AIMC Topic: Kidney Failure, Chronic
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Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.
Scientific reports
Nov 29, 2024
Early mortality after hemodialysis (HD) initiation significantly impacts the longevity of HD patients. This study aimed to quantify the effect sizes of risk factors on mortality using various machine learning approaches. A cohort of 3284 HD patients ...
Prediction of dialysis adequacy using data-driven machine learning algorithms.
Renal failure
Nov 11, 2024
BACKGROUND: Adequate delivery of hemodialysis (HD), measured by the spKt/V derived from urea reduction, is an important determinant of clinical outcomes in chronic hemodialysis patients. However, the need for pre- and postdialysis blood samples preve...
Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis.
Scientific reports
Nov 5, 2024
Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using erythropoiesis-stimulating agents (ESAs) is challenging. This study introduces a gated recurrent unit-att...
Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.
JACC. Clinical electrophysiology
Oct 9, 2024
BACKGROUND: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laborator...
Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.
Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy
Sep 26, 2024
INTRODUCTION: The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to ...
The application of blood flow sound contrastive learning to predict arteriovenous graft stenosis of patients with hemodialysis.
PloS one
Aug 16, 2024
End-stage kidney disease (ESKD) presents a significant public health challenge, with hemodialysis (HD) remaining one of the most prevalent kidney replacement therapies. Ensuring the longevity and functionality of arteriovenous accesses is challenging...
First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation.
Journal of nephrology
Jun 5, 2024
BACKGROUND: Living kidney donors are screened pre-donation to estimate the risk of end-stage kidney disease (ESKD). We evaluate Machine Learning (ML) to predict the progression of kidney function deterioration over time using the estimated GFR (eGFR)...
Prediction of cardiovascular and renal risk among patients with apparent treatment-resistant hypertension in the United States using machine learning methods.
Journal of clinical hypertension (Greenwich, Conn.)
Mar 24, 2024
Apparent treatment-resistant hypertension (aTRH), defined as blood pressure (BP) that remains uncontrolled despite unconfirmed concurrent treatment with three antihypertensives, is associated with an increased risk of developing cardiovascular and re...
Improved Arterial Stiffness Indices 3 and 6 Months after Living-donor Renal Transplantation.
Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
Mar 11, 2024
Arterial stiffness is a non-traditional risk factor of cardiovascular disease and may explain part of the excess cardiovascular risk in chronic kidney disease patients. Successful renal transplantation (RT) may restore renal function and improve seve...
Deep learning-based prediction of coronary artery calcium scoring in hemodialysis patients using radial artery calcification.
Seminars in dialysis
Jan 4, 2024
OBJECTIVE: This study used random forest model to explore the feasibility of radial artery calcification in prediction of coronary artery calcification in hemodialysis patients.
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