End Stage Renal Disease

Univariate- and machine learning-based plasma metabolite signature differentiates PSC-IBD from IBD and is predicted to be driven by gut microbial changes.

INTRODUCTION: Inflammatory bowel disease (IBD) is a group of chronic inflammatory conditions of the gastrointestinal tract comprising two major phenotypes, Crohn's disease (CD) and ulcerative colitis (UC). Up to 8% of patients with IBD also develop primary sclerosing cholangitis (PSC), characterised by cholestasis and progressive destruction of the biliary tree, resulting in cirrhosis, end-stage l...

Incremental Prognostic Value of Pericardial Adipose Tissue Radiomic Phenotype Based on Cardiac MRI in Patients With End-Stage Renal Disease.

BACKGROUND: While pericardial adipose tissue (PEAT) volume is linked to cardiac risk, the prognostic value of its radiomic features for major adverse cardiac events (MACE: a composite of cardiac death, myocardial infarction, heart failure hospitalization, or stroke) in end-stage renal disease (ESRD) patients is unknown. PURPOSE: To evaluate the association of PEAT radiomics with MACE in patients w...

FDG-PET Medullary Total Tumor Volume Highlights High-Risk Newly Diagnosed Multiple Myeloma Patients in CASSIOPEIA Trial.

This study aimed to assess the prognostic value of medullary total metabolic tumor volume (mTMTV) derived from fluorodeoxyglucose-positron emission to...

From algorithms to action: Risk stratification for arteriovenous fistula failure in hemodialysis.

BACKGROUND: Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis, yet primary failure and early dysfunction remain common, th...

Development and validation of an artificial intelligence-based model for predicting teicoplanin plasma concentrations in intensive care unit patients with pulmonary infections: a retrospective study.

INTRODUCTION: Teicoplanin is widely used to treat pulmonary infections, particularly in critically ill patients with gram-positive bacterial infection...

Machine Learning Prediction of Progression to Dialysis in Patients With Polycystic Kidney Disease: Population-Based Retrospective Cohort Study.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD), characterized by progressive cyst growth and renal decline, is the leading genetic c...

Advanced artificial intelligence vs simpler models for 1-year death prediction among patients receiving hemodialysis.

OBJECTIVES: We evaluated the data requirement for modern AI tools to outperform simpler models in predicting short-term mortality in over 500 000 pati...

A complete blood count-based machine learning model for rapid differentiation of aplastic anemia, immune thrombocytopenia, and myelodysplastic syndromes in routine clinical practice.

BACKGROUND: Accurate differentiation of common hematologic disorders remains challenging in routine clinical practice and often requires invasive diag...

An interpretable nomogram for predicting sleep disturbance risk in maintenance hemodialysis patients.

BACKGROUND: This study aimed to develop and validate an interpretable nomogram to predict the risk of sleep disturbance in maintenance hemodialysis (M...

Preoperative Risk Score for Mortality Within 3 Years of Elective Aortobifemoral Artery Bypass.

BACKGROUND: The purpose of this study was to create a risk score for mortality within 3 years of elective aortobifemoral artery bypass for aortoiliac ...

Cross-validation strategies under data dependency: An example with anemia prediction in sheep using ocular conjunctiva images.

Computer vision techniques have been increasingly used in livestock farming for tasks such as animal identification and health monitoring, but their p...

Predictive divergence in machine learning models for clinical mortality risk: A multicohort study of covid-19 patients.

BACKGROUND: Machine learning (ML) algorithms are increasingly used in healthcare to support clinical decision-making. While models with similar overal...

Comparison of the diagnostic performance of machine learning algorithms for differentiating iron deficiency anemia and thalassemia.

Exploring factors and models to predict post-dialysis volume overload status in maintenance hemodialysis patients based on pre-dialysis parameters.

OBJECTIVE: This study explored factors and models to predict post-dialysis volume overload status in maintenance hemodialysis patients (MHD) based on ...

Predicting mortality and short-term outcomes of continuous kidney replacement therapies in neonates and infants.

BACKGROUND: Continuous kidney replacement therapy (CKRT) has emerged as a valuable treatment option in critically ill neonates and infants with acute ...

Deep learning for fast screening and localization of spinal dural arteriovenous fistulas to enhance clinical workflow.

Manual processing of spinal CTA images for detecting dural arteriovenous fistulas (SDAVF) is laborious and operator-dependent. We developed an automat...

msCNN-PLM-FAD: Enhanced predicting FAD binding sites by using protein language representation combined with multiple separable windows convolutional neural networks.

The FAD binding sites classification problem is crucial because it is directly related to many diseases, such as flavoprotein-associated diseases, dev...

Association between hypothermic machine perfusion parameters and graft function in deceased donor kidney transplantation.

BACKGROUND: Kidney transplantation (KT) is the most effective treatment for end-stage renal disease. Hypothermic machine perfusion (HMP) can improve r...

Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.

Artificial intelligence (AI) is rapidly transforming many areas of medicine. Pediatric nephrology may be a field of particular relevance due to its da...

Regional brain age is decreased in children with sickle cell anemia.

DeepBrainNet, a machine learning tool, uses magnetic resonance imaging (MRI) to predict an individual's brain age, allowing calculation of the brain a...