The aim of this prospective, randomized study was to investigate the effect of pretreatment with two different intracellular calcium-lowering drugs (verapamil and metoprolol) on recovery from atrial effective refractory period (AERP) shortening after internal electrical cardioversion (EC) of persistent atrial fibrillation (AF) in patients on amiodarone. Twenty-one patients on amiodarone for at least 30 days were referred to our hospital for internal EC of a persistent AF refractory to external EC. They were randomized to receive only amiodarone (group AMI, n=7), or amiodarone and verapamil 240 mg/day (group VER, n=7), or amiodarone and metoprolol 100 mg/day (group MET, n=7). Left AERP was measured 10 min and 24 h after EC. AERP was also determined in 13 controls. The AERP after 10 min was significantly shorter in group AMI (201 (31) ms, P<0.02) and group MET (203 (34) ms, P<0.03) than in controls (249 (45) ms), but not in group VER (237 (51) ms, P=NS). The AERP after 24 h was still significantly shorter in group AMI (204 (38) ms, P<0.04) than in controls, but not in group MET (225 (52) ms, P=NS) or in group VER (290 (36) ms, P=NS). Pretreatment with amiodarone and verapamil prevents AERP shortening, while pretreatment with amiodarone and metoprolol only accelerated AERP recovery.
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http://dx.doi.org/10.1016/s0167-5273(02)00210-3 | DOI Listing |
Explor Res Clin Soc Pharm
September 2024
Speciality Dental Residency Program, Primary Health Care Centers, Manama, Bahrain.
Background: Medication review and reconciliation is essential for optimizing drug therapy and minimizing medication errors. Large language models (LLMs) have been recently shown to possess a lot of potential applications in healthcare field due to their abilities of deductive, abductive, and logical reasoning. The present study assessed the abilities of LLMs in medication review and medication reconciliation processes.
View Article and Find Full Text PDFHeadache
October 2024
Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA.
Objective: To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications.
Background: Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial-and-error approach is inefficient.
Methods: In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023.
Cell Rep Med
May 2024
Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Ted Rogers Centre for Heart Research, Toronto, ON M5G 1M1, Canada; Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada. Electronic address:
Pathogenic variants in MYH7 and MYBPC3 account for the majority of hypertrophic cardiomyopathy (HCM). Targeted drugs like myosin ATPase inhibitors have not been evaluated in children. We generate patient and variant-corrected iPSC-cardiomyocytes (CMs) from pediatric HCM patients harboring single variants in MYH7 (V606M; R453C), MYBPC3 (G148R) or digenic variants (MYBPC3 P955fs, TNNI3 A157V).
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