Beohar, Nirat

ABSTRACT Left bundle branch block (LBBB) is the most common conduction abnormality following transcatheter aortic valve replacement (TAVR) and has been associated with adverse clinical outcomes. While multiple predictors of post‐TAVR LBBB have been proposed, data remain limited. This systematic review and meta‐analysis aims to identify and summarize predictors of new‐onset LBBB post‐TAVR. A systematic literature search was performed to identify studies that reported predictors of new‐onset post‐TAVR LBBB. Extracted data included patient factors, electrocardiographic (ECG) and anatomic parameters, and device‐related factors. A random‐effects model was used to calculate crude risk ratios (RRs), mean differences, and 95% confidence intervals (CI) of the predictors. Of 450 articles screened, 17 studies comprising a total of 6357 patients were included. Among these, 26.4% developed post‐TAVR LBBB. Increased risk was associated with diabetes mellitus (RR: 1.22, p < 0.001), use of 29‐mm valve (RR: 1.59, p < 0.001), and use of Medtronic CoreValve (MCV) (RR: 2.25, p = 0.008). Significant differences were found between patients with and without LBBB in interventricular septal thickness (IVS; by −0.66 mm), membranous septal length (MSL; by −0.9 mm), left ventricular outflow tract (LVOT) diameter (by −0.48 mm), PR interval (by +13.95 ms), and prosthesis implantation depth (by +2.6 mm). Diabetes Mellitus, use of 29‐mm valve or MCV, decreased IVS thickness, shorter MSL, smaller LVOT diameter, increased prosthesis implantation depth, and prolonged PR duration were all associated with an elevated risk of post‐TAVR LBBB. Early identification of these predictors may help reduce the risk of conduction abnormalities after TAVR.

Introduction: Ischemic stroke (IS) causes substantial morbidity and mortality in patients undergoing percutaneous coronary intervention (PCI) with a 5 yr incidence ~ 3%. We sought the test the accuracy of Machine learning (ML) algorithms in predicting IS in patients undergoing PCI. Methods: Mayo Clinic CathPCI registry data were retrospectively analyzed from Jan 2003 - June 2018 including 21,872 patients who underwent PCI. The cohort was randomly divided into a training sample (75%, n=16404) and a unique test sample (25%, n=5468) prior to model generation. The risk prediction model was generated utilizing a random forest algorithm (RF model) on 188 unique variables to predict the risk of IS at 6-month, 1, 2, and 5-year post PCI. Conventional risk factors for stroke were used for logistic regression. The receiver operating characteristic (ROC) curve and area under the curve for the RF and logistic regression models were compared for the test cohort. Results: The mean age was 66.9 ± 12.4 years, and 71% were male. Patient demographics and outcomes are shown in Table 1 . The ROC area under the curve for the RF model was superior compared to the logistic regression model in predicting IS at 6 months, 1,2 and 5 yrs for the test cohort ( Figure 1 .) Conclusions: The RF model accurately predicts short and long term risk of IS and outperforms logistic regression analysis in patients undergoing PCI.

Background: Identifying patients at a high risk of mortality post percutaneous coronary intervention (PCI) is of vital clinical importance. We investigated the utility of machine learning algorithms to predict short and intermediate-term risk of all-cause mortality in patients undergoing PCI. Methods: Patient-level demographics, clinical, electrocardiographic ,echocardiographic and angiographic data from January 2006 to December 2017 were extracted from the Mayo Clinic CathPCI registry and clinical records. For patients with multiple PCI events, data collected at the time of the index PCI was used for analysis. Patients who underwent bailout coronary artery bypass graft surgery (CABG) prior to discharge were excluded. 306 variables were incorporated into random forest machine learning model (RF) to predict all-cause mortality at 6 months and 1 year after PCI. Ten-fold cross-validation repeated five times was used to optimize the hyperparameters and estimate its external performance. The National Cardiovascular Data Registry (NCDR) based logistic regression model was used for comparison. The area under receiver operator characteristic curves (AUC) was calculated to assess the ability of the models to predict all-cause mortality. Results: A total of 17356 unique patients were included for the final analysis after excluding 165 patients who underwent CABG surgery during the index hospitalization. The mean age was 66.9 ± 12.5 years;71% were male. Indications for PCI were ST-elevation myocardial infarction (9.4%), non-ST elevation myocardial infarction (12.9%), unstable angina (17.7%), and stable angina (52.8%) in the cohort. In-hospital, 6-month & 1 year mortality rates were 1.9%,4.2% & 5.8% respectively. The RF model was superior to the NCDR model in predicting inhospital, 6-month, 1 year mortality (p<0.0001) ( Figure 1 ). Conclusion: Machine learning is superior to NCDR model in predicting short and intermediate risk of all-cause mortality post PCI.

Abstract Background Coronary artery disease (CAD) and atrial fibrillation (AF) are commonly associated. Co-treatment with multiple anti-thrombotic agents can increase the risk of bleeding. Purpose We sought to evaluate patient-centered outcomes in patients with AF on double therapy with direct oral anticoagulants (DOACs) compared to patients with standard triple therapy, [a vitamin K antagonist (VKA) plus dual antiplatelet therapy]. Methods We performed an extensive systematic review of the literature and meta-analysis of randomized controlled trials reporting outcomes of patients receiving double therapy with DOACs compared to triple therapy with VKAs in patients with AF undergoing percutaneous coronary intervention (PCI). Patient-centered outcomes were the International Society of Thrombosis and Hemostasis (ISTH) major or clinically relevant non-major bleeding (CRNB), all-cause mortality, major adverse cardiovascular events (MACE), myocardial infarction, stent thrombosis, and stroke. Results Four randomized controlled trials (9602 patients) met our inclusion criteria. Compared to VKAs, DOACs were associated with significantly lower ISTH major bleeding/ CRNB (RR - 0.75, 95% CI: 0.67-0.82, p &lt; 0.00001, I2 = 11%). There were no statistically significant differences in the efficacy outcomes, including myocardial infarction (RR- 0.99, 95% CI :0.79-1.25, p = 0.96, I2 = 0%), stent thrombosis (RR - 0.97, 95% CI: 0.6-1.55, p = 0.89, I2 = 0%), ischemic stroke (RR - 0.76, 95% CI: 0.5-1.15, p = 0.19, I2 = 0%), all-cause mortality (RR - 1.06, 95% CI: 0.85-1.31, p = 0.61, I2 = 0%) and MACE (RR - 1.06, 95% CI: 0.91-1.22, p = 0.97, I2 = 0%). Conclusion Compared with triple therapy with VKAS, double therapy with DOACs is associated with a reduced risk of bleeding and is as effective in patients with AF undergoing PCI. Abstract Figure.

AbstractBackgroundAtrial fibrillation (AF) and coronary artery disease (CAD) are commonly associated. Cotreatment with multiple antithrombotic agents can increase the risk of bleeding. We sought to evaluate patient‐centered outcomes in patients with AF on double therapy with direct oral anticoagulants (DOACs) compared to patients with standard triple therapy, [a vitamin K antagonist (VKA) plus dual antiplatelet therapy].MethodsWe performed a literature search of randomized controlled trials (RCTs) reporting outcomes of patients receiving double therapy with DOACs compared to triple therapy with VKAs in patients with AF undergoing percutaneous coronary intervention (PCI). Patient‐centered outcomes were the International Society of Thrombosis and Hemostasis (ISTH) major or clinically relevant nonmajor bleeding (CRNB), all‐cause mortality, major adverse cardiovascular events (MACE), stent thrombosis, myocardial infarction, and stroke.ResultsFour RCTs (9602 patients) met our inclusion criteria. Compared to VKAs, DOACs were associated with significantly lower ISTH major bleeding/ CRNB (RR: 0.75, 95% CI: 0.67‐0.82, P < .00001, I2 = 11%). There were no statistically significant differences in the efficacy outcomes, including myocardial infarction (RR: 0.99, 95% CI :0.79‐1.25, P = .96), stent thrombosis (RR: 0.97, 95% CI: 0.6‐1.55, P = .89), ischemic stroke (RR: 0.76, 95% CI: 0.5‐1.15, P = .19), all‐cause mortality (RR: 1.06, 95% CI: 0.85‐1.31, P = .61), and MACE (RR: 1.06, 95% CI: 0.91‐1.22, P = .97).ConclusionCompared with triple therapy with VKAS, double therapy with DOACs is associated with a reduced risk of bleeding and is as effective in patients with AF undergoing PCI.

Background Post‐myocardial infarction (MI) ventricular septal defects (PIVSD) are an uncommon but life‐threatening complication of acute MI. Although surgical closure has been the standard of care, mortality, and recurrence of VSD remain high even after emergent surgery. Transcatheter VSD closure (TCC) devices have become an alternative or adjunct to surgical closure. Methods Online database search was performed for studies that included adults with PIVSD who underwent medical treatment (MT) alone, surgical closure (SC) (early or late), and TCC (early, late, or for post‐surgical residual VSD). Results Twenty‐six studies were included with a total of 737 patients who underwent either MT ( N = 100), SC (early ( n = 167), late ( n = 100)), and TCC (early ( n = 176), late ( n = 115), or post‐surgical residual VSD ( n = 79)). The 30‐day mortality among MT group was 92 ± 6.3%, among SC was 61 ± 22.5% (early 56 ± 23%, late 41 ± 30%), and for all TCC patients was 33 ± 24% (early 54 ± 32.7%, late 16 ± 26%), and TCC for post‐surgical residual VSD 11 ± 34.9%. The mortality among overall SC, overall TCC and early TCC groups was significantly lower as compared with the MT ( P < 0.001 for all comparisons). The overall mortality among all TCC, and late TCC groups was significantly lower when compared with the late SC ( P < 0.0001, P < 0.0001, respectively). Conclusion Closure of PIVSD decreases mortality as compared with MT alone and should be attempted as early as possible after diagnosis. Selection of TCC versus SC should be based on factors including complexity of the defect, availability of closure devices, expertise of the operator, and clinical condition of patient.