Faculty Bibliography

Purpose: Cardiac radioablation is an emerging therapy for recurrent ventricular tachycardia. Electrophysiology (EP) data, including electroanatomic maps (EAM) and electrocardiographic imaging (ECGI), provide crucial information for defining the arrhythmogenic target volume. The absence of standardized workflows and software tools to integrate the EP maps into a radiation planning system limits their use. This study developed a comprehensive software tool to enable efficient utilization of the mapping for cardiac radioablation treatment planning. Methods and Materials: The tool, HeaRTmap, is a Python-scripted plug-in module on the open-source 3D Slicer software platform. HeaRTmap is able to import EAM and ECGI data and visualize the maps in 3D Slicer. The EAM is translated into a 3D space by registration with cardiac magnetic resonance images (MRI) or computed tomography (CT). After the scar area is outlined on the mapping surface, the tool extracts and extends the annotated patch into a closed surface and converts it into a structure set associated with the anatomic images. The tool then exports the structure set and the images as The Digital Imaging and Communications in Medicine Standard in Radiotherapy for a radiation treatment planning system to import. Overlapping the scar structure on simulation CT, a transmural target volume is delineated for treatment planning. Results: The tool has been used to transfer Ensite NavX EAM data into the Varian Eclipse treatment planning system in radioablation on 2 patients with ventricular tachycardia. The ECGI data from CardioInsight was retrospectively evaluated using the tool to derive the target volume for a patient with left ventricular assist device, showing volumetric matching with the clinically used target with a Dice coefficient of 0.71. Conclusions: HeaRTmap smoothly fuses EP information from different mapping systems with simulation CT for accurate definition of radiation target volume. The efficient integration of EP data into treatment planning potentially facilitates the study and adoption of the technique.

• Precise LAA anatomy must be established for LAA occlusion device selection. • We have developed the TUPLE maneuver, an acronym for “tilt up and turn left”. • The TUPLE maneuver facilitates LAA device selection and intraprocedural guidance.

BACKGROUND:With the growing use of implantable cardiac devices, the need for transvenous lead extraction has increased, which translates to increased procedural volumes. Sex differences in lead extraction outcomes are not well studied. OBJECTIVE:The present study aims at evaluating the impact of sex on outcomes of lead extraction. METHODS:We identified 71,754 patients who presented between 2016 and 2019 and underwent transvenous lead extraction. Their clinical data were retrospectively accrued from the National Readmission Database (NRD) using the corresponding diagnosis codes. We compared clinical outcomes between male and female patients. Odds ratios (ORs) for the primary and secondary outcomes were calculated, and multivariable regression analysis was utilized to adjust for confounding variables. RESULTS:Compared to male patients, female patients had higher in-hospital complications including pneumothorax (OR 1.26, 95% CI (1.07-1.4), P < 0.01), hemopericardium (OR 1.39, 95% CI (1.02-1.88), P = 0.036), injury to superior vena cava and innominate vein requiring repair (OR 1.88, 95% CI (1.14-3.1), P = 0.014; OR 3.4, 95% CI (1.8-6.5), P < 0.01), need for blood transfusion (OR 1.28, 95% CI (1.18-1.38), P < 0.01), and pericardiocentesis (OR 1.6, 95% CI (1.3-2), P < 0.01). Thirty-day readmission was also significantly higher in female patients (OR 1.09, 95% CI (1.02-1.17), P < 0.01). There was no significant difference regarding in-hospital mortality (OR 0.99, 95% CI (0.87-1.14), P = 0.95). CONCLUSION/CONCLUSIONS:In female patients, lead extraction is associated with worse clinical outcomes and higher 30-day readmission rate.

Background: A lack of explainability in published machine learning (ML) models limits clinicians"™ understanding of how predictions are made, in turn undermining uptake of the models into clinical practice. Objective: The purpose of this study was to develop explainable ML models to predict in-hospital mortality in patients hospitalized for myocardial infarction (MI). Methods: Adult patients hospitalized for an MI were identified in the National Inpatient Sample between January 1, 2012, and September 30, 2015. The resulting cohort comprised 457,096 patients described by 64 predictor variables relating to demographic/comorbidity characteristics and in-hospital complications. The gradient boosting algorithm eXtreme Gradient Boosting (XGBoost) was used to develop explainable models for in-hospital mortality prediction in the overall cohort and patient subgroups based on MI type and/or sex. Results: The resulting models exhibited an area under the receiver operating characteristic curve (AUC) ranging from 0.876 to 0.942, specificity 82% to 87%, and sensitivity 75% to 87%. All models exhibited high negative predictive value ≥0.974. The SHapley Additive exPlanation (SHAP) framework was applied to explain the models. The top predictor variables of increasing and decreasing mortality were age and undergoing percutaneous coronary intervention, respectively. Other notable findings included a decreased mortality risk associated with certain patient subpopulations with hyperlipidemia and a comparatively greater risk of death among women below age 55 years. Conclusion: The literature lacks explainable ML models predicting in-hospital mortality after an MI. In a national registry, explainable ML models performed best in ruling out in-hospital death post-MI, and their explanation illustrated their potential for guiding hypothesis generation and future study design.

• LAA membranes are exceedingly rare with variable morphologies. • Thromboembolic risk with LAA membranes remains unknown. • Use of 3D TEE transillumination may assist in visualization and understanding.

BACKGROUND/UNASSIGNED:A lack of explainability in published machine learning (ML) models limits clinicians' understanding of how predictions are made, in turn undermining uptake of the models into clinical practice. OBJECTIVE/UNASSIGNED:The purpose of this study was to develop explainable ML models to predict in-hospital mortality in patients hospitalized for myocardial infarction (MI). METHODS/UNASSIGNED:Adult patients hospitalized for an MI were identified in the National Inpatient Sample between January 1, 2012, and September 30, 2015. The resulting cohort comprised 457,096 patients described by 64 predictor variables relating to demographic/comorbidity characteristics and in-hospital complications. The gradient boosting algorithm eXtreme Gradient Boosting (XGBoost) was used to develop explainable models for in-hospital mortality prediction in the overall cohort and patient subgroups based on MI type and/or sex. RESULTS/UNASSIGNED:The resulting models exhibited an area under the receiver operating characteristic curve (AUC) ranging from 0.876 to 0.942, specificity 82% to 87%, and sensitivity 75% to 87%. All models exhibited high negative predictive value ≥0.974. The SHapley Additive exPlanation (SHAP) framework was applied to explain the models. The top predictor variables of increasing and decreasing mortality were age and undergoing percutaneous coronary intervention, respectively. Other notable findings included a decreased mortality risk associated with certain patient subpopulations with hyperlipidemia and a comparatively greater risk of death among women below age 55 years. CONCLUSION/UNASSIGNED:The literature lacks explainable ML models predicting in-hospital mortality after an MI. In a national registry, explainable ML models performed best in ruling out in-hospital death post-MI, and their explanation illustrated their potential for guiding hypothesis generation and future study design.

BACKGROUND:Established electroanatomic mapping techniques for substrate mapping for ventricular tachycardia (VT) ablation includes voltage mapping, isochronal late activation mapping (ILAM), and fractionation mapping. Omnipolar mapping (Abbott Medical, Inc.) is a novel optimized bipolar electrogram creation technique with integrated local conduction velocity annotation. The relative utilities of these mapping techniques are unknown. OBJECTIVE:The purpose of this study was to evaluate the relative utility of various substrate mapping techniques for the identification of critical sites for VT ablation. METHODS:Electroanatomic substrate maps were created and retrospectively analyzed in 27 patients in whom 33 VT critical sites were identified. RESULTS:. CONCLUSION/CONCLUSIONS:ILAM, fractionation, and CV mapping each identified distinct critical sites and provided a smaller area of interest than did voltage mapping alone. The sensitivity of novel mapping modalities improved with greater local point density.

• Individual cases of Watchman FLX DAT are scare in literature. • The Watchman FLX has shown lower rates of DAT than the Watchman 2.0. • Thrombus formation is still possible in rare instances with the Watchman FLX.

INTRODUCTION/BACKGROUND:Radiofrequency ablation (RFA) slow pathway modification for catheter ablation of atrioventricular nodal reentrant tachycardia (AVNRT) is traditionally performed using a 4-mm nonirrigated (NI) RF ablation catheter. Slow pathway modification using irrigated, contact-force sensing (ICFS) RFA catheters has been described in case reports, but the outcomes have not been systematically evaluated. METHODS:Acute procedural outcomes of 200 consecutive patients undergoing slow pathway modification for AVNRT were analyzed. A 3.5-mm ICFS RFA catheter (ThermoCool SmartTouch STSF, Biosense Webster, Inc.) was utilized in 134 patients, and a 4-mm NI RFA catheter (EZ Steer, Biosense Webster, Inc.) was utilized in 66 patients. Electroanatomic maps were retrospectively analyzed in a blinded fashion to determine the proximity of ablation lesions to the His region. RESULTS:The baseline characteristics of patients in both groups were similar. Total RF time was significantly lower in the ICFS group compared to the NI group (5.53 ± 4.6 vs. 6.24 ± 4.9 min, p = 0.03). Median procedure time was similar in both groups (ICFS, 108.0 (87.5-131.5) min vs. NI, 100.0 (85.0-125.0) min; p = 0.2). Ablation was required in closer proximity to the His region in the NI group compared to the ICFS group (14.4 ± 5.9 vs. 16.7 ± 6.4 mm, respectively, p = 0.01). AVNRT was rendered noninducible in all patients, and there was no arrhythmia recurrence during follow-up in both groups. Catheter ablation was complicated by AV block in one patient in the NI group. CONCLUSION/CONCLUSIONS:Slow pathway modification for catheter ablation of AVNRT using an ICFS RFA catheter is feasible, safe, and may facilitate shorter duration ablation while avoiding ablation in close proximity to the His region.