The objective of this study was to develop tissue Doppler parameters that could be used to differentiate right ventricular (RV) volume overload from RV pressure overload. The RV-pressure-overload group consisted of 40 patients with severe pulmonary hypertension, and the RV-volume-overload group consisted of 40 patients who had an atrial septal defect without evidence of right-to-left shunt, significant pulmonary hypertension, or Eisenmenger's complex. Another 40 healthy subjects were enrolled and served as a control group. Routine echocardiography and tissue Doppler imaging were performed. RV myocardial performance index was determined based on data collected during tissue Doppler imaging over the lateral tricuspid annulus. In patients with RV pressure overload, tissue Doppler parameters showed characteristically lower systolic velocity over the tricuspid annulus (RV myocardial systolic wave [Sm]) and longer isovolumic relaxation time (RV-IVRT). Nevertheless, in patients with RV volume overload, RV-Sm increased significantly, but early-diastolic velocity over tricuspid annulus was relatively low. In conclusion, RV-MPI, RV-Sm/early-diastolic velocity over tricuspid annulus, and RV-IVRT/RV-Sm were all useful to differentiate RV pressure overload from volume overload, although RV-IVRT/RV-Sm was the best parameter, with excellent sensitivity and specificity.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.amjcard.2007.08.058 | DOI Listing |
Indian J Crit Care Med
December 2024
Department of Anesthesiology, Pain Medicine and Critical Care, All India Institute of Medical Sciences (AIIMS), New Delhi, India.
Pharmacotherapy
January 2025
Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, Georgia, USA.
Background: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given the high frequency and time-dependency of their use and other factors affecting FO. We sought to employ unsupervised machine learning methods to uncover medication administration patterns correlating with FO.
View Article and Find Full Text PDFSci Rep
January 2025
Cardiovascular Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Heart failure with preserved ejection fraction (HFpEF) is increasingly common but its pathogenesis is poorly understood. The ability to assess genetic and pharmacologic interventions is hampered by the lack of robust preclinical mouse models of HFpEF. We developed a novel "two-hit" model, which combines obesity and insulin resistance with chronic pressure overload to recapitulate clinical features of HFpEF.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, 324000, Zhejiang, China.
Fluid administration is widely used to treat hypotension in patients undergoing veno-venous extracorporeal membrane oxygenation (VV-ECMO). However, excessive fluid administration may lead to fluid overload can aggravate acute respiratory distress syndrome (ARDS) and increase patient mortality, predicting fluid responsiveness is of great significance for VV-ECMO patients. This prospective single-center study was conducted in a medical intensive care unit (ICU) and finally included 51 VV-ECMO patients with ARDS in the prone position (PP).
View Article and Find Full Text PDFSci Rep
December 2024
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, 03680, Kyiv, Ukraine.
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!