Faculty Bibliography

BACKGROUND:Prediction of drug-induced long QT syndrome (diLQTS) is of critical importance given its association with torsades de pointes. There is no reliable method for the outpatient prediction of diLQTS. OBJECTIVES/OBJECTIVE:This study sought to evaluate the use of a convolutional neural network (CNN) applied to electrocardiograms (ECGs) to predict diLQTS in an outpatient population. METHODS:We identified all adult outpatients newly prescribed a QT-prolonging medication between January 1, 2003, and March 31, 2022, who had a 12-lead sinus ECG in the preceding 6 months. Using risk factor data and the ECG signal as inputs, the CNN QTNet was implemented in TensorFlow to predict diLQTS. RESULTS:Models were evaluated in a held-out test dataset of 44,386 patients (57% female) with a median age of 62 years. Compared with 3 other models relying on risk factors or ECG signal or baseline QTc alone, QTNet achieved the best (P < 0.001) performance with a mean area under the curve of 0.802 (95% CI: 0.786-0.818). In a survival analysis, QTNet also had the highest inverse probability of censorship-weighted area under the receiver-operating characteristic curve at day 2 (0.875; 95% CI: 0.848-0.904) and up to 6 months. In a subgroup analysis, QTNet performed best among males and patients ≤50 years or with baseline QTc <450 ms. In an external validation cohort of solely suburban outpatient practices, QTNet similarly maintained the highest predictive performance. CONCLUSIONS:An ECG-based CNN can accurately predict diLQTS in the outpatient setting while maintaining its predictive performance over time. In the outpatient setting, our model could identify higher-risk individuals who would benefit from closer monitoring.

OBJECTIVES/OBJECTIVE:We investigated characteristics of left atrial conduction in patients with HCM, paroxysmal AF and normal bipolar voltage. BACKGROUND:Patients with hypertrophic cardiomyopathy (HCM) exhibit abnormal cardiac tissue arrangement. The incidence of atrial fibrillation (AF) is increased fourfold in patients with HCM and confers a fourfold increased risk of death. Catheter ablation is less effective in HCM, with twofold increased risk of AF recurrence. The mechanisms of AF perpetuation in HCM are poorly understood. METHODS:We analyzed 20 patients with HCM and 20 controls presenting for radiofrequency ablation of paroxysmal AF normal left atrial voltage(> 0.5 mV). Intracardiac electrograms were extracted from the CARTO mapping system and analyzed using Matlab/Python code interfacing with Core OpenEP software. Conduction velocity maps were calculated using local activation time gradients. RESULTS: = 0.13, p = 0.03). CONCLUSIONS:Atrial conduction velocity is significantly reduced in patients with HCM and paroxysmal AF, possibly contributing to arrhythmia persistence after catheter ablation.

INTRODUCTION/UNASSIGNED:Excessive dialytic potassium (K) and acid removal are risk factors for arrhythmias; however, treatment-to-treatment dialysate modification is rarely performed. We conducted a multicenter, pilot randomized study to test the safety, feasibility, and efficacy of 4 point-of-care (POC) chemistry-guided protocols to adjust dialysate K and bicarbonate (HCO3) in outpatient hemodialysis (HD) clinics. METHODS/UNASSIGNED:Participants received implantable cardiac loop monitors and crossed over to four 4-week periods with adjustment of dialysate K or HCO3 at each treatment according to pre-HD POC values: (i) K-removal minimization, (ii) K-removal maximization, (iii) Acidosis avoidance, and (iv) Alkalosis avoidance. The primary end point was percentage of treatments adhering to the intervention algorithm. Secondary endpoints included pre-HD K and HCO variability, adverse events, and rates of clinically significant arrhythmias (CSAs). RESULTS/UNASSIGNED:Nineteen subjects were enrolled in the study. HD staff completed POC testing and correctly adjusted the dialysate in 604 of 708 (85%) of available HD treatments. There was 1 K ≤3, 29 HCO3 <20 and 2 HCO3 >32 mEq/l and no serious adverse events related to study interventions. Although there were no significant differences between POC results and conventional laboratory measures drawn concurrently, intertreatment K and HCO3 variability was high. There were 45 CSA events; most were transient atrial fibrillation (AF), with numerically fewer events during the alkalosis avoidance period (8) and K-removal maximization period (3) compared to other intervention periods (17). There were no significant differences in CSA duration among interventions. CONCLUSION/UNASSIGNED:Algorithm-guided K/HCO3 adjustment based on POC testing is feasible. The variability of intertreatment K and HCO3 suggests that a POC-laboratory-guided algorithm could markedly alter dialysate-serum chemistry gradients. Definitive end point-powered trials should be considered.

• 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/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.

• 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.