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.

Introduction: Minimally invasive cardiac surgery has been evolving, with the intention of reducing surgical trauma, improve cosmesis and patient satisfaction. Single dose, crystalloid cardioplegia such as Del Nido cardioplegia and Custoidol solution have been increasingly used to reduce the interruption from repeating cardioplegia dosing to minimise the cardiopulmonary bypass and cross clamp time. However, the best cardioplegia for myocardial protection in adult minimally invasive cardiac surgery remains controversial. We aimed to conduct a meta-analysis to analyse the current evidence in the literature. Method: A systematic review and meta-analysis was performed following the updated 2020 PRISMA guideline. Articles published in the five major electronic databases up 1st of April 2021 were identified and reviewed. The primary outcome was in-hospital or 30-day mortality. Traditional pairwise and Bayesian network meta-analyses were conducted. Results: Nine articles were included in this study. The use of Del Nido cardioplegia was associated with a lower volume of cardioplegia used (Del Nido vs Blood, 1105.62 mL+/-123.47 vs 2569.46 mL+/-1515.52, p <0.001), cardiopulmonary bypass (Del Nido vs Custoidol vs Blood: 91.67+/−14.78 vs 138.05 +/− 21.30 vs 119.38+/−26.91 minutes, p <0.001) and cross-clamp time (Del Nido vs Custoidol vs Blood: 74.99+/−18.55 vs 82.01 +/− 17.28 vs 93.66+/−8.88 minutes, p < 0.001). No differences were observed in the incidence of in-hospital/30-day mortality rate, new onset of atrial fibrillation and stroke. Ranking analysis showed the Custoidol solution has the highest probability to be the first ranked cardioplegia. Conclusion: No differences were found between blood and crystalloid cardioplegia in adult minimally invasive cardiac surgery in several clinical outcomes. The cardioplegia of choice in minimally invasive cardiac surgery remains the surgeons’ decision and preference.

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.

Large language models (LLM), such as Chat Generative Pre-trained Transformer (ChatGPT) and Bard utilise deep learning algorithms that have been trained on a massive data set of text and code to generate human-like responses. Several studies have demonstrated satisfactory performance on postgraduate examinations, including the United States Medical Licensing Examination. We aimed to evaluate artificial intelligence performance in Part A of the intercollegiate Membership of the Royal College of Surgeons (MRCS) examination. The MRCS mock examination from Pastest, a commonly used question bank for examinees, was used to assess the performance of three LLMs: GPT-3.5, GPT 4.0 and Bard. Three hundred mock questions were input into the three LLMs, and the responses provided by the LLMs were recorded and analysed. The pass mark was set at 70%. The overall accuracies for GPT-3.5, GPT 4.0 and Bard were 67.33%, 71.67% and 65.67%, respectively (p = 0.27). The performances of GPT-3.5, GPT 4.0 and Bard in Applied Basic Sciences were 68.89%, 72.78% and 63.33% (p = 0.15), respectively. Furthermore, the three LLMs obtained correct answers in 65.00%, 70.00% and 69.17% of the Principles of Surgery in General questions (p = 0.67). There were no differences in performance in the overall and subcategories among the three LLMs. Our findings demonstrated satisfactory performance for all three LLMs in the MRCS Part A examination, with GPT 4.0 the only LLM that achieved the pass mark set.

OBJECTIVES: Benefits of using multiple arterial grafting (MAG), over single arterial grafting in major adverse cardiac event rates and the need for repeat revascularization, have been widely reported. Several guidelines have recommended the use of MAG in selected patients. We report the trend of MAG in patients undergoing isolated coronary artery bypass grafting (CABG) in the UK. METHODS: This is a retrospective analysis of a prospectively collected UK national database in patients undergoing non-emergency, isolated CABG from 1996 to 2018. Patients were divided into single arterial grafting and MAG, and trends in perioperative characteristics were analysed. RESULTS: A total of 336 321 patients were included, of whom 284 003 (84.44%) received single arterial grafting and 52 318 (15.56%) received MAG. The use of MAG after an initial increase from 1996 to 2001, steadily decreased thereafter, particularly in the use of radial artery. MAG was likely to be performed in younger patients [66.72 (standard deviation: 9.22) vs 62.30 (standard deviation: 10.06), P < 0.001] and males (85% vs 81%, P < 0.001). After propensity score matching, the single arterial grafting group was more likely to undergo on-pump CABG (90% vs 69%, P < 0.001), experienced a lower in-hospital mortality (1.1% vs 1.3%, P < 0.001) and incidence of return to theatre for bleeding (2.5% vs 3.0%, P < 0.001). CONCLUSIONS: Our data show that the use of MAG in CABG in the UK after an initial increase from 1996 to 2001 steadily decreased thereafter until 2018. This is likely to be multifactorial and a better understanding of the main causes may contribute to establishing the best indication for MAG in everyday clinical practice.