Background: Greater variability in estimated glomerular filtration rate (eGFR) is associated with mortality in patients with chronic kidney disease (CKD). However, the association between eGFR variability and cardiovascular (CV) mortality and/or end-stage kidney disease (ESKD) in the CKD population is not very clear. This study aimed to clarify whether such an association exists.
Methods: We analyzed a final cohort of 2869 eligible Asian patients with CKD. Patients were stratified into three groups according to eGFR variability during the first year and were followed-up for a median of 3.15 years. Primary CV composite endpoints were hospitalization or death due to CV events, and renal composite endpoints were doubling of serum creatinine levels or ESKD. Multivariate Cox hazard models adjusted for classical risk factors and eGFR slope were used to examine the CV and renal risk associated with eGFR variability.
Results: CV endpoints were observed in 14 (2.89%), 25 (5.69%), and 41 (10.79%) patients and renal endpoints were observed in 165 (27.6%), 235 (39.0%), and 298 patients (50.9%) in the lowest, intermediate, and highest tertiles of eGFR variability, respectively. Patients in the highest tertile were at a significantly higher risk for CV events (hazard ratio 1.90; 95% confidence interval 1.03-3.71) than those in the lowest tertile. However, there was no association between eGFR variability and renal endpoints.
Conclusions: Variability in eGFR can predict CV outcomes among patients with CKD.
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http://dx.doi.org/10.1007/s10157-019-01695-9 | DOI Listing |
JCO Precis Oncol
January 2025
Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
Purpose: To investigate whether hormone receptor-positive, human epidermal growth factor receptor 2-low (HR+HER2-low) versus HR+HER2-zero early breast cancers have distinct genomic and clinical characteristics.
Methods: This study included HR+, HER2-negative early breast cancers from patients enrolled in the phase III, randomized BIG 1-98 and SOFT clinical trials that had undergone tumor genomic sequencing. Tumors were classified HR+HER2-low if they had a centrally reviewed HER2 immunohistochemistry (IHC) score of 1+ or 2+ with negative in situ hybridization and HR+HER2-zero if they had an HER2 IHC score of 0.
ESC Heart Fail
January 2025
Division of Research Methodology, Department of Nursing, Faculty of Nursing and Midwifery, Wroclaw Medical University, Wrocław, Poland.
Aims: This study aimed to identify factors associated with frailty in heart failure (HF) patients, focusing on demographic, biochemical and health-related variables. It also explored the correlation between frailty and comorbidities such as malnutrition, cognitive impairment and depression, assessing how these factors interact to influence frailty risk.
Methods: A total of 250 HF patients (mean age 73.
Cureus
December 2024
Clinical Research, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, PAK.
Background Lung cancer is the most frequent cause of cancer-related deaths and the most common type of cancer globally. It is generally classified into two main histologic subtypes: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC is the most prevalent type and is enriched with genetic and molecular diversity.
View Article and Find Full Text PDFNat Commun
January 2025
Oncology Research & Development, Pfizer Inc., San Diego, CA, USA.
To better understand drug resistance mechanisms to CDK4/6 inhibitors and inform precision medicine, we analyze real-world multi-omics data from 400 HR+/HER2- metastatic breast cancer patients treated with CDK4/6 inhibitors plus endocrine therapies, including 200 pre-treatment and 227 post-progression samples. The prevalences of ESR1 and RB1 alterations significantly increase in post-progression samples. Integrative clustering analysis identifies three subgroups harboring different resistance mechanisms: ER driven, ER co-driven and ER independent.
View Article and Find Full Text PDFPLOS Digit Health
January 2025
Department of Mathematics & Statistics, York University, Toronto, Canada.
Chronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic patients showing signs of kidney function impairment based on their CKD development risk.
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