<b><i>Introduction:</i></b> Cardiovascular side effects associated with energy drink consumption may be related to effects on vascular endothelial function, heart rate, blood pressure, and electrocardiogram parameters. We sought to measure them following energy drink consumption. <b><i>Methods:</i></b> Forty-four healthy non-smoking young volunteer medical students, at an average age of 24.7 years (range 23–27 years, 34 males), with an average BMI of 23.4, received electrocardiograms and had their heart rates and blood pressures taken. Subjects then underwent baseline testing of endothelial function using the technique of endothelium-dependent flow-mediated dilatation (FMD) with high-resolution ultrasound. The subjects then drank an energy drink (24 oz Monster Energy Drink®). Hemodynamic measurements were repeated 15 and 90 min later. FMD and the electrocardiogram were repeated 90 min later. The FMD was calculated as the ratio of the post-cuff release and the baseline diameter. <b><i>Results:</i></b> Energy drink consumption resulted in a significantly attenuated peak FMD response (mean ± SD): baseline 5.1 ± 4.1% versus post-energy drink (2.8 ± 3.8%; <i>p</i> = 0.004). In addition, systolic and diastolic blood pressures and heart rate increased after 15 min. Diastolic blood pressure and heart rate remained increased 90 min following energy drink consumption. There were no significant changes in electrocardiogram parameters. <b><i>Conclusion:</i></b> Energy drink consumption was associated with an acute significant impairment in endothelial function in young healthy adults as well as with significant hemodynamic changes. As energy drinks are becoming more popular, it is important to study their effects to better determine safe consumption patterns.
A 66-year-old woman with follicular lymphoma on lenalidomide and rituximab presented with chest pain. High-sensitivity troponin T peaked at 7,566 ng/l. Cardiac biopsy revealed extensive inflammation consistent with medication-induced myocarditis. Lenalidomide was stopped with improvement in troponins and patient was initiated on high-dose corticosteroid therapy. ( Level of Difficulty: Intermediate. )
Background: Transcatheter aortic valve replacement (TAVR) is being increasingly performed in patients with severe aortic stenosis. Despite newer generation valves, atrioventricular (AV) conduction disturbance is a common complication, necessitating permanent pacemaker (PPM) implantation in about 10% of patients. Hence, it is imperative to improve periprocedural risk stratification to predict PPM implantation after TAVR. The objective of this study was to externally validate a novel risk-stratification model derived from the National Inpatient Sample (NIS) database that predicts risk of PPM from TAVR. Methods:Components of the score included pre-TAVR left and right bundle branch block, sinus bradycardia, second-degree AV block, and transfemoral approach. The scoring system was applied to 917 patients undergoing TAVR at our institution from November 2011 to February 2017. We assessed its predictive accuracy by looking at two components: discrimination using the C-statistic and calibration using the Hosmer-Lemeshow goodness of fit test.Results: Ninety patients (9.8%) required PPM. The scoring system showed good discrimination with C-statistic score of 0.6743 (95% CI: 0.618-0.729). Higher scores suggested increased PPM risk, that is, 7.3% with score ≤3, 19.23% with score 4-6, and 37.50% with score ≥7. Patients requiring PPM were older (81.4 versus 78.7 years, P = .002). Length of stay and in-hospital mortality was significantly higher in PPM group. Conclusions:The NIS database derived PPM risk prediction model was successfully validated in our database with acceptable discriminative and gradation capacity. It is a simple but valuable tool for patient counseling pre-TAVR and in identifying high-risk patients. K E Y W O R D Selectrocardiogram, electrophysiology -basic, electrophysiology -clinical, pacing
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.