Results 161 to 170 of about 166,832 (321)
APOE4 and cognition in intracranial atherosclerosis: beyond Alzheimer's pathology
Abstract INTRODUCTION The apolipoprotein E ε4 (APOE ε4) allele is a major genetic risk factor for Alzheimer's disease, but its relevance to cognition in intracranial atherosclerosis (ICAS) remains unclear. We investigated the association between APOE ε4 and cognition in ICAS. METHODS Baseline data from a multicenter cohort were analyzed.
Anqi Cheng +14 more
wiley +1 more source
In Estonia, warfarin is widely prescribed by general practitioners to prevent and treat thromboembolic diseases. To date, there has been no systematic analysis of the potential risk of warfarin interactions with other drugs in the outpatient population ...
Gavronski M +3 more
doaj
Letters to the Editor: Managing warfarin therapy in the community [PDF]
GS Hale
openalex +1 more source
Outcomes of Aquablation in BPH with bladder stones: Analysis of the ICARUS database
Abstract Objective The purpose of this study is investigate the clinical outcomes of men with benign prostatic hyperplasia (BPH) and bladder stones treated concomitantly with Aquablation and bladder stone removal in an international, multi‐institutional cohort.
Joshua D. Cabral +25 more
wiley +1 more source
Effect of Fentanyl Patch on PT-INR Fluctuation during Warfarin Administration
Kanako Watanabe +6 more
openalex +2 more sources
Abstract Objectives The objective of this study is to evaluate the safety and efficacy of Rezūm water vapour energy therapy (WAVE) in Japanese patients with benign prostatic hyperplasia (BPH) continuing antithrombotic therapy and to validate the Okayama University Modified Clavien‐Dindo classification (OU‐mCD) for perioperative hematuria.
Takatoshi Moriwake +13 more
wiley +1 more source
The impact of simvastatin on warfarin disposition and dose requirements [PDF]
Elizabeth Sconce +4 more
openalex +1 more source
Machine learning methods for predicting adverse drug events: A systematic review
Abstract Predicting adverse drug events (ADEs) in outpatient settings is crucial for improving medication safety, identifying high‐risk patients and reducing health‐care costs. While traditional methods struggle with the complexity of health‐care data, machine learning (ML) models offer improved prediction capabilities; however, their effectiveness in ...
Niaz Chalabianloo +8 more
wiley +1 more source

