Association of perioperative transfusion of fresh frozen plasma and outcomes after cardiac surgery.

Background: Fresh frozen plasma (FFP) transfusion is used to manage coagulopathy and bleeding in cardiac surgery patients despite uncertainty about its safety and effectiveness.

Methods: We performed a propensity score matched analysis of the Australian and New Zealand Society of Cardiac and Thoracic Surgeons National Cardiac Surgery Database including patients from 39 centres from 2005 to 2018. We investigated the association of perioperative FFP transfusion with mortality and other clinical outcomes.

Fresh frozen plasma transfusion after cardiac surgery.

Introduction: Fresh frozen plasma (FFP) transfusion in the intensive care unit (ICU) is commonly used to treat coagulopathy and bleeding in cardiac surgery, despite suggestion that it may increase the risk of morbidity and mortality through mechanisms such as fluid overload and infection.

Methods: We retrospectively studied consecutive adults undergoing cardiac surgery from the Medical Information Mart for Intensive Care III and IV databases. We applied propensity score matching to investigate the independent association of within-ICU FFP transfusion with mortality and other key clinical outcomes.

Paying attention to cardiac surgical risk: An interpretable machine learning approach using an uncertainty-aware attentive neural network.

Machine learning (ML) is increasingly applied to predict adverse postoperative outcomes in cardiac surgery. Commonly used ML models fail to translate to clinical practice due to absent model explainability, limited uncertainty quantification, and no flexibility to missing data. We aimed to develop and benchmark a novel ML approach, the uncertainty-aware attention network (UAN), to overcome these common limitations.

Tree-based survival analysis improves mortality prediction in cardiac surgery.

Objectives: Machine learning (ML) classification tools are known to accurately predict many cardiac surgical outcomes. A novel approach, ML-based survival analysis, remains unstudied for predicting mortality after cardiac surgery. We aimed to benchmark performance, as measured by the concordance index (C-index), of tree-based survival models against Cox proportional hazards (CPH) modeling and explore risk factors using the best-performing model.