of current risk stratification. ...

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 ...

Deep Learning-Based Early Prediction of Syncope Onset During Tilt Table Testing via Temporal Convolutional Autoencoder Anomaly Detector.

INTRODUCTION: The head-up tilt table test (HUTT) is a lengthy and uncomfortable procedure for patients which often induces fainting. Post-transient lo...

Composite small vessel disease scores predict hemorrhagic transformation after thrombectomy: a machine learning study.

BACKGROUND: Hemorrhagic transformation (HT) is a major complication of acute ischemic stroke (AIS), especially after mechanical thrombectomy (MT) and ...

Feasibility study of non-invasive blood pressure measurement based on multidimensional characteristics of radial artery pressure pulse wave.

OBJECTIVE: This study focuses on a non-invasive blood pressure prediction method based on radial artery pulse wave feature analysis, aiming to achieve...

Heart failure detection in electrocardiograms using Artificial Intelligence and pragmatic labelling.

The diagnosis of heart failure (HF) is resource-intensive, leading to severe underdiagnosis. This study proposes the use of a deep learning model to d...

Physics-informed DynUNet for brain metastasis segmentation.

BACKGROUND: In neuro-oncology, detecting, segmenting, and delineating the boundaries of small-volume brain metastatic foci remains a significant chall...

Multimodal Artificial Intelligence for Early Detection and Precision Management of Inflammatory and Infiltrative Cardiomyopathies.

BACKGROUND: Inflammatory and infiltrative cardiomyopathies, including cardiac sarcoidosis, transthyretin amyloidosis, and autoimmune myocarditis, are ...