Association of Bovine Arch Anatomy With Incident Stroke After Transcatheter Aortic Valve Replacement.

BACKGROUND: Acute ischemic stroke complicates 2% to 3% of transcatheter aortic valve replacements (TAVRs). This study aimed to identify the aortic anatomic correlates in patients after TAVR stroke. METHODS AND RESULTS: This is a single-center, retrospective study of patients who underwent TAVR at the Mayo Clinic between 2012 and 2022. The aortic arch morphology was determined via a manual review of the pre-TAVR computed tomography images. An "a priori" approach was used to select the covariates for the following: (1) the logistic regression model assessing the association between a bovine arch and periprocedural stroke (defined as stroke within 7 days after TAVR); and (2) the Cox proportional hazards regression model assessing the association between a bovine arch and long-term stroke after TAVR. A total of 2775 patients were included (59.6% men; 97.8% White race; mean±SD age, 79.3±8.4 years), of whom 495 (17.8%) had a bovine arch morphology. Fifty-seven patients (1.7%) experienced a periprocedural stroke. The incidence of acute stroke was significantly higher among patients with a bovine arch compared with those with a nonbovine arch (3.6% versus 1.7%; P=0.01). After adjustment, a bovine arch was independently associated with increased periprocedural strokes (adjusted odds ratio, 2.16 [95% CI, 1.22-3.83]). At a median follow-up of 2.7 years, the overall incidence of post-TAVR stroke was 6.0% and was significantly higher in patients with a bovine arch even after adjusting for potential confounders (10.5% versus 5.0%; adjusted hazard ratio, 2.11 [95% CI, 1.51-2.93]; P<0.001). CONCLUSIONS: A bovine arch anatomy is associated with a significantly higher risk of periprocedural and long-term stroke after TAVR.

Association of transcatheter left atrial appendage occlusion with acute changes in left atrial pressure: An invasive hemodynamic study.

BACKGROUND: The hemodynamic effects of transcatheter left atrial appendage occlusion (LAAO) remain unclear. OBJECTIVE: We sought to assess the effect of LAAO on invasive hemodynamics and their correlation with clinical outcomes. METHODS: We recorded mean left atrial pressure (mLAP) before and after device deployment. We assessed the prevalence and predictors of mLAP increase after deployment, the association between significant mLAP increase after deployment and 45-day peridevice leak (PDL), and the association between mLAP increase and heart failure (HF) hospitalization. A significant mLAP increase was defined as one equal to or greater than the mean percentage increase in mLAP after deployment (≥28%). RESULTS: We included 302 patients (36.4% female; mean age, 75.8 ± 9.5 years). After deployment, mLAP increased in 48% of patients, 38% of whom experienced significant mLAP increase. Independent predictors of mLAP increase were baseline mLAP ≤14 mm Hg, nonparoxysmal atrial fibrillation, and age per 5 years (odds ratios: 3.66 [95% CI, 2.21-6.05], 1.81 [95% CI, 1.08-3.02], and 0.85 [95% CI, 0.73-0.99], respectively). Significant mLAP increase was an independent predictor of 45-day PDL (odds ratio, 2.55; 95% CI, 1.04-6.26). There was no association between mLAP increase and HF hospitalization. CONCLUSION: After deployment, mLAP acutely rises in 48% of patients, although this is not associated with increased HF hospitalizations. PDL is more likely to develop at 45 days in patients with significant increase in mLAP after deployment, although most leaks were small (<5 mm). These findings suggest that mLAP increase after deployment is not associated with major safety concerns. Additional studies are warranted to explore the long-term hemodynamic effects of LAAO.

Machine learning vs. conventional methods for prediction of 30-day readmission following percutaneous mitral edge-to-edge repair.

BACKGROUND: Identifying predictors of readmissions after mitral valve transcatheter edge-to-edge repair (MV-TEER) is essential for risk stratification and optimization of clinical outcomes. AIMS: We investigated the performance of machine learning [ML] algorithms vs. logistic regression in predicting readmissions after MV-TEER. METHODS: We utilized the National-Readmission-Database to identify patients who underwent MV-TEER between 2015 and 2018. The database was randomly split into training (70 %) and testing (30 %) sets. Lasso regression was used to remove non-informative variables and rank informative ones. The top 50 informative predictors were tested using 4 ML models: ML-logistic regression [LR], Naive Bayes [NB], random forest [RF], and artificial neural network [ANN]/For comparison, we used a traditional statistical method (principal component analysis logistic regression PCA-LR). RESULTS: A total of 9425 index hospitalizations for MV-TEER were included. Overall, the 30-day readmission rate was 14.6 %, and heart failure was the most common cause of readmission (32 %). The readmission cohort had a higher burden of comorbidities (median Elixhauser score 5 vs. 3) and frailty score (3.7 vs. 2.9), longer hospital stays (3 vs. 2 days), and higher rates of non-home discharges (17.4 % vs. 8.5 %). The traditional PCA-LR model yielded a modest predictive value (area under the curve [AUC] 0.615 [0.587-0.644]). Two ML algorithms demonstrated superior performance than the traditional PCA-LR model; ML-LR (AUC 0.692 [0.667-0.717]), and NB (AUC 0.724 [0.700-0.748]). RF (AUC 0.62 [0.592-0.677]) and ANN (0.65 [0.623-0.677]) had modest performance. CONCLUSION: Machine learning algorithms may provide a useful tool for predicting readmissions after MV-TEER using administrative databases.

Prognostic Value of Patient-Reported Outcomes in Predicting Long-Term Mortality After Transcatheter Aortic Valve Replacement.

Background Patient-reported outcome measures have been shown to have important prognostic value after various cardiac interventions. We assessed the association between the change in Kansas City Cardiomyopathy Questionnaire 12 (KCCQ-12) score after transcatheter aortic valve replacement and mortality. Methods and Results We included patients who underwent transcatheter aortic valve replacement at Mayo Clinic between February 2012 to June 2022 and who completed a KCCQ-12 before and 30 to 45 days after the procedure. Patients were categorized into 3 groups: those who experienced significant (>+19 points; group 1), modest (1-19 points; group 2), and no (≤0 points; group 3) improvement. A total of 1124 patients were included: 60.8% men; 97.6% White. Mean age was 79.4±8.3 years, baseline KCCQ-12 score was 53.9±24.5, and median Society of Thoracic Surgeons score was 4.9% (interquartile range, 3.1-8.0). At 45 days, the mean change in KCCQ-12 score was 19±24 points; 46.3% (n=520) of patients had a significant improvement in their KCCQ-12 score, while 33.4% (n=375) and 20.4% (n=229) had modest and no improvement, respectively. Median survival was higher in group 1 (5.7±0.2 years) compared with groups 2 and 3 (5.1±0.3 and 4.1±0.4 years, respectively; P<0.001). Compared with patients in group 1, those in groups 2 and 3 had higher long-term risk-adjusted mortality (adjusted hazard ratios, 1.54 [95% CI, 1.20-1.96], and 2.30 [95% CI, 1.74-3.04], respectively). Conclusions Patients who experience modest or no improvement in KCCQ-12 score after transcatheter aortic valve replacement have substantially higher long-term mortality. Delta KCCQ-12 is a cost-effective, efficient tool that can identify patients at increased risk of death at long-term follow-up post-transcatheter aortic valve replacement.

Development and Validation of a Machine Learning Score for Readmissions After Transcatheter Aortic Valve Implantation.

Background: Identifying predictors of readmissions after transcatheter aortic valve implantation (TAVI) is an important unmet need. Objectives: We sought to explore the role of machine learning (ML) in predicting readmissions after TAVI. Methods: We included patients who underwent TAVI between 2016 and 2019 in the Nationwide Readmission Database. A total of 917 candidate predictors representing all International Classification of Diseases, Tenth Revision, diagnosis and procedure codes were included. First, we used lasso regression to remove noninformative variables and rank informative ones. Next, we used an unsupervised ML model (K-means) to identify patterns/clusters in the data. Furthermore, we used Light Gradient Boosting Machine and Shapley Additive exPlanations to specify the impact of individual predictors. Finally, we built a parsimonious model to predict 30-day readmission. Results: A total of 117,398 and 93,800 index TAVI hospitalizations were included in the 30- and 90-day analyses, respectively. Lasso regression identified 138 and 199 informative predictors for the 30- and 90-day readmission, respectively. Next, K-means recognized 2 distinct clusters: low risk and high risk. In the 30-day cohort, the readmission rate was 10.1% in the low risk group and 23.3% in the high risk group. In the 90-day cohort, the rates were 17.4% and 35.3%, respectively. The top predictors were the length of stay, frailty score, total discharge diagnoses, acute kidney injury, and Elixhauser score. These predictors were incorporated into a risk score (TAVI readmission score), which exhibited good performance in an external validation cohort (area under the curve 0.74 [0.7-0.78]). Conclusions: ML methods can leverage widely available administrative databases to identify patients at risk for readmission after TAVI, which could inform and improve post-TAVI care.

Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery.

OBJECTIVE: To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF>35%). METHODS: Patients who underwent valve or coronary bypass surgery at Mayo Clinic (1993-2019) and had documented LVEF above 35% on baseline electrocardiography were included. We compared patients with an abnormal vs a normal AI-enhanced electrocardiogram (AI-ECG) screen for LVEF of 35% or below on preoperative electrocardiography. The primary end point was all-cause mortality. RESULTS: A total of 20,627 patients were included, of whom 17,125 (83.0%) had a normal AI-ECG screen and 3502 (17.0%) had an abnormal AI-ECG screen. Patients with an abnormal AI-ECG screen were older and had more comorbidities. Probability of survival at 5 and 10 years was 86.2% and 68.2% in patients with a normal AI-ECG screen vs 71.4% and 45.1% in those with an abnormal screen (log-rank, P<.01). In the multivariate Cox survival analysis, the abnormal AI-ECG screen was independently associated with a higher all-cause mortality overall (hazard ratio [HR], 1.31; 95% CI, 1.24 to 1.37) and in subgroups of isolated valve surgery (HR, 1.30; 95% CI, 1.18 to 1.42), isolated coronary artery bypass grafting (HR, 1.29; 95% CI, 1.20 to 1.39), and combined coronary artery bypass grafting and valve surgery (HR, 1.19; 95% CI, 1.08 to 1.32). In a subgroup analysis, the association between abnormal AI-ECG screen and mortality was consistent in patients with LVEF of 35% to 55% and among those with LVEF above 55%. CONCLUSION: A novel electrocardiography-based AI algorithm that predicts severe ventricular dysfunction can predict long-term mortality among patients with LVEF above 35% undergoing valve and/or coronary bypass surgery.

Endosonographic examination of thyroid gland among patients with nonthyroid cancers.

OBJECTIVES: There is limited endosonographic literature regarding thyroid gland pathology, which is frequently visualized during upper endoscopic ultrasound (EUS). Our objective was to assess the prevalence of benign and malignant thyroid lesions encountered during routine upper EUS within a cancer center setting. MATERIALS AND METHODS: The data were prospectively collected and retrospectively analyzed. All upper EUS procedures performed between October 2012 and July 2014 were reviewed at a large referral cancer center. Data collected included patient demographics, preexisting thyroid conditions, thyroid gland dimensions, the presence or absence of thyroid lesions, and EUS morphology of lesions if present, and interventions performed to characterize thyroid lesions and pathology results when applicable. RESULTS: Two hundred and forty-five EUS procedures were reviewed. Of these, 100 cases reported a detailed endosonographic examination of the thyroid gland. Most of the thyroid glands were endosonographically visualized when the tip of the scope was at 18 cm from the incisors. Twelve cases showed thyroid lesions, out of which three previously undiagnosed thyroid cancers were visualized during EUS (two primary papillary thyroid cancers and one anaplastic thyroid cancer). Transesophageal EUS-guided fine needle aspiration of thyroid lesions was feasible when the lesion was in the inferior portion of the thyroid gland, and the tip of the scope was at 18 cm or more from the incisors. CONCLUSIONS: Routine EUS examination may detect unexpected thyroid lesions including malignant ones. We encourage endosonographers to screen the visualized portions of the thyroid gland during routine withdrawal of the echoendoscope.

Primary papillary thyroid carcinoma diagnosed by using endoscopic ultrasound with fine needle aspiration.

There is paucity in the literature on the use of endoscopic ultrasound (EUS) for evaluating the thyroid gland. We report the first case of primary papillary thyroid cancer diagnosed by using EUS and fine needle aspiration (FNA). A 66-year-old man underwent EUS for the evaluation of mediastinal lymphadenopathy. FNA of the lymph nodes showed benign findings. A hypoechoic mass was noted in the right lobe of the thyroid gland. Therefore, FNA was performed. The cytological results were consistent with primary papillary thyroid cancer.