Background Surgical management of coexisting cardiac disease and extra‐cranial carotid artery disease is a controversial area of debate. Thus, in this challenging scenario, risk stratification may play a key role in surgical decision making. Aim To report the results of single‐stage coronary/valve surgery (CVS) and carotid endarterectomy (CEA), and to identify predictive factors associated with 30‐day mortality. Methods This was a multicenter, retrospective study of prospectively maintained data from three academic tertiary referral hospitals. For this study, only patients treated with single‐stage CVS, meaning coronary artery bypass surgery or valve surgery, and CEA between March 1, 2000 and March 30, 2020, were included. Primary outcome measure of interest was 30‐day mortality. Secondary outcomes were neurologic events rate, and a composite endpoint of postoperative stroke/death rate. Results During the study period, there were 386 patients who underwent the following procedures: CEA with isolated coronary artery bypass graft in 243 (63%) cases, with isolated valve surgery in 40 (10.4%), and combination of coronary artery bypass grafting and valve surgery in 103 (26.7%). Postoperative neurologic event rate was 2.6% (n = 10) which includes 5 (1.3%) transient ischemic attacks and 5 (1.3%) strokes (major n = 3, minor n = 2). The 30‐day mortality rate was 3.9% (n = 15). Predictors of 30‐day mortality included preoperative left heart insufficiency (odds ratio [OR]: 5.44, 95% confidence interval [CI]: 1.63–18.17, p = .006), and postoperative stroke (OR: 197.11, 95% CI: 18.28–2124.93, p < .001). No predictor for postoperative stroke and for composite endpoint was identified. Conclusions Considering that postoperative stroke rate and mortality was acceptably low, single‐stage approach is an effective option in such selected high‐risk patients.

Background Coronary artery disease (CAD) is the single leading cause of mortality, premature death, and morbidity worldwide. Artificial intelligence (AI) could help identify markers present within first-line diagnostic imaging routinely performed in patients referred for suspected angina, such as chest radiographs. Purpose To train, test, and validate a deep learning (DL) algorithm for detecting the presence of significant CAD based on chest radiographs. Methods Data of patients undergoing chest radiography and coronary angiography were retrospectively analysed. A deep convolutional neural network (DCNN) was designed to detect significant CAD from the patient posteroanterior/anteroposterior chest radiograph. The DCNN was trained for binary classification of severe CAD absence/presence (at least one diseased coronary vessel with ≥70% stenosis). Coronary angiography reports were used as the ground truth. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the DCNN were calculated. Multivariate analysis was performed to identify independent correlation among the presence of significant CAD (dependent variable), DCNN prediction, and CAD risk factors. Results Information of 7728 patients referred for suspected angina was reviewed. Severe CAD was present in 4482 patients (58%; 1% left main, 28% one vessel, 16% two vessels, and 12% 3 vessels). Patients were randomly divided for training (70%; n=5454) and fine-tuning/testing (10%; n=773) of the algorithm. Internal validation was performed with the remaining patients (20%; n=1501). At binary logistic regression, the DCNN prediction was the strongest independent determinant of severe CAD (p&lt;0.0001; OR: 52.8; CI: 25.1–110.9). Age (p=0.008; OR: 1.01; CI: 1.0–1.02) and Diamond-Forrester score (p&lt;0.0001; OR: 1.022; CI: 1.018–1.026) were also independently related to CAD, although with lower significance and odds-ratios. Using an operating cut-point with high sensitivity, the DCNN had a sensitivity of 0.90 and specificity of 0.31 to detect significant CAD in the internal validation group (AUC 0.73; 95% CI DeLong, 0.69–0.76). Adding to the AI chest radiograph interpretation, patient age and angina status improved the prediction (AUC 0.77; 95% CI DeLong, 0.74–0.80). Conclusion The chest radiograph is ubiquitous and carries a plethora of information concerning the patient's health status, including direct and indirect signs of CAD. Our DL algorithm can predict, with high sensitivity, the presence of severe CAD in patients referred for suspected angina. It could be used to pre-test significant CAD probability in outpatient clinics, emergency room settings, and CAD screening in more extensive settings. Further studies are required to externally validate the algorithm and develop a clinically applicable tool. Funding Acknowledgement Type of funding sources: None.

INTRODUCTION Situs inversus totalis, dextrocardia with interrupted inferior vena cava and azygos vein continuation concomitant with symptomatic atrial fibrillation requiring ablation. This case was deemed not suitable for percutaneous ablation due to anatomic variations and the lack of case reports in literature. METHODS AND RESULTS We performed bilateral thoracoscopic epicardial ablation and epicardial left atrial appendage exclusion. The direct vision allowed for a complete box lesion set with bipolar radiofrequency device. Patient remained in sinus rhythm at 12-months follow-up. CONCLUSION Surgical thoracoscopic epicardial ablation is safe and effective also in congenital defects. Multidisciplinary expertise can offer minimally invasive ablation treatments.