Chronic kidney disease (CKD) currently affects 20 million Americans and is associated with increased morbidity and mortality. Resource-efficient and appropriate treatment of CKD benefits the patient and provides improved resource allocation for the health care system. Prediction models can be useful in efficiently allocating resources, and currently are being used at the bedside for several important clinical decisions. There is a paucity of prediction models in use in nephrology, but one such model, the Kidney Failure Risk Equation, uses routinely collected laboratory values and can inform clinical decisions related to the following: (1) triage of nephrology referrals, (2) evaluating the need for more intensive interdisciplinary clinic care, (3) determining the timing of modality education, and (4) dialysis access planning. The development of new models that predict survival and quality of life on dialysis, success on home modalities, failure of arteriovenous fistulas, and risk of cardiovascular disease in patients with CKD is needed.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.semnephrol.2016.12.004 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Development of a CT radiomics prognostic model for post renal tumor resection overall survival based on transformer enhanced K-means clustering.
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Preserved brain youthfulness: longitudinal evidence of slower brain aging in superagers.
Geroscience
January 2025
Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Background: Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.
Methods: A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years.
Pharmaceuticals removal from aqueous solution by water hyacinth (Eichhornia crassipes): a comprehensive investigation of kinetics, equilibrium, and thermodynamics.
Environ Sci Pollut Res Int
January 2025
Grupo de Investigación Materiales Con Impacto (Mat&Mpac), Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 No. 30-65, 050026, Medellín, Colombia.
This study shows the efficiency of WH-C450, an adsorbent obtained from water hyacinth (WH) biomass, in the removal of sulfamethoxazole (SMX) from aqueous solutions. The process involves calcination of WH at 450 °C to produce an optimal adsorbent material capable of removing up to 73% of SMX and maximum SMX adsorption capacity of 132.23 mg/g.
View Article and Find Full Text PDFEmoAtlas: An emotional network analyzer of texts that merges psychological lexicons, artificial intelligence, and network science.
Behav Res Methods
January 2025
CogNosco Lab, Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy.
We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.
View Article and Find Full Text PDFExploring the role of multimodal [F]F-PSMA-1007 PET/CT and multiparametric MRI data in predicting ISUP grading of primary prostate cancer.
Eur J Nucl Med Mol Imaging
January 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!