Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study.

INTRODUCTION: Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these. OBJECTIVE: To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD. METHODS: We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling. RESULTS: 499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides. CONCLUSIONS: Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis.

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.

Impact of explanted valve type on aortic valve reoperations: nationwide UK experience.

OBJECTIVES: This nationwide retrospective cohort study assessed the impact of the explanted valve type on reoperative outcomes in aortic valve surgery within the UK over a 23-year period. METHODS: Data were sourced from the National Institute for Cardiovascular Outcomes Research (NICOR) database. All patients undergoing first-time isolated reoperative aortic valve replacement between 1996 and 2019 in the UK were included. Concomitant procedures, homograft implantation or aortic root enlargement were excluded. Propensity score matching was utilized to compare outcomes and risk factors for in-hospital mortality was evaluated through multivariable logistic regression. Final model selection was conducted using Akaike Information Criterion through bootstrapping. The primary end point was in-hospital mortality, and secondary end points included postoperative morbidities. RESULTS: Out of 2371 patients, 24.9% had mechanical and 75% had bioprosthetic valves implanted during the primary procedure. Propensity matched groups of 324 patients each, were compared. In-hospital mortality for mechanical and bioprosthetic valve explants was 7.1% and 5.9%, respectively (P = 0.632). On multivariable logistic regression analysis, valve type was not a risk factor for mortality [odds ratio (OR) 0.62, 95% confidence interval (CI) 0.37-1.05; P = 0.1]. Age (OR 1.03, 95% CI 1.01-1.05; P < 0.05), left ventricular ejection fraction (OR 1.62, 95% CI 1.08-2.42; P < 0.05), creatinine ≥ 200 mg/dl (OR 2.21, 95% CI 1.17-4.04; P < 0.05) and endocarditis (OR 2.66, 95% CI 1.71-4.14; P < 0.05) emerged as risk factors for mortality. CONCLUSIONS: The type of valve initially implanted (mechanical or bioprosthetic) did not determine mortality. Instead, age, left ventricular ejection fraction, renal impairment and endocarditis were significant risk factors for in-hospital mortality.

Corrigendum: Patterns of cytokine release and association with new onset of post-cardiac surgery atrial fibrillation.

[This corrects the article DOI: 10.3389/fsurg.2023.1205396.].

Trend in mitral valve prostheses of choice and early outcomes in the United Kingdom.

OBJECTIVE: Despite the superiority of mitral valve repair, surgical mitral valve replacement (SMVR) remains an important intervention for patients with valve stenosis, infective endocarditis and complex mitral valve degeneration. There has been an increasing popularity in the worldwide use of biological valves due to the avoidance of long-term anti-coagulation and recent advancements in transcatheter techniques. We aim to evaluate the trend, early clinical outcomes and the choice of prostheses use in isolated SMVR over a 23 years period in the United Kingdom. METHODS: All patients (n = 13,147) who underwent elective or urgent isolated SMVR from March 1996 to April 2019 were identified from the National Adult Cardiac Surgery Audit database. Trends in clinical outcomes, predicted/observed mortality of patients and the utilization of biological prostheses across 5 different age groups: <50, 50-59, 60-69, 70-79 and ≥80 years old were investigated. Early clinical outcomes associated with the use of mechanical and biological mitral valve prostheses in patients between the age of 60-70 years old were analysed. RESULTS: The number of isolated SMVR performed has remained stable with approximately 600 cases annually since 2010. The in-hospital/30-day mortality rate has decreased from 7.41% (1996) to 3.92% (2018), despite the EuroScore II increasing from 1.42% in 1996 to 2.43% in 2018. Biological prostheses usage increased across all age group, and particularly in the 60-69 and 70-79 group, from 17.86% and 53.85% in 1996 to 48.85% and 82.38% in 2018, respectively. The use of mechanical prostheses was reduced in patients between the age of 50-59 from 100% in 1996 to 80.65% in 2018. There were no differences in short term outcomes among patients aged 60-70 years who received either a biological or mechanical prostheses. CONCLUSION: There has been a significant reduction in surgical mitral valve replacement early in-hospital mortality, despite an observed increase in the risk profile of patients over 23 years. A shifting trend in valve replacement choices was observed with a rise in the use of biological prostheses, particularly within the 60-69 and 70-79 age group. Early in hospital outcomes for patients aged 60-70 were not determined by the implanted valve type.

Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.

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.

Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study.

Objective: The introduction of new clinical risk scores (e.g. European System for Cardiac Operative Risk Evaluation (EuroSCORE) II) superseding original scores (e.g. EuroSCORE I) with different variable sets typically result in disparate datasets due to high levels of missingness for new score variables prior to time of adoption. Little is known about the use of ensemble learning to incorporate disparate data from legacy scores. We tested the hypothesised that Homogenenous and Heterogeneous Machine Learning (ML) ensembles will have better performance than ensembles of Dynamic Model Averaging (DMA) for combining knowledge from EuroSCORE I legacy data with EuroSCORE II data to predict cardiac surgery risk. Methods: Using the National Adult Cardiac Surgery Audit dataset, we trained 12 different base learner models, based on two different variable sets from either EuroSCORE I (LogES) or EuroScore II (ES II), partitioned by the time of score adoption (1996-2016 or 2012-2016) and evaluated on holdout set (2017-2019). These base learner models were ensembled using nine different combinations of six ML algorithms to produce homogeneous or heterogeneous ensembles. Performance was assessed using a consensus metric. Results: Xgboost homogenous ensemble (HE) was the highest performing model (clinical effectiveness metric (CEM) 0.725) with area under the curve (AUC) (0.8327; 95% confidence interval (CI) 0.8323-0.8329) followed by Random Forest HE (CEM 0.723; AUC 0.8325; 95%CI 0.8320-0.8326). Across different heterogenous ensembles, significantly better performance was obtained by combining siloed datasets across time (CEM 0.720) than building ensembles of either 1996-2011 (t-test adjusted, p = 1.67×10-6) or 2012-2019 (t-test adjusted, p = 1.35×10-193) datasets alone. Conclusions: Both homogenous and heterogenous ML ensembles performed significantly better than DMA ensemble of Bayesian Update models. Time-dependent ensemble combination of variables, having differing qualities according to time of score adoption, enabled previously siloed data to be combined, leading to increased power, clinical interpretability of variables and usage of data.

Trend and early clinical outcomes of off-pump coronary artery bypass grafting in the UK.

OBJECTIVES: The popularity of off-pump coronary artery bypass grafting (CABG) varies across the world, ranging from 20% in Europe and the USA to 56% in Asia. We present the trend and early clinical outcomes in off pump in the UK. METHODS: All patients who underwent elective or urgent isolated CABG from 1996 to 2019 were extracted from the National Adult Cardiac Surgery Audit database. The trend in operating surgeons and units volume and training in off pump were analysed. Early clinical outcomes between off- and on-pump CABG were compared using propensity score matching. RESULTS: A total of 351 422 patients were included. The overall off-pump rate during the study period was 15.17%, it peaked in 2008 (19.8%), followed by a steady decreased to 2018 (7.63%). Its adoption varied across centres and surgeons, ranging from <1% to 48.36% and <1% to 85.5%, respectively, of total cases performed. After propensity score matching for the period 1996-2019, off pump, when compared to on pump, was associated with a lower in-hospital/30-day mortality (1.2% vs 1.5%, P < 0.001), return to theatre (3.7% vs 4.5%, P < 0.001), cerebrovascular accident (transient ischaemic attack: 0.3% vs 0.6%, stroke: 0.3% vs 0.6%, P < 0.001) and deep sternal wound infection (0.8% vs 1.2%, P ≤ 0.001). In a sub-analysis from the introduction of EuroScore II (2012-2019), there were no differences in-hospital/30-day mortality (1.0% vs 1.0%, P = 0.71). However, on pump, had a higher return to theatre (4.2% vs 2.7%, P < 0.001), cerebrovascular accident (transient ischaemic attack: 0.4% vs 0.2%, stroke: 0.5% vs 0.3%, P = 0.003) and deep sternal wound infection (1.0% vs 0.6%, P = 0.004). CONCLUSIONS: Our data show a decreasing trend in the use of off pump in the UK since 2008. This is likely to be multifactorial and raises the question of whether it should be a specialized revascularization technique.

Patterns of cytokine release and association with new onset of post-cardiac surgery atrial fibrillation.

Introduction: Postoperative Atrial Fibrillation (POAF) is a common complication of cardiac surgery, associated with increased mortality, stroke risk, cardiac failure and prolonged hospital stay. Our study aimed to assess the patterns of release of systemic cytokines in patients with and without POAF. Methods: A post-hoc analysis of the Remote Ischemic Preconditioning (RIPC) trial, including 121 patients (93 males and 28 females, mean age of 68 years old) who underwent isolated coronary artery bypass grafting (CABG) and aortic valve replacement (AVR). Mixed-effect models were used to analyze patterns of release of cytokines in POAF and non-AF patients. A logistic regression model was used to assess the effect of peak cytokine concentration (6 h after the aortic cross-clamp release) alongside other clinical predictors on the development of POAF. Results: We found no significant difference in the patterns of release of IL-6 (p = 0.52), IL-10 (p = 0.39), IL-8 (p = 0.20) and TNF-α (p = 0.55) between POAF and non-AF patients. Also, we found no significant predictive value in peak concentrations of IL-6 (p = 0.2), IL-8 (p = >0.9), IL-10 (p = >0.9) and Tumour Necrosis Factor Alpha (TNF-α)(p = 0.6), however age and aortic cross-clamp time were significant predictors of POAF development across all models. Conclusions: Our study suggests no significant association exists between cytokine release patterns and the development of POAF. Age and Aortic Cross-clamp time were found to be significant predictors of POAF.

Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database.

OBJECTIVES: To perform a systematic comparison of in-hospital mortality risk prediction post-cardiac surgery, between the predominant scoring system-European System for Cardiac Operative Risk Evaluation (EuroSCORE) II, logistic regression (LR) retrained on the same variables and alternative machine learning techniques (ML)-random forest (RF), neural networks (NN), XGBoost and weighted support vector machine. METHODS: Retrospective analyses of prospectively routinely collected data on adult patients undergoing cardiac surgery in the UK from January 2012 to March 2019. Data were temporally split 70:30 into training and validation subsets. Mortality prediction models were created using the 18 variables of EuroSCORE II. Comparisons of discrimination, calibration and clinical utility were then conducted. Changes in model performance, variable-importance over time and hospital/operation-based model performance were also reviewed. RESULTS: Of the 227 087 adults who underwent cardiac surgery during the study period, there were 6258 deaths (2.76%). In the testing cohort, there was an improvement in discrimination [XGBoost (95% confidence interval (CI) area under the receiver operator curve (AUC), 0.834-0.834, F1 score, 0.276-0.280) and RF (95% CI AUC, 0.833-0.834, F1, 0.277-0.281)] compared with EuroSCORE II (95% CI AUC, 0.817-0.818, F1, 0.243-0.245). There was no significant improvement in calibration with ML and retrained-LR compared to EuroSCORE II. However, EuroSCORE II overestimated risk across all deciles of risk and over time. The calibration drift was lowest in NN, XGBoost and RF compared with EuroSCORE II. Decision curve analysis showed XGBoost and RF to have greater net benefit than EuroSCORE II. CONCLUSIONS: ML techniques showed some statistical improvements over retrained-LR and EuroSCORE II. The clinical impact of this improvement is modest at present. However the incorporation of additional risk factors in future studies may improve upon these findings and warrants further study.

Risk Factors and Outcomes of Reoperative Surgical Aortic Valve Replacement in the United Kingdom.

BACKGROUND: Mortality after reoperative aortic valve surgery continues to decline but remains high compared with primary isolated replacement. We sought to examine temporal trends, morbidity, and mortality among patients undergoing isolated first-time reoperative aortic valve surgery. METHODS: The study included all patients undergoing reoperative aortic valve surgery in the United Kingdom between January 2007 and March 2019. Patients undergoing isolated reoperative aortic valve replacement (AVR) were compared with a propensity matched cohort of patients undergoing isolated primary AVR. Outcomes measured included inhospital mortality, neurologic dysfunction, postoperative dialysis, deep sternal wound infections, and hospital length of stay. RESULTS: During the study period, 40,858 primary isolated AVRs and 3015 first-time isolated reoperative AVRs were carried out in the United Kingdom. In the propensity matched reoperative group, median age of participants was 69.8 years (60.8-76.2) with median duration between the initial surgery and the reoperation being 7.69 years. Overall mortality was 3.1% (94) for reoperative AVR compared with 1.9% (56) for primary AVR. Mortality of both primary and reoperative AVR declined during the study period. Reoperation, age, New York Heart Association class, and chronic kidney disease were independently associated with early mortality. CONCLUSIONS: Reoperative isolated AVR can be performed with acceptable inhospital mortality and provides a benchmark against which alternative strategies should be compared.

Impact of Preoperative Atrial Fibrillation on In-Hospital Outcomes of Coronary Artery Bypass Grafting.

Perioperative atrial fibrillation (AF) is associated with increased mortality, morbidity, and excess healthcare costs. The objective of our study was to assess if preoperative AF in patients undergoing coronary artery bypass grafting is a predictor of operative mortality, postoperative stroke, and need for postoperative dialysis by interrogating a large registry database. We included all isolated procedures performed between February 1996 and March 2019. We used a generalized linear mixed model to assess the effect of preoperative AF on mortality stroke and the need for postoperative dialysis after adjusting for the relevant confounders derived from EuroSCORE 2. Confounders considered included age, gender, neurological dysfunction, renal dysfunction, recent myocardial infarction, pulmonary disease, unstable angina, NYHA class, pulmonary hypertension, diabetes on insulin and peripheral vascular disease, and urgency of the operation. We treated the hospital and operating consultant as random effect variables. We also performed LV function subgroup analyses to assess the effect of preoperative AF on the outcomes of interest. The incidence of pre-existent AF in the cohort of patients we analyzed (N = 356,040 patients) was 3.5% (N = 12,664). In the unadjusted baseline characteristics, preoperative AF patients had more associated comorbidities. After adjustment, preoperative AF remained a significant predictor of increased mortality (odds ratio [OR]: 1.63, confidence interval [CI] 1.48-1.79, p < 0.001), stroke (OR: 1.33, CI 1.16-1.54, p = 0.001), and need for renal dialysis (OR:1.61, CI 1.46-1.78, p < 0.001). Preoperative AF was a significant predictor of adverse outcomes in patients with moderate and good LV function but not in patients with poor LV function (EF <30%). Our study suggests that preoperative AF is associated with an increased risk for perioperative mortality and stroke in patients undergoing coronary artery bypass grafting.

In-hospital outcomes predictors and trends of redo sternotomy aortic root replacements: insights from a UK registry analysis.

Background: Redo sternotomy aortic root surgery is technically demanding, and the evidence on outcomes is mostly from retrospective, small sample, single-centre studies. We report the trend, early clinical results and outcome predictors of redo aortic root replacement over 20 years in the United Kingdom. Methods: We retrospectively analysed collected data from the UK National Adult Cardiac Surgery Audit (NACSA) on all redo sternotomy aortic root replacements performed between 30th January 1998 and 19th March 2019. We analysed trends in the volume of operations, characteristics of hospital survivors vs. non-survivors, and predictors of in-hospital outcomes. Results: During the study period, 1,107 redo sternotomy aortic root replacements were performed (median age 59, 26% of patients were females). Eighty-four per cent of cases (N = 931) underwent a composite root replacement, 11% (N = 119) had homograft root replacement and valve-sparing root replacement was performed in 5.1% (N = 57) of cases. There was a steady increase in the volume of redo sternotomy root replacements beyond 2006, from an annual volume of 22 procedures in 2006 to 106 procedures in 2017. Hospital mortality was 17% (n = 192), postoperative stroke or TIA occurred in 5.2% (n = 58), and postoperative dialysis was required in 11% (n = 109) of patients. Return to the theatre for bleeding/tamponade was required in 9% (n = 102) and median in-hospital stay was 9 days. Age >59 (OR: 2.99, CI: 1.92-4.65, P < 0.001), recent myocardial infarction (OR: 6.42, CI: 2.24-18.41, P = 0.001) were associated with increased in-hospital mortality. Emergency surgery (OR: 3.95, 2.27-6.86, P < 0.001), surgery for endocarditis (OR: 2.05, CI: 1.26-3.33, P = 0.001), salvage coronary artery bypass grafting (OR: 2.20, CI: 1.37-3.54, P < 0.001), arch surgery (OR: 2.47, CI: 1.30-3.61, P = 0.018) and aortic cross-clamp longer than 169 min (OR: 2.17, CI: 1.00-1.01, P = 0.003) were associated with increased risk of mortality. We found no effect of the centre or surgeon volume on mortality (P > 0.05). Conclusions: Redo sternotomy aortic root replacement still carries significant morbidity and mortality and is sporadically performed across surgeons and centres in the UK.

On- versus off-pump CABG in octogenarians: A propensity-matched analysis from the UK National Database.

INTRODUCTION: Coronary artery bypass grafting (CABG) remains a good revascularization strategy in octogenarians with excellent clinical outcomes and quality of life postoperatively. However, the benefits of off-pump over on-pump CABG in the elderly population are still controversial. We investigated this issue in the UK National Audit database. METHOD: We retrospectively analyzed all octogenarians undergoing nonemergency, isolated CABG from 1996 to 2019. Propensity score matching (PSM) was conducted to adjust for imbalance in the baseline characteristics between the off-pump and on-pump groups. Primary outcome was in-hospital mortality and postoperative cerebrovascular accidents. Secondary outcomes were bleeding requiring reoperation, deep sternal wound infection, and postoperative dialysis. RESULT: A total of 6436 patients were included for analysis. No differences were observed between off- and on-pump group in-hospital mortality (4% vs. 3.8%, p = .89), return to theater rate (5.4% vs. 6.2%, p = .16) and incidence of deep sternal wound infection (1.1% vs. 1.6%, p = .34). However, octogenarian undergoing off-pump CABG were less likely to experience postoperative transient ischemic attack (TIA)/stroke (1.4% vs. 2.3%, p = .004) but more likely to require renal dialysis (4.8% vs. 3.5%, p = .03). CONCLUSION: The data show similar in-hospital mortality in octogenarians regardless of the revascularization technique used. Off-pump when compared with on-pump CABG is associated with a lower incidence in postoperative neurological events but a higher need for renal dialysis.

Diagnostic MALDI-TOF MS can differentiate between high and low toxic Staphylococcus aureus bacteraemia isolates as a predictor of patient outcome.

Staphylococcus aureus bacteraemia (SAB) is a major cause of blood-stream infection (BSI) in both healthcare and community settings. While the underlying comorbidities of a patient significantly contributes to their susceptibility to and outcome following SAB, recent studies show the importance of the level of cytolytic toxin production by the infecting bacterium. In this study we demonstrate that this cytotoxicity can be determined directly from the diagnostic MALDI-TOF mass spectrum generated in a routine diagnostic laboratory. With further development this information could be used to guide the management and improve the outcomes for SAB patients.

Trend and factors associated with multiple arterial revascularization in coronary artery bypass grafting in the UK.

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.

Deep recurrent reinforced learning model to compare the efficacy of targeted local versus national measures on the spread of COVID-19 in the UK.

OBJECTIVES: To prevent the emergence of new waves of COVID-19 caseload and associated mortalities, it is imperative to understand better the efficacy of various control measures on the national and local development of this pandemic in space-time, characterise hotspot regions of high risk, quantify the impact of under-reported measures such as international travel and project the likely effect of control measures in the coming weeks. METHODS: We applied a deep recurrent reinforced learning based model to evaluate and predict the spatiotemporal effect of a combination of control measures on COVID-19 cases and mortality at the local authority (LA) and national scale in England, using data from week 5 to 46 of 2020, including an expert curated control measure matrix, official statistics/government data and a secure web dashboard to vary magnitude of control measures. RESULTS: Model predictions of the number of cases and mortality of COVID-19 in the upcoming 5 weeks closely matched the actual values (cases: root mean squared error (RMSE): 700.88, mean absolute error (MAE): 453.05, mean absolute percentage error (MAPE): 0.46, correlation coefficient 0.42; mortality: RMSE 14.91, MAE 10.05, MAPE 0.39, correlation coefficient 0.68). Local lockdown with social distancing (LD_SD) (overall rank 3) was found to be ineffective in preventing outbreak rebound following lockdown easing compared with national lockdown (overall rank 2), based on prediction using simulated control measures. The ranking of the effectiveness of adjunctive measures for LD_SD were found to be consistent across hotspot and non-hotspot regions. Adjunctive measures found to be most effective were international travel and quarantine restrictions. CONCLUSIONS: This study highlights the importance of using adjunctive measures in addition to LD_SD following lockdown easing and suggests the potential importance of controlling international travel and applying travel quarantines. Further work is required to assess the effect of variant strains and vaccination measures.

Weekday and outcomes of elective cardiac surgery in the UK: a large retrospective database analysis.

OBJECTIVES: Several studies have shown worse outcomes in patients operated on later in the week. We tested this hypothesis in a large UK national audit database in elective patients undergoing adult cardiac surgery. METHODS: We used a generalized additive model to evaluate the effect of the day of the week on the following postoperative outcomes: 30-day mortality, stroke, need for dialysis and return to theatre for bleeding. We have adjusted for the relevant European System for Cardiac Operative Risk Evaluation (EuroSCORE) II covariates, plus responsible consultant, hospital and year of operation and performed subgroup analysis for isolated coronary artery bypass grafting (CABG) procedures. RESULTS: Out of 371 500 patients, 60 555 (16.3%) underwent AVR, 36 553 (9.8%) AVR plus CABG, 238 812 (64.3%) isolated CABG, 26 517 (7.1%) isolated mitral valve repair or replacement and 9063 (2.4%) mitral valve plus CABG. A total of 13 997 (3%) had surgery over the weekend. After covariate adjustment, we found no effect of day of surgery on mortality (P = 0.081), stroke (P = 0.137) and need for postop dialysis (P = 0.732). However, across all operations, there was evidence of a lower rate of return to theatre for bleeding/tamponade at the weekend (P = 0.039). In subgroup analysis of isolated CABG, the day of the week did not affect any outcomes. CONCLUSIONS: We found no effect of the day of the week on risk-adjusted short-term mortality, stroke, and the requirement for postoperative dialysis after elective cardiac surgery. Overall, the patients operated on during the weekdays were less likely to return to theatre for bleeding. In isolated CABG, the day of the week did not affect any outcomes.

Trend and early outcomes in isolated surgical aortic valve replacement in the United Kingdom.

Objective: Surgical aortic valve replacement (SAVR) is traditionally the gold-standard treatment in patients with aortic valve disease. The advancement of transcatheter aortic valve replacement (TAVR) provides an alternative treatment to patients with high surgical risks and those who had previous cardiac surgery. We aim to evaluate the trend, early clinical outcomes, and the choice of prosthesis use in isolated SAVR in the United Kingdom. Methods: All patients (n = 79,173) who underwent elective or urgent isolated surgical aortic valve replacement (SAVR) from 1996 to 2018 were extracted from the National Adult Cardiac Surgery Audit database. Patients who underwent additional procedures and emergency or salvage SAVR were excluded from the study. Trend and clinical outcomes were investigated in the whole cohort. Patients who had previous cardiac surgery, high-risk groups (EuroSCORE II >4%), and predicted/observed mortality were evaluated. Furthermore, the use of biological prostheses in five different age groups, that are <50, 50-59, 60-69, 70-79, and >80, was investigated. Clinical outcomes between the use of mechanical and biological aortic valve prostheses in patients <65 years old were analyzed. Results: The number of isolated SAVR increased across the study period with an average of 4,661 cases performed annually after 2010. The in-hospital/30-day mortality rate decreased from 5.28% (1996) to 1.06% (2018), despite an increasing trend in EuroSCORE II. The number of isolated SAVR performed in octogenarians increased from 596 to 2007 (the first year when TAVR was introduced in the UK) to 872 in 2015 and then progressively decreased to 681 in 2018. Biological prosthesis usage increased across all age groups, particularly in the 60-69 group, from 24.59% (1996) to 81.87% (2018). There were no differences in short-term outcomes in patients <65 years old who received biological or mechanical prostheses. Conclusion: Surgical aortic valve replacement remains an effective treatment for patients with isolated aortic valve disease with a low in-hospital/30-day mortality rate. The number of patients with high-risk and octogenarians who underwent isolated SAVR and those requiring redo surgery has reduced since 2016, likely due to the advancement in TAVR. The use of biological aortic prostheses has increased significantly in recent years in all age groups.

Disparity in clinical outcomes after cardiac surgery between private and public (NHS) payers in England.

BACKGROUND: There is little known about how payer status impacts clinical outcomes in a universal single-payer system such as the UK National Health Service (NHS). The aim of this study was to evaluate the relationship between payer status (private or public) and clinical outcomes following cardiac surgery from NHS providers in England. METHODS: The National Adult Cardiac Surgery Audit (NACSA) registry was interrogated for patients who underwent adult cardiac surgery in England from 2009 to 2018. Information on socioeconomic status were provided by linkage with the Iteration of the English Indices of Deprivation (IoD). The primary outcome was in-hospital mortality. Secondary outcomes included incidence of in-hospital postoperative cerebrovascular accident (CVA), renal dialysis, sternal wound infection, and re-exploration. To assess whether payer status was an independent predictor of in-hospital mortality, binomial generalized linear mixed models (GLMM) were fitted along with 17 items forming the EuroSCORE and the IoD domains. FINDINGS: The final sample consisted of 280,209 patients who underwent surgery in 31 NHS hospitals in England from 2009 to 2018. Of them, 5,967 (2.1%) and 274,242 (97.9%) were private and NHS payers respectively. Private payer status was associated with a lower risk of in-hospital mortality (OR 0.79; 95%CI 0.65 - 0.97;P = 0.026), CVA (OR 0.77; 95%CI 0.60 - 0.99; P = 0.039), need for re-exploration (OR 0.84; 95%CI 0.72 - 0.97; P = 0.017) and with non-significant lower risk of dialysis (OR 0.84; 95%CI 0.69 - 1.02; P = 0.074). Private payer status was found to be independently associated with lower risk of in-hospital mortality in the elective subgroup (OR 0.76; 95%CI 0.61 - 0.96; P = 0.020) but not in the non-elective subgroup (OR 1.01; 95%CI 0.64 - 1.58; P = 0.976). INTERPRETATION: In conclusion, using a national database, we have found evidence of significant beneficial effect of payer status on hospital outcomes following cardiac surgery in favour of private payers regardless their socioeconomic factors.