Background: Atrial fibrillation is a common complication after cardiac surgery. The aim of this study is to evaluate whether N-acetylcysteine (NAC) could prevent postoperative atrial fibrillation (POAF).
Methods: PubMed, Embase and Cochrane Center Register of Controlled Trials were searched from the date of their inception to 1 July 2013 for relevant randomized controlled trials (RCTs), in which NAC was compared with controls for adult patients undergoing cardiac surgery. Outcome measures comprised the incidence of POAF, all-cause mortality, length of intensive care unit (ICU) stay, hospital length of stay, and the incidence of cerebrovascular events. The meta-analysis was performed with the fixed-effect model or random-effect model according to the heterogeneity.
Results: We retrieved ten studies enrolling a total of 1026 patients. Prophylactic NAC reduced the incidence of POAF (OR 0.56; 95% CI 0.40 to 0.77; P < 0.001) and all-cause mortality (OR 0.40; 95% CI 0.17 to 0.93; P = 0.03) compared with controls, but failed to reduce the stay in ICU and overall stay in hospital. No difference in the incidence of cerebrovascular events was observed.
Conclusions: Prophylactic use of NAC could reduce the incidence of POAF and all-cause mortality in adult patients undergoing cardiac surgery. However, larger RCTs evaluating these and other postoperative complication endpoints are needed.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4012554 | PMC |
http://dx.doi.org/10.1186/1471-2261-14-52 | DOI Listing |
J Med Internet Res
March 2025
Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia.
Background: Conversational artificial intelligence (AI) allows for engaging interactions, however, its acceptability, barriers, and enablers to support patients with atrial fibrillation (AF) are unknown.
Objective: This work stems from the Coordinating Health care with AI-supported Technology for patients with AF (CHAT-AF) trial and aims to explore patient perspectives on receiving support from a conversational AI support program.
Methods: Patients with AF recruited for a randomized controlled trial who received the intervention were approached for semistructured interviews using purposive sampling.
Europace
March 2025
Clinical Cardiac Academic Group, Genetic and Cardiovascular Sciences Institute, City-St George's University of London, London, UK.
Atrial fibrillation (AF) is one of the most common cardiac diseases and a complicating comorbidity for multiple associated diseases. Many clinical decisions regarding AF are currently based on the binary recognition of AF being present or absent with the categorical appraisal of AF as continued or intermittent. Assessment of AF in clinical trials is largely limited to the time to (first) detection of an AF episode.
View Article and Find Full Text PDFAm J Respir Crit Care Med
March 2025
Imperial College London, National Heart and Lung Institute, London, United Kingdom of Great Britain and Northern Ireland;
JACC Case Rep
January 2025
Division of Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA. Electronic address:
Background: Although rare, embolization of left atrial appendage occlusion (LAAO) devices carries a significant morbidity and mortality burden.
Case Summary: An asymptomatic 77-year-old woman with inability to tolerate anticoagulation due to gastrointestinal bleeding presented for 45-day transesophageal echocardiography following LAAO with a Watchman device, which demonstrated incidental device migration to the left ventricular outflow tract (LVOT). Percutaneous extraction was performed using a novel technique with rat tooth/alligator forceps to successfully retrieve the Watchman from the LVOT using a transaortic approach.
Eur J Haematol
March 2025
Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Background: One of the limiting toxicities of BTKi is the development of atrial fibrillation (AF), with an incidence of 3%-16%.
Aim: This study aimed to identify patients with chronic lymphocytic leukemia (CLL) starting both first- and second-generation BTKis who are at high risk of developing AF using a machine learning approach.
Methods: The CLL cohort is based on data obtained from electronic medical records from Maccabi, the second-largest healthcare organization in Israel.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!