Post Cardiotomy Extra Corporeal Membrane Oxygenation: Australian Cohort Review.

BACKGROUND: Over the last two decades, technological advancements in the delivery of extra corporeal membrane oxygenation (ECMO) have seen its use broaden and results improve. However, in the post cardiotomy ECMO patient group, survival remains very poor without significant improvements over the last two decades. Our study aims to report on the Australian experience, with the intention of providing background data for the formation of guidelines in the future. METHODS: Retrospective analysis of prospectively collected data from the Australian and New Zealand Society of Cardiothoracic Surgeons (ANZSCTS) Database was performed. The ANZSCTS database captures at least 60% of cardiac surgical data in Australia, annually. Data was collected on adult patients who received ECMO post cardiotomy from September 2016 to November 2017 inclusive. Transplant and primary cardiomyopathy patients were excluded. RESULTS: Of the 16,605 adult patients undergoing cardiac surgery in the 15-month period of the study, 87 patients required post cardiotomy ECMO (0.52%). The average age of the entire cohort was 56 years. Overall survival to discharge was 43.7% (n=38). Multivariable logistic regression analysis demonstrated that multiorgan failure (MOF), increasing age and longer cardiopulmonary bypass time were significant predictors of in hospital mortality. CONCLUSIONS: Post cardiotomy ECMO support is an uncommon condition. Survival in this study appears to be better than historical reports. Identification of poor prognostic indicators in this study may help inform the development of guidelines for the most appropriate use of this support modality

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. Two Bayesian uncertainty quantification methods were tested, generalized variational inference (GVI) or a posterior network (PN). The UAN models were compared with an ensemble of XGBoost models and a Bayesian logistic regression model (LR) with imputation. The derivation datasets consisted of 153,932 surgery events from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) Cardiac Surgery Database. An external validation consisted of 7343 surgery events which were extracted from the Medical Information Mart for Intensive Care (MIMIC) III critical care dataset. The highest performing model on the external validation dataset was a UAN-GVI with an area under the receiver operating characteristic curve (AUC) of 0.78 (0.01). Model performance improved on high confidence samples with an AUC of 0.81 (0.01). Confidence calibration for aleatoric uncertainty was excellent for all models. Calibration for epistemic uncertainty was more variable, with an ensemble of XGBoost models performing the best with an AUC of 0.84 (0.08). Epistemic uncertainty was improved using the PN approach, compared to GVI. UAN is able to use an interpretable and flexible deep learning approach to provide estimates of model uncertainty alongside state-of-the-art predictions. The model has been made freely available as an easy-to-use web application demonstrating that by designing uncertainty-aware models with innately explainable predictions deep learning may become more suitable for routine clinical use

Platelet versus fresh frozen plasma transfusion for coagulopathy in cardiac surgery patients.

BackgroundPlatelets (PLTS) and fresh frozen plasma (FFP) are often transfused in cardiac surgery patients for perioperative bleeding. Their relative effectiveness is unknown.MethodsWe conducted an entropy-weighted retrospective cohort study using the Australian and New Zealand Society of Cardiac and Thoracic Surgeons National Cardiac Surgery Database. All adults undergoing cardiac surgery between 2005-2021 across 58 sites were included. The primary outcome was operative mortality.ResultsOf 174,796 eligible patients, 15,360 (8.79%) received PLTS in the absence of FFP and 6,189 (3.54%) patients received FFP in the absence of PLTS. The median cumulative dose was 1 unit of pooled platelets (IQR 1 to 3) and 2 units of FFP (IQR 0 to 4) respectively. After entropy weighting to achieve balanced cohorts, FFP was associated with increased perioperative (Risk Ratio [RR], 1.63; 95% Confidence Interval [CI], 1.40 to 1.91; PConclusionIn perioperative bleeding in cardiac surgery patient, platelets are associated with a relative mortality benefit over FFP. This information can be used by clinicians in their choice of procoagulant therapy in this setting