Background: The development of microsurgical techniques has facilitated the establishment of fully vascularized cardiac transplantation models in small mammals. Anastomotic stenosis and bleeding continue to hamper procedures and limit long-term graft survival. In this study we assess a novel technique to improve outcome after cardiac transplantation in mice.
Methods: Our novel technique of murine heterotopic cardiac transplantation consists of three critical steps: (i) a novel procedure for graft harvest; (ii) a modified method for recipient vessel preparation; and (iii) a novel suturing procedure for graft implantation. Importantly, a new knotless suturing technique for end-to-side vascular anastomosis was applied, which allows for adjustment of the anastomosis after transplantation, thus reducing the risk of anastomotic bleeding or stenosis.
Results: The recipient survival rate based on this novel technique was between 90% and 98%, depending on physician expertise. Graft implantation time varied between 20 and 25 minutes after the initial 200 training cases. In comparing the standard knot microvascular suturing technique to the new knotless technique carried out by an experienced surgeon, the latter was found to be more efficient by significantly reducing the rate of anastomotic stenosis (0% vs 8% with knot, p < 0.001, n = 200) and anastomotic bleeding (2% vs 7% with knot, p < 0.05, n = 200).
Conclusions: This novel technique offers a rapid, easy and effective method for murine heterotopic cardiac transplantation.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.healun.2009.05.025 | DOI Listing |
Clin Transplant
January 2025
Department of Cardiovascular Medicine, Mayo Clinic in Arizona, Scottsdale, Arizona, USA.
Background: The prognosis in patients with advanced cardiac amyloidosis (CA) remains poor.
Objectives: We sought to describe survival post heart transplantation (HT) in amyloid compared with non-amyloid recipients, highlight waitlist times within the new allocation system across three Organ Procurement and Transplantation Network (OPTN) regions, and describe multiorgan transplantation (MOT) in hereditary amyloidosis.
Methods: This is a retrospective review of end-stage CA patients who underwent HT at Mayo Clinic from January 2007 to December 2020.
Clin Transplant
January 2025
Department of Cardiovascular Surgery, Başkent University Faculty of Medicine, Ankara, Turkey.
Introduction: End-stage heart failure (ESHF) remains a significant challenge despite optimal treatment, with heart transplantation (HTx) being the gold standard of care. Mechanical circulatory support (MCS) devices such as left ventricular assist devices (LVADs) are increasingly used for temporary or permanent treatment. Psychiatric comorbidities are common in patients with ESHF and may affect treatment outcomes, but the relationship between sociodemographic, clinical, and psychiatric characteristics remains unclear.
View Article and Find Full Text PDFAge Ageing
January 2025
Department of Population Medicine, Heath Park, Cardiff University, CF14 4YS.
Objectives: To investigate if frailty status alters following solid organ transplantation (lung, liver, kidney and heart) without rehabilitation intervention.
Research Design And Methods: Studies published between 1 January 2000 and 30 May 2023 were searched across five databases. Studies measuring frailty, using a validated or established frailty measure, pre- and post-transplant were included.
Kardiol Pol
January 2024
Institute of Cardiology, Jagiellonian University Medical College, Kraków, Poland.
PLoS One
January 2025
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms-Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!