Trends in sex-specific differences following aortic arch repair: results from the Canadian Thoracic Aortic Collaborative.

Background: Previous data have shown that sex-related differences exist in aortic arch surgery, with female patients experiencing worse outcomes. Over time, as surgical techniques and strategies have improved, these improvements have benefitted female patients. Using a multicenter national aortic registry from the Canadian Thoracic Aortic Collaborative (CTAC), we aimed to determine the relationship between sex and outcomes following aortic arch repair and to examine how these have changed over time. Methods: The multicenter prospective CTAC database of all aortic procedures performed under circulatory arrest from participating centers across Canada (n=9) was used. Patients were included who underwent elective or urgent/emergency arch reconstruction under circulatory arrest from 2002 to 2021. The primary composite endpoint was defined as the occurrence of one of the following endpoints: in-hospital mortality, stroke, dialysis-dependent renal failure, deep sternal wound infection, reoperation, or prolonged ventilation of >40 hours. Secondary endpoints included in-hospital mortality, in-hospital stroke, and a modified version of the Society of Thoracic Surgeons-defined composite endpoint for mortality and major morbidity (MMOM). Results: A total of 2,592 patients who underwent aortic arch repair between 2002 and 2021 (31.4% female and 68.6% male patients). Operative mortality decreased through the study period for female patients. No change in operative mortality was observed in male patients or following elective repair. The composite endpoint improved for female patients over time in both elective and urgent surgery, while for male patients, rates improved for elective surgery and remained stable for urgent. Ultimately, female sex was not an independent predictor of adverse outcomes following aortic arch repair. Conclusions: Our results are congruent with existing data and are highly encouraging. It shows that multilevel improvements in our approach to aortic arch surgery have helped to serve female patients who were previously disadvantaged.

Machine learning and decision making in aortic arch repair.

BACKGROUND: Decision making during aortic arch surgery regarding cannulation strategy and nadir temperature are important in reducing risk, and there is a need to determine the best individualized strategy in a data-driven fashion. Using machine learning (ML), we modeled the risk of death or stroke in elective aortic arch surgery based on patient characteristics and intraoperative decisions. METHODS: The study cohort comprised 1323 patients from 9 institutions who underwent an elective aortic arch procedure between 2002 and 2021. A total of 69 variables were used in developing a logistic regression and XGBoost ML model trained for binary classification of mortality and stroke. Shapely additive explanations (SHAP) values were studied to determine the importance of intraoperative decisions. RESULTS: During the study period, 3.9% of patients died and 5.4% experienced stroke. XGBoost (area under the curve [AUC], 0.77 for death, 0.87 for stroke) demonstrated better discrimination than logistic regression (AUC, 0.65 for death, 0.75 for stroke). From SHAP analysis, intraoperative decisions are 3 of the top 20 predictors of death and 6 of the top 20 predictors of stroke. Predictor weights are patient-specific and reflect the patient's preoperative characteristics and other intraoperative decisions. Patient-level simulation also demonstrates the variable contribution of each decision in the context of the other choices that are made. CONCLUSIONS: Using ML, we can more accurately identify patients at risk of death and stroke, as well as the strategy that better reduces the risk of adverse events compared to traditional prediction models. Operative decisions made may be tailored based on a patient's specific characteristics, allowing for maximized, personalized benefit.

Mixed aortic stenosis and regurgitation: a clinical conundrum.

Mixed aortic stenosis (AS) and aortic regurgitation (AR) is the most frequent concomitant valve disease worldwide and represents a heterogeneous population ranging from mild AS with severe AR to mild AR with severe AS. About 6.8% of patients with at least moderate AS will also have moderate or greater AR, and 17.9% of patients with at least moderate AR will suffer from moderate or greater AS. Interest in mixed AS/AR has increased, with studies demonstrating that patients with moderate mixed AS/AR have similar outcomes to those with isolated severe AS. The diagnosis and quantification of mixed AS/AR severity are predominantly echocardiography-based, but the combined lesions lead to significant limitations in the assessment. Aortic valve peak velocity is the best parameter to evaluate the combined haemodynamic impact of both lesions, with a peak velocity greater than 4.0 m/s suggesting severe mixed AS/AR. Moreover, symptoms, increased left ventricular wall thickness and filling pressures, and abnormal left ventricular global longitudinal strain likely identify high-risk patients who may benefit from closer follow-up. Although guidelines recommend interventions based on the predominant lesion, some patients could potentially benefit from earlier intervention. Once a patient is deemed to require intervention, for patients receiving transcatheter valves, the presence of mixed AS/AR could confer benefit to those at high risk of paravalvular leak. Overall, the current approach of managing patients based on the dominant lesion might be too reductionist and a more holistic approach including biomarkers and multimodality imaging cardiac remodelling and inflammation data might be more appropriate.

Artificial intelligence for the echocardiographic assessment of valvular heart disease.

Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.

Machine learning as a new frontier in mitral valve surgical strategy.

BACKGROUND: One of the surgical options available for ischemic mitral regurgitation (MR) is mitral valve repair but is limited by recurrent regurgitation as it is experienced by a significant percentage of patients and has a negative impact on patient outcomes. Efforts to model and identify predictors of recurrent MR rely on complicated echocardiographic and clinical measurements that are subjective and not routinely collected. AIMS: Kachroo et al. approached this problem in a unique way by using the STS database and machine learning (ML) to develop models that predict recurrent MR or death at 1 year. DISCUSSION: The STS database contains many routinely collected demographic and clinical parameters but requires a methodology, such as ML, that will accommodate collinearity and the unknown significance of many predictors. Kachroo et al. developed three good ML models with the area under curve 0.72-0.75. Data-driven selection of important predictors showed that three revascularization targets, peripheral vascular disease, and use of ß-blockers are most predictive of recurrent MR. CONCLUSION: We applaud the authors for pioneering a novel methodology and paving the way for a bright future in ML which includes integrating medical imaging, waveform, and genomic data to practice personalized medicine for our patients.

The cardiac surgeon's guide to artificial intelligence.

PURPOSE OF REVIEW: Artificial intelligence is the ability for machines to perform intelligent tasks. Artificial intelligence is already penetrating many aspects of medicine including cardiac surgery. Here, we offer a platform introduction to artificial intelligence for cardiac surgeons to understand the implementations of this transformative tool. RECENT FINDINGS: Artificial intelligence has contributed greatly to the automation of cardiac imaging, including echocardiography, cardiac computed tomography, cardiac MRI and most recently, in radiomics. There are also several artificial intelligence based clinical prediction tools that predict complex outcomes after cardiac surgery. Waveform analysis, specifically, automated electrocardiogram analysis, has seen significant strides with promise in wearables and remote monitoring. Experimentally, artificial intelligence has also entered the operating room in the form of augmented reality and automated robotic surgery. SUMMARY: Artificial intelligence has many potential exciting applications in cardiac surgery. It can streamline physician workload and help make medicine more human again by placing the physician back at the bedside. Here, we offer cardiac surgeons an introduction to this transformative tool so that they may actively participate in creating clinically relevant implementations to improve our practice.

Ventricular Standstill in a Patient With a Left Ventricular Assist Device.

A 58-year-old woman had medically refractory heart failure due to idiopathic dilated cardiomyopathy. She underwent tricuspid repair and left ventricular assist device implantation for inotropic-dependent heart failure. Because of severe right ventricular dysfunction, she experienced progressive bradycardia and ventricular asystole with electrocardiographic and echocardiographic standstill. Despite the lack of native cardiac activity, she maintained end-organ perfusion with inotropic support until she underwent successful transplantation. This report highlights a case of mechanical circulatory support with an isolated left ventricular assist device implantation even in the absence of native right ventricular function.

Web-Based Tools for Educating Caregivers About Childhood Fever: A Randomized Controlled Trial.

OBJECTIVES: Fever is a common reason for an emergency department visit and misconceptions abound. We assessed the effectiveness of an interactive Web-based module (WBM), read-only Web site (ROW), and written and verbal information (standard of care [SOC]) to educate caregivers about fever in their children. METHODS: Caregivers in the emergency department were randomized to a WBM, ROW, or SOC. Primary outcome was the gain score on a novel questionnaire testing knowledge surrounding measurement and management of fever. Secondary outcome was caregiver satisfaction with the interventions. RESULTS: There were 77, 79, and 77 participants in the WBM, ROW, and SOC groups, respectively. With a maximum of 33 points, Web-based interventions were associated with a significant mean (SD) pretest to immediate posttest gain score of 3.5 (4.2) for WBM (P < 0.001) and 3.5 (4.1) for ROW (P < 0.001) in contrast to a nonsignificant gain score of 0.1 (2.7) for SOC. Mean (SD) caregiver satisfaction scores (out of 32) for the WBM, ROW, and SOC groups were 22.6 (3.2), 20.7 (4.3), and 17 (6.2), respectively. All groups were significantly different from one another in the following rank: WBM > ROW > SOC (P < 0.001). CONCLUSIONS: Web-based interventions are associated with significant improvements in caregiver knowledge about fever and high caregiver satisfaction. These interventions should be used to educate caregivers pending the demonstration of improved patient-centered outcomes.

Multiple Arterial Grafting Is Associated With Better Outcomes for Coronary Artery Bypass Grafting Patients.

BACKGROUND: Observational studies have shown better survival in patients undergoing coronary artery bypass grafting (CABG) with 2 arterial grafts compared with 1. However, whether a third arterial graft is associated with incremental benefit remains uncertain. We sought to analyze the outcomes of 3 versus 2 arterial grafts during CABG. As a secondary objective, we compared CABG with 2 or 3 arterial grafts (multiple arterial grafts [MAG]) with CABG using a single arterial graft (SAG). METHODS: Retrospective cohort analyses of all patients undergoing primary isolated CABG in Ontario, Canada, from October 2008 to March 2016. Propensity score matching was performed between patients with 3 arterial grafts (3Art group) versus 2 (2Art group). The primary outcome was time to first event of a composite of death, myocardial infarction, stroke, and repeat revascularization (major adverse cardiac and cerebrovascular events). Additional analyses were performed to evaluate the association between MAG versus SAG and long-term outcomes using propensity score matching. RESULTS: Fifty thousand, two hundred thirty patients underwent isolated CABG during our study period; 3044 (6.1%) and 8253 (16.4%) patients had 3 and 2 arterial grafts, respectively, resulting in 2789 propensity score matching pairs for the primary analyses. Mean and maximum follow-up was 4.2 and 8.5 years, respectively. Radial artery grafting was more common in the 3Art versus 2Art group (79.3% versus 65.6%, P<0.01). In-hospital outcomes were not significantly different, including death (3Art 0.8% versus 2Art 0.5%, P=0.26). Up to 8 years, there were no differences in major adverse cardiac and cerebrovascular events (3Art 27%, 95% confidence interval [CI], 24% to 30% versus 2Art 25%, 95% CI, 22% to 28%; hazard ratio [HR], 1.08, 95% CI, 0.94-1.25), death (HR, 1.08; 95% CI, 0.90-1.29), myocardial infarction (HR, 1.15; 95% CI, 0.87-1.51), stroke (HR, 1.39; 95% CI, 0.95-2.06), or repeat revascularization (HR, 1.04; 95% CI, 0.82-1.32). When evaluating MAG versus SAG, 8629 patient pairs were formed using propensity score matching. At 8 years, cumulative incidences of major adverse cardiac and cerebrovascular events (HR, 0.82, 95% CI, 0.77-0.88), survival (HR, 0.80; 95% CI, 0.73-0.88), repeat revascularization (HR, 0.79; 95% CI, 0.69-0.90), and myocardial infarction (HR, 0.83; 95% CI, 0.72-0.97) were superior in the MAG group. CONCLUSIONS: CABG with 3 arterial grafts was not associated with increased in-hospital death nor with better clinical outcomes at 8-year follow-up, compared with CABG with 2 arterial grafts. MAG was associated with superior outcomes compared with SAG.

Rigid Plate Fixation Versus Wire Cerclage for Sternotomy After Cardiac Surgery: A Meta-Analysis.

BACKGROUND: Traditionally, wire cerclage has been used to reapproximate the sternum after sternotomy. Recent evidence suggests that rigid plate fixation for sternal closure may reduce the risk of sternal complications. METHODS: The Medline and Embase databases were searched from inception to February 2017 for studies that compared rigid plate fixation with wire cerclage for cardiac surgery patients undergoing sternotomy. Random effects meta-analysis compared rates of sternal complications (primary outcome, defined as deep or superficial sternal wound infection, or sternal instability), early mortality, and length of stay (secondary outcomes). RESULTS: Three randomized controlled trials (n = 427) and five unadjusted observational studies (n = 1,025) met inclusion criteria. There was no significant difference in sternal complications with rigid plate fixation at a median of 6 months' follow-up (incidence rate ratio 0.51, 95% confidence interval [CI]: 0.20 to 1.29, p = 0.15) overall, but a decrease when including only patients at high risk for sternal complications (incidence rate ratio 0.23, 95% CI: 0.06 to 0.89, p = 0.03; two observational studies). Perioperative mortality was reduced favoring rigid plate fixation (relative risk 0.40, 95% CI: 0.28 to 0.97, p = 0.04; four observational studies and one randomized controlled trial). Length of stay was similar overall (mean difference -0.77 days, 95% CI: -1.65 to +0.12, p = 0.09), but significantly reduced with rigid plate fixation in the observational studies (mean difference -1.34 days, 95% CI: -2.05 to -0.63, p = 0.0002). CONCLUSIONS: This meta-analysis, driven by the results of unmatched observational studies, suggests that rigid plate fixation may lead to reduced sternal complications in patients at high risk for such events, improved perioperative survival, and decreased hospital length of stay. More randomized controlled trials are required to confirm the potential benefits of rigid plate fixation for primary sternotomy closure.