A New Insight Into Nonbacterial Thrombotic Endocarditis: A Systematic Review of Cases.

BACKGROUND: Nonbacterial thrombotic endocarditis is characterized by the presence of organized thrombi on cardiac valves, often associated with hypercoagulable states. There is a paucity of data regarding the predictors of mortality in patients with nonbacterial thrombotic endocarditis. Our primary aim was to identify predictors of in-hospital mortality in patients with nonbacterial thrombotic endocarditis. METHODS: A systematic literature review of all published cases and case series was performed until May 2018 according to Preferred Reporting Items for Systematic Review and Meta-analyses statement guidelines. We applied random forest machine learning model to identify predictors of in-patient mortality in patients with nonbacterial thrombotic endocarditis. RESULTS: Our search generated a total of 163 patients (mean age, 46 ± 17 years; women, 69%) with newly diagnosed nonbacterial thrombotic endocarditis. The in-hospital mortality rate in the study cohort was 30%. Among the patients who died in the hospital, initial presentation of pulmonary embolism (12.2 vs. 2.6%), splenic (38.7 vs. 10.5%), and renal (40.8 vs. 9.6%) infarcts were higher compared to patients alive at the time of discharge. Higher rates of malignancy (71.4 vs. 39.4%, P = .0003) and lower rates of antiphospholipid syndrome (8.1 vs. 48.2%, P = .0001) were noted in deceased patients. Random forest machine learning analysis showed that older age, presence of antiphospholipid syndrome, splenic infarct, renal infarct, peripheral thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality. CONCLUSION: Patients admitted with nonbacterial thrombotic endocarditis have a high rate of in-hospital mortality. Factors including older age, presence of antiphospholipid syndrome, splenic/renal infarct, lower limb thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality in patients with nonbacterial thrombotic endocarditis.

Artificial Intelligence Trumps TAVI2-SCORE and CoreValve Score in Predicting 1-Year Mortality Post-Transcatheter Aortic Valve Replacement.

BACKGROUND/PURPOSE: Machine learning has been used to predict procedural risk in patients undergoing various medical interventions and procedures. One-year mortality in patients after Transcatheter Aortic Valve Replacement (TAVR) has a wide range (from 8.5 to 24% in various studies). We sought to apply machine learning to determine predictors of one year mortality in patients undergoing TAVR. METHODS/MATERIALS: A retrospective study of 1055 patients who underwent TAVR (Jan 2014-June 2017) with one-year follow up was completed. Baseline demographics, clinical, electrocardiography (ECG), Computed Tomography (CT) and echocardiography data were abstracted. Variables with near zero variance or ≥50% missing data were excluded. The Gradient Boosting Machine learning (GBM) prediction model included 163 variables and was optimized using 5-fold cross-validation repeated 10-times. The receiver operator characteristic (ROC) for the GBM model was calculated to predict one-year mortality post TAVR, and then compared to the TAVI2-SCORE and CoreValve score. RESULTS: Among 1055 TAVR patients (mean age 80.9 ± 7.9 years, 42% female), 14.02% died at one year. 78% had balloon expandable valves placed. Based on GBM, the ten most predictive variables for one-year survival were cardiac power index, hemoglobin, systolic blood pressure, INR, diastolic blood pressure, body mass index, valve calcium score, serum creatinine, aortic annulus area, and albumin. The area under ROC to predict survival for the GBM model vs TAVI2-SCORE and CoreValve Score was 0.72 (95% CI 0.68-0.78) vs 0.56 (95%CI 0.51-0.62) and 0.53 (95% CI 0.47-0.59) respectively with p < 0.0001. CONCLUSION: The GBM model outperforms TAVI2-SCORE and CoreValve Score in predicting mortality one-year post TAVR.

Comparison of echocardiographic parameters with cardiac magnetic resonance imaging in the assessment of right ventricular function.

INTRODUCTION: The right ventricle (RV) strain measured by speckle tracking (RVS) is an echocardiographic parameter used to assess RV function. We compared RVS to RV fractional area change (FAC%), tricuspid annular plane systolic excursion (TAPSE) and Doppler tissue imaging-derived peak systolic velocity (S') in the assessment of right ventricular (RV) systolic function measured using cardiac magnetic resonance imaging (MRI). METHODS: We enrolled consecutive patients who underwent cardiac MRI between Jan 2012 and Dec 2017 and a transthoracic echocardiogram (TTE) within 1 month of the MRI with no interval event. Baseline clinical characteristics and MRI parameters were extracted from chart review. Echocardiographic parameters were measured prospectively. TTE parameters including RVS, TAPSE, S', and FAC% were tested for accuracy to identify impaired RV EF (EF < 45% & <30%) using receiver operator curves. RESULTS: The study cohort included 500 patients with mean age 55 years ± 18 and peak tricuspid regurgitation velocity 2.7 ± 1.4 m/s. The area under ROC for RVS was 0.69 (95% CI 0.63-0.75) and 0.78 (95% CI 0.70-0.88) to predict RVEF < 45% & RVEF < 30%, respectively. The RV FAC% had second highest accuracy of predicting RVEF among all the TTE parameters tested in study. CONCLUSION: Right ventricular strain is the most accurate echocardiographic method to detect impaired right ventricular systolic function when using MRI as the gold standard.

A Unique Compensatory Mechanism for Total Pulmonary Vein Occlusion Post Atrial Fibrillation Catheter Ablation Visualized by Multimodality Imaging.

Pulmonary vein (PV) stenosis is a rare and serious complication of radiofrequency catheter ablation (RFCA) for atrial fibrillation. However, it can be asymptomatic or mildly symptomatic depending on the severity of the stenosis and the development of compensatory mechanisms. This study provides a detailed description and visualization of a unique type of venous collaterals that bypass the PV stenosis and drain directly in the left atrium alleviating PV stenosis sequelae. This study reports a case of a 61-year-old male who presented with mild dyspnea and fatigue 3 years post atrial fibrillation RFCA. After a thorough evaluation of the case, a redo-ablation was planned. As a part of the preablation workup, a transesophageal echocardiography (TEE), a ventilation-perfusion (V/Q) scan of the lungs, and a chest computed tomography angiogram (CTA) were performed. The TEE revealed total obstruction of the left superior PV, with no color Doppler flow detected. It also showed evidence of multiple collateral flows at the os of the left superior PV. The V/Q scan showed a large perfusion defect involving the entire left upper lobe consistent with a compromised left upper PV flow. The CTA with 3D volume rendering revealed the total occlusion of the left superior PV at its ostium. Moreover, the scan confirmed the pulmonary venous drainage via small collateral channels that was suggested by the TEE.

Does a Gradient-Adjusted Cardiac Power Index Improve Prediction of Post-Transcatheter Aortic Valve Replacement Survival Over Cardiac Power Index?

PURPOSE: Cardiac power (CP) index is a product of mean arterial pressure (MAP) and cardiac output (CO). In aortic stenosis, however, MAP is not reflective of true left ventricular (LV) afterload. We evaluated the utility of a gradient-adjusted CP (GCP) index in predicting survival after transcatheter aortic valve replacement (TAVR), compared to CP alone. MATERIALS AND METHODS: We included 975 patients who underwent TAVR with 1 year of follow-up. CP was calculated as (CO×MAP)/[451×body surface area (BSA)] (W/m²). GCP was calculated using augmented MAP by adding aortic valve mean gradient (AVMG) to systolic blood pressure (CP1), adding aortic valve maximal instantaneous gradient to systolic blood pressure (CP2), and adding AVMG to MAP (CP3). A multivariate Cox regression analysis was performed adjusting for baseline covariates. Receiver operator curves (ROC) for CP and GCP were calculated to predict survival after TAVR. RESULTS: The mortality rate at 1 year was 16%. The mean age and AVMG of the survivors were 81±9 years and 43±4 mm Hg versus 80±9 years and 42±13 mm Hg in the deceased group. The proportions of female patients were similar in both groups (p=0.7). Both CP and GCP were independently associated with survival at 1 year. The area under ROCs for CP, CP1, CP2, and CP3 were 0.67 [95% confidence interval (CI), 0.62-0.72], 0.65 (95% CI, 0.60-0.70), 0.66 (95% CI, 0.61-0.71), and 0.63 (95% CI 0.58-0.68), respectively. CONCLUSION: GCP did not improve the accuracy of predicting survival post TAVR at 1 year, compared to CP alone.

Machine Learning on High-Dimensional Data to Predict Bleeding Post Percutaneous Coronary Intervention.

INTRODUCTION: The purpose of the current study is to determine the accuracy of machine learning in predicting bleeding outcomes post percutaneous coronary intervention (PCI) in comparison with the American College of Cardiology CathPCI bleeding risk (ACC-BR) model. METHODS: Mayo Clinic CathPCI registry data were retrospectively analyzed from January, 2003 to June, 2018, including 15,603 patients who underwent PCI. The cohort was randomly divided into a training sample of 11,703 patients (75%) and a unique test sample of 3900 patients (25%) prior to model generation. The risk-prediction model was generated utilizing a boosted classification tree algorithm of 105 unique variables to predict the risk of major and minor bleeding complications within 72 hours after PCI or before hospital discharge. The receiver operating characteristic (ROC) curves and areas under the curve (AUC) for the boosted classification tree algorithm (AI-BR) model and ACC-BR model were compared for the test cohort. RESULTS: The mean age of the patient cohort was 67 ± 12.7 years, and women constituted 30% of the cohort. The rate of major bleeding complications in the entire cohort was 1.8%. The sensitivity and specificity of the AIBR model were 77.3% and 80.9%, respectively. The ROC-AUC for the AI-BR model (0.873) was superior vs the ACC-BR model (0.764; P=.02) in predicting major bleeding for the test cohort. CONCLUSION: The AI-BR model accurately predicts bleeding post PCI and outperforms the ACC-BR model in predicting the risk of bleeding in patients undergoing PCI.

Resting Cardiac Efficiency Affects Survival Following Transcatheter Aortic Valve Replacement.

OBJECTIVE: Cardiac power to left ventricular mass (LVM) ratio, also termed cardiac efficiency (CE), reflects the rate of cardiac work delivered to the potential energy stored in LVM. We sought to assess the association between baseline resting CE and survival post transcatheter aortic valve replacement (TAVR). METHODS: We retrospectively extracted data of patients who received TAVR in the Mayo Clinic Foundation with follow up data available at 1 year. Cardiac output was measured using Doppler echocardiography at baseline. CE was calculated using the formula, (cardiac output × mean arterial blood pressure)/(451 × LVM × 100) W/100 g. Survival score analysis was performed to identify cut off value for CE to identify the maximum difference in mortality in the study cohort. Patients were subsequently divided into 2 groups CE < 0.38 W/100 g and CE ≥ 0.38 W/100 g. Survival was determined using Kaplan-Meier method. RESULTS: We included 954 patients in the final analysis. CE in group1 vs group 2 was 0.31 ± 0.05 W/100 g vs 0.59 ± 0.18 W/100 g. Patients in group1 were more likely to be male, had a higher prevalence of atrial fibrillation, prior myocardial infarction, mitral and tricuspid regurgitation. They also had a higher STS risk score, NYHA functional class, and lower aortic valve area. The remainder of the baseline characteristics was similar in both groups. A lower CE was associated with higher 1-year mortality following TAVR based on multivariate analysis. (Group1: 22.18% vs Group 2: 9.89%, p < .0001). CONCLUSION: In our cohort, a low baseline CE (<0.38 W/100 g) conferred higher mortality risk following TAVR.

Does Resting Cardiac Power Index Affect Survival Post Transcatheter Aortic Valve Replacement?

OBJECTIVE: Cardiac power index (CPI) is an integrative hemodynamic measure of cardiac pumping capability and is the product of the simultaneously measured mean arterial pressure and the cardiac output. We assessed the association between baseline resting CPI and survival post transcatheter aortic valve replacement (TAVR). METHODS AND RESULTS: We retrospectively abstracted data of patients who underwent TAVR at the Mayo Clinic Foundation with follow-up data available at 1 year. Baseline demographic, clinical, and echocardiographic data were abstracted. CPI was calculated using the formula, (cardiac output x mean arterial blood pressure) / (451 x body surface area) W/m². Patients were divided into CPI <0.48 W/m² (group 1) and CPI ≥0.48 W/m² (group 2). Survival according to CPI was determined using Kaplan-Meier method. Multivariate Cox regression analysis was performed to adjust for covariates. Nine hundred and seventy-five patients were included in the final analysis. CPI in group 1 vs group 2 was 0.41 ± 0.05 W/m² vs 0.66 ± 0.14 W/m², respectively (P<.001, two-sided t-test). Patients in group 1 were more likely to be male and to have a prior history of myocardial infarction, coronary revascularization, peripheral arterial disease, diabetes mellitus, transient ischemic attack, carotid artery disease, atrial fibrillation, lower left ventricular ejection fraction, and moderate to severe mitral and tricuspid regurgitation. After adjusting for baseline covariates, a lower CPI was associated with higher 1-year mortality among patients undergoing TAVR (24.39% in group 1 vs 8.28% in group 2; P<.001). CONCLUSION: Low baseline CPI (<0.48 W/m²) confers higher mortality risk among patients undergoing TAVR and provides additional prognostic information, which can help risk-stratify patients.

Renal Denervation for Resistant Hypertension in the contemporary era: A Systematic Review and Meta-analysis.

Renal denervation (RDN) is a catheter-based ablation procedure designed to treat resistant hypertension (RH). The objective of our study is to determine the effect of RDN on blood pressure and renal function in patients with RH in comparison to medical therapy alone. We performed an extensive literature search for randomized control trials (RCT) reporting office and 24 hr. blood pressure changes and estimated glomerular filtration rate (eGFR) at baseline and 6 months. We calculated a weighted standardized mean difference of blood pressure and renal outcomes between RDN and control groups using random effects models. Our search yielded 608 studies of which we included 15 studies for the final analysis. A total of 857 patients were treated with RDN and 616 patients treated with medical therapy ± sham procedure. Only 5 studies were double-blinded RCT with sham control. The adjusted standardized mean difference in the change in office based systolic and diastolic pressures (p = 0.18; p = 0.14); 24 hr. systolic and diastolic pressures (p = 0.20; p = 0.18); and eGFR (p = 0.20) from baseline to 6 months is statistically insignificant with significant heterogeneity. Subgroup analysis showed that among sham controlled trials, 24 hr. systolic blood pressure showed a modest but statistically significant benefit favoring renal denervation in patients with RH. Our meta-analysis of 15 RCTs showed no significant benefit of RDN on blood pressure control in patients with resistant hypertension. Subgroup analysis of sham control studies showed a modest benefit in 24 hr. systolic blood pressure at 6 months with RDN.