Faculty Bibliography

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.

AIMS/OBJECTIVE:We reviewed the literature to date for high-level evidence on the cardiovascular and other health effects of olive oil with a focus on the amount, frequency of use and type of olive oil consumed in prior studies. A total of twelve prospective cohort studies with sample sizes of at least 4000 individuals and one meta-analysis were identified. DATA SYNTHESIS/RESULTS:The majority of cohorts followed individuals aged ≥55 years old, free of cardiovascular disease (CVD) at baseline but at high risk, over periods of 4-10 years and with daily consumption amounts of 10-35 g/day. With the exception of the PREDIMED cohort that employed extra virgin olive oil, most remaining studies did not differentiate between different types of olive oil. Taken together, the data suggests an association between greater olive oil consumption and a lower CVD incidence/mortality and stroke risk. We use this information to evaluate the use of commercially available, capsule-based olive oil dietary supplements and suggest future directions. Notably, achieving minimum total daily doses described in the aforementioned studies would be challenging with current market formulations of olive oil supplements dosed at 1-1.25 g/capsule. CONCLUSIONS:Outside of mechanistic studies, little progress has been made in determining the olive oil component(s) underlying the observed health effects given the lack of compositional reporting and consistency across large scale human studies. We propose the use of supplements of varying composition, such as varying total phenolic content, in pragmatic trial designs focused on low-cost methodologies to address this question.

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:Prolongation of the QTc interval is associated with the risk of torsades de pointes. Determination of the QTc interval is therefore of critical importance. There is no reliable method for measuring or correcting the QT interval in atrial fibrillation (AF). OBJECTIVES:The authors sought to evaluate the use of a convolutional neural network (CNN) applied to AF electrocardiograms (ECGs) for accurately estimating the QTc interval and ruling out prolongation of the QTc interval. METHODS:The authors identified patients with a 12-lead ECG in AF within 10 days of a sinus ECG, with similar (±10 ms) QRS durations, between October 23, 2001, and November 5, 2021. A multilayered deep CNN was implemented in TensorFlow 2.5 (Google) to predict the MUSE (GE Healthcare) software-generated sinus QTc value from an AF ECG waveform, demographic characteristics, and software-generated features. RESULTS:The study identified 6,432 patients (44% female) with an average age of 71 years. The CNN predicted sinus QTc values with a mean absolute error of 22.2 ms and root mean squared error of 30.6 ms, similar to the intrinsic variability of the sinus QTc interval. Approximately 84% and 97% of the model's predictions were contained within 1 SD (±30.6 ms) and 2 SD (±61.2 ms) from the sinus QTc interval. The model outperformed the AFQTc method, exhibiting narrower error ranges (mean absolute error comparison P < 0.0001). The model performed best for ruling out QTc prolongation (negative predictive value 0.82 male, 0.92 female; specificity 0.92 male, 0.97 female). CONCLUSIONS:A CNN model applied to AF ECGs accurately predicted the sinus QTc interval, outperforming current alternatives and exhibiting a high negative predictive value.

Cardiac amyloidosis (CA) often goes unrecognized as a cause of heart failure with preserved ejection fraction (HFpEF). There is paucity of contemporary data evaluating the trends of CA diagnosis and associated sex differences. Adult heart failure hospitalizations were identified from the National Inpatient Sample between 2016 and 2019. Hospitalizations with heart failure other than HFpEF were excluded. Hospitalizations with a diagnosis of CA were identified. A Linear regression was utilized to calculate the trend of CA diagnosis over time. A multivariate logistic regressions analysis was performed to analyze sex differences. There was an increasing trend of CA from 1.2 to 2.3 per 1000 HFpEF admission in the first quarter of 2016 to the fourth quarter of 2019 (P_trend <0.001). In females, as compared to males, there was an increased risk of AIS (6% vs 3%, aOR: 1.68[1.24-2.27], P=0.001) and major bleeding events (10% vs 5%, aOR: 1.97[1.53-2.52], P<0.001). No difference was observed in the in-hospital mortality outcome (8% vs 7%, aOR: 1.2[0.95-1.53], P=0.12) between both groups. Our real-world contemporary analysis showed an increase in CA diagnosis from 2016 to 2019. Despite similar in-hospital mortality, females were associated with higher AIS and major bleeding events rates. Further prospective studies are needed to validate these results.

INTRODUCTION/BACKGROUND:Contemporary efforts to predict surgical outcomes focus on the associations between traditional discrete surgical risk factors. We aimed to determine whether natural language processing (NLP) of unstructured operative notes improves the prediction of residual disease in women with advanced epithelial ovarian cancer (EOC) following cytoreductive surgery. METHODS:Electronic Health Records were queried to identify women with advanced EOC including their operative notes. The Term Frequency - Inverse Document Frequency (TF-IDF) score was used to quantify the discrimination capacity of sequences of words (n-grams) regarding the existence of residual disease. We employed the state-of-the-art RoBERTa-based classifier to process unstructured surgical notes. Discrimination was measured using standard performance metrics. An XGBoost model was then trained on the same dataset using both discrete and engineered clinical features along with the probabilities outputted by the RoBERTa classifier. RESULTS:The cohort consisted of 555 cases of EOC cytoreduction performed by eight surgeons between January 2014 and December 2019. Discrete word clouds weighted by n-gram TF-IDF score difference between R0 and non-R0 resection were identified. The words 'adherent' and 'miliary disease' best discriminated between the two groups. The RoBERTa model reached high evaluation metrics (AUROC .86; AUPRC .87, precision, recall, and F1 score of .77 and accuracy of .81). Equally, it outperformed models that used discrete clinical and engineered features and outplayed the performance of other state-of-the-art NLP tools. When the probabilities from the RoBERTa classifier were combined with commonly used predictors in the XGBoost model, a marginal improvement in the overall model's performance was observed (AUROC and AUPRC of .91, with all other metrics the same). CONCLUSION/IMPLICATIONS/CONCLUSIONS:We applied a sui generis approach to extract information from the abundant textual surgical data and demonstrated how it can be effectively used for classification prediction, outperforming models relying on conventional structured data. State-of-art NLP applications in biomedical texts can improve modern EOC care.

INTRODUCTION/BACKGROUND:Key challenges of left atrial appendage occlusion (LAAO) procedures include accurate preprocedural device sizing and proper device positioning at the left atrial appendage (LAA) ostium to ensure sufficient device anchoring and avoid peri-device leaks. To address these challenges, 3D printing (3DP) of LAA models has recently emerged in the literature, first being described in 2015. We present a review of the benefits and drawbacks of employing this technology in LAAO procedures. METHODS:A comprehensive search of Pubmed was performed from database inception to October 1, 2021. A total of 19 eligible studies were identified and evaluated based on pre-, intra-, and postprocedural outcomes. Preprocediral imaging modalities, 3D segmentation software, 3D printer, 3DD printing material, occlusion deevice, cohort size, average age, thrombotic /bleeding risk scores, and AF type were also recorded when available. RESULTS:Preprocedurally the use of 3DP can consistently and accurately determine LAAO device size over the standard of care approaches. Intraprocedurally 3DP's impact entailed a statistically significant decrease in the number of devices used per procedure, as well as in the fluoroscopic time and dose. Postprocedurally, there is some evidence that 3DP could reduce the rate of peri-device leaks, with limited data on its effect on complication rates. CONCLUSION/CONCLUSIONS:Based on existing evidence, we recommend the focused application of 3DP to cases of complex LAA anatomy and for the training of proceduralists. However, next-generation LAAO devices and emerging augmented/virtual reality (AR/VR) systems could limit even this narrow window of clinical benefit afforded by 3DP. Key challenges of left atrial appendage include accurate preprocedural device sizing and proper device positioning at the left atrial appendage (LAA) ostium to ensure sufficient device anchoring and avoid peri-device leaks. To address these challenges, 3D printing (3DP) of LAA models has recently emerged in the literature, first being described in 2015. We present a review of the benefits and drawbacks of employing this technology in LAAO procedures.

Background/UNASSIGNED:, known to colonize all parts of the cotton plant. Only one prior case report has suggested cotton fever as a potential risk factor of infective endocarditis (IE). Case summary/UNASSIGNED:IE with bilateral septic pulmonary emboli. Transthoracic echocardiography findings included new tricuspid regurgitation and two mobile echodensities on the right atrial implantable cardioverter defibrillator (ICD) lead. Despite broad antibiotic coverage and extraction of the ICD leads, the patient passed away from septic shock. Discussion/UNASSIGNED:genus were isolated in patients' blood cultures. This evidence supports the endotoxin theory as the leading pathophysiologic explanation for cotton fever and suggests cotton fever as a risk factor for Gram-negative IE. In the inpatient setting it informs proper antibiotic coverage, whereas in the outpatient setting it supports harm reduction interventions in the form of sterile cotton balls.