Coronary artery bypass grafting remains the standard treatment for patients with extensive coronary artery disease. Coronary surgery without use of cardiopulmonary bypass avoids the deleterious systemic inflammatory effects of the extracorporeal circuit. However there is an ongoing debate surrounding the clinical outcomes after on-pump versus off-pump coronary artery bypass (ONCAB versus OPCAB) surgery. The current review is based on evidence from randomized controlled trials (RCTs) and meta-analyses of randomized studies. It focuses on operative mortality, mid- and long-term survival, graft patency, completeness of revascularisation, neurologic and neurophysiologic outcomes, perioperative complications and outcomes in the high risk groups. Early and late survival rates for both OPCAB and ONCAB grafting are similar. Some studies suggest early poorer vein graft patency with off-pump when compared with on-pump, comparable midterm arterial conduit patency with no difference in long term venous and arterial graft patency. A recent, pooled analysis of randomised trials shows a reduction in stroke rates with use off-pump techniques. Furthermore, OPCAB grafting seems to reduce postoperative renal dysfunction, bleeding, transfusion requirement and respiratory complications while perioperative myocardial infarction rates are similar to ONCAB grafting. The high risk patient groups seem to benefit from off-pump coronary surgery.

Paediatric heart surgery is associated with increased inflammation and the production of reactive oxygen species. Use of the extracorporeal cardiopulmonary bypass during correction of congenital heart defects generates reactive oxygen species by various mechanisms: haemolysis, neutrophil activation, ischaemia reperfusion injury, reoxygenation injury, or depletion of the endogenous antioxidants. The immature myocardium is more vulnerable to reactive oxygen species because of developmental differences compared to the adult heart but also because of associated congenital heart diseases that can deplete its antioxidant reserve. Oxidative stress can be manipulated by various interventions: exogenous antioxidants, use of steroids, cardioplegia, blood prime strategies, or miniaturisation of the cardiopulmonary bypass circuit. However, it is unclear if modulation of the redox pathways can alter clinical outcomes. Further studies powered to look at clinical outcomes are needed to define the role of oxidative stress in paediatric patients.

Background: The Society of Thoracic Surgeons and European System for Cardiac Operative Risk Evaluation (EuroSCORE) II risk scores are the most commonly used risk prediction models for in-hospital mortality after adult cardiac surgery. However, they are prone to miscalibration over time and poor generalization across data sets; thus, their use remains controversial. Despite increased interest, a gap in understanding the effect of data set drift on the performance of machine learning (ML) over time remains a barrier to its wider use in clinical practice. Data set drift occurs when an ML system underperforms because of a mismatch between the data it was developed from and the data on which it is deployed. Objective: In this study, we analyzed the extent of performance drift using models built on a large UK cardiac surgery database. The objectives were to (1) rank and assess the extent of performance drift in cardiac surgery risk ML models over time and (2) investigate any potential influence of data set drift and variable importance drift on performance drift. Methods: We conducted a retrospective analysis of prospectively, routinely gathered data on adult patients undergoing cardiac surgery in the United Kingdom between 2012 and 2019. We temporally split the data 70:30 into a training and validation set and a holdout set. Five novel ML mortality prediction models were developed and assessed, along with EuroSCORE II, for relationships between and within variable importance drift, performance drift, and actual data set drift. Performance was assessed using a consensus metric. Results: A total of 227,087 adults underwent cardiac surgery during the study period, with a mortality rate of 2.76% (n=6258). There was strong evidence of a decrease in overall performance across all models (P<.0001). Extreme gradient boosting (clinical effectiveness metric [CEM] 0.728, 95% CI 0.728-0.729) and random forest (CEM 0.727, 95% CI 0.727-0.728) were the overall best-performing models, both temporally and nontemporally. EuroSCORE II performed the worst across all comparisons. Sharp changes in variable importance and data set drift from October to December 2017, from June to July 2018, and from December 2018 to February 2019 mirrored the effects of performance decrease across models. Conclusions: All models show a decrease in at least 3 of the 5 individual metrics. CEM and variable importance drift detection demonstrate the limitation of logistic regression methods used for cardiac surgery risk prediction and the effects of data set drift. Future work will be required to determine the interplay between ML models and whether ensemble models could improve on their respective performance advantages.

Minimally invasive aortic valve replacement (AVR) aims to preserve the sternal integrity and improve postoperative outcomes. In low risk patients, this technique can be achieved with comparable mortality to the conventional approach and there is evidence of possible reduction in intensive care and hospital length of stay, transfusion requirement, renal dysfunction, improved respiratory function and increased patient satisfaction. In this review, we aim to asses if these benefits can be transferred to the high risk patient groups. We therefore, discuss the available evidence for the following high risk groups: elderly patients, re-operative surgery, poor lung function, pulmonary hypertension, obesity, concomitant procedures and high risk score cohorts.

Background: Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse, because of their narrative structure. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. Objectives: To develop an NLP-based approach for extracting and categorising data from echocardiography reports by accurately converting continuous (e.g., LVOT VTI, AV VTI and TR Vmax) and discrete (e.g., regurgitation severity) outcomes in a semi-structured narrative format into a structured and categorised format, allowing for future research or clinical use. Methods: 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and was iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Results: Continuous outcomes including LVOT VTI, AV VTI and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC = 1.00, p < 0.05) alongside high R² values, demonstrating an ideal alignment between the NLP system and clinicians. A good level (ICC = 0.75–0.9, p < 0.05) of inter-rater reliability was observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E’ Velocity, PV Vmax, Sinuses of Valsalva and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. Conclusions: The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to a structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

The current Covid‐19 pandemic is a significant global health threat. The outbreak has profoundly affected all healthcare professionals, including heart surgeons. To adapt to these exceptional circumstances, cardiac surgeons had to change their practice significantly. We herein discuss the challenges and broad implications of the Covid‐19 pandemic from the perspective of the heart surgeons.