Treatment Strategies and Outcomes of Right Ventricular Failure Post Left Ventricular Assist Device Implantation: An INTERMACS Analysis

The Society of Thoracic Surgeons Intermacs 2024 Annual Report: Focus on Outcomes in Younger Patients

The 15th Annual Report from The Society of Thoracic Surgeons Interagency Registry for Mechanically Assisted Circulatory Support includes 29,634 continuous-flow left ventricular assist devices from the 10-year period between 2014 and 2024. The outcomes reported here demonstrate continued improved survival in the current era of fully magnetically levitated devices, with a significantly higher 1-year (85.7% vs 78.4%) and 5-year (59.7% vs 43.7%) survival than those receiving non-magnetically levitated devices. Magnetically levitated device recipients are experiencing a lower incidence of adverse events, including freedom from gastrointestinal bleeding (72.6%), device malfunction (82.9%), and stroke (86.7%) at 5 years. Additionally, a focus on a subgroup of patients younger than 50 years of age has demonstrated both superior outcomes in survival (91.6% survival at 1 year and 72.6% survival at 5 years) and decreased incidence of adverse events compared with older recipients. This younger cohort also demonstrated more tolerance to the characteristics of sex, race, ethnicity, and psychosocial indicators that are associated with worse outcomes after heart transplantation. Based upon these data, a potential net prolongation of life may be realized by considering prolonged left ventricular assist device support prior to heart transplantation in this population. These analyses provide preliminary data that could positively influence adoption of left ventricular assist device technology in groups previously not seen as candidates for this therapy, while providing a more responsible donor allocation strategy for advanced heart failure patients.

Advanced Heart Failure: Therapeutic Options and Challenges in the Evolving Field of Left Ventricular Assist Devices

Heart Failure is a chronic and progressively deteriorating syndrome that has reached epidemic proportions worldwide. Improved outcomes have been achieved with novel drugs and devices. However, the number of patients refractory to conventional medical therapy is growing. These advanced heart failure patients suffer from severe symptoms and frequent hospitalizations and have a dismal prognosis, with a significant socioeconomic burden in health care systems. Patients in this group may be eligible for advanced heart failure therapies, including heart transplantation and chronic mechanical circulatory support with left ventricular assist devices (LVADs). Heart transplantation remains the treatment of choice for eligible candidates, but the number of transplants worldwide has reached a plateau and is limited by the shortage of donor organs and prolonged wait times. Therefore, LVADs have emerged as an effective and durable form of therapy, and they are currently being used as a bridge to heart transplant, destination lifetime therapy, and cardiac recovery in selected patients. Although this field is evolving rapidly, LVADs are not free of complications, making appropriate patient selection and management by experienced centers imperative for successful therapy. Here, we review current LVAD technology, indications for durable MCS therapy, and strategies for timely referral to advanced heart failure centers before irreversible end-organ abnormalities.

The Role of Artificial Intelligence and Machine Learning in the Prediction of Right Heart Failure after Left Ventricular Assist Device Implantation: A Comprehensive Review

One of the most challenging and prevalent side effects of LVAD implantation is that of right heart failure (RHF) that may develop afterwards. The purpose of this study is to review and highlight recent advances in the uses of AI in evaluating RHF after LVAD implantation. The available literature was scanned using certain key words (artificial intelligence, machine learning, left ventricular assist device, prediction of right heart failure after LVAD) was scanned within Pubmed, Web of Science, and Google Scholar databases. Conventional risk scoring systems were also summarized, with their pros and cons being included in the results section of this study in order to provide a useful contrast with AI-based models. There are certain interesting and innovative ML approaches towards RHF prediction among the studies reviewed as well as more straightforward approaches that identified certain important predictive clinical parameters. Despite their accomplishments, the resulting AUC scores were far from ideal for these methods to be considered fully sufficient. The reasons for this include the low number of studies, standardized data availability, and lack of prospective studies. Another topic briefly discussed in this study is that relating to the ethical and legal considerations of using AI-based systems in healthcare. In the end, we believe that it would be beneficial for clinicians to not ignore these developments despite the current research indicating more time is needed for AI-based prediction models to achieve a better performance.