Faculty Bibliography

IMPORTANCE/UNASSIGNED:Bariatric surgery, once the criterion standard in obesity treatment, has a small but concerning association with increased suicidality. Glucagon-like peptide 1 receptor agonists (GLP-1 RAs), originally developed to treat diabetes, now provide substantial efficacy in the treatment of obesity. However, concerns of risk of suicidality with these medicines have been raised. OBJECTIVE/UNASSIGNED:To evaluate the risk of suicidality and self-harm in randomized, placebo-controlled trials of GLP-1 RAs in adults with diabetes or obesity. DATA SOURCES/UNASSIGNED:MEDLINE, Embase, ClinicalTrials.gov, and Cochrane databases were systematically searched from inception to August 29, 2023. STUDY SELECTION/UNASSIGNED:Reports of randomized clinical trials (RCTs) lasting 6 or more months comparing GLP-1 RAs with placebo for the treatment of diabetes or obesity published in peer-reviewed journals were identified. Two independent reviewers screened all search-identified studies for inclusion. Records of outcomes were queried from primary papers, ClinicalTrials.gov entries, and corresponding authors. DATA EXTRACTION AND SYNTHESIS/UNASSIGNED:Two independent researchers abstracted data and assessed data quality and validity using PRISMA guidelines. Data were pooled using random-effects models. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Pooled incidence of completed or attempted suicide, occurrences of suicidal ideation, or self-harm. RESULTS/UNASSIGNED:A total of 27 of 144 RCTs meeting inclusion criteria systematically recorded suicide and/or self-harm-related events and included 32 357 individuals receiving GLP-1 RAs and 27 046 treated with placebo, over 74 740 and 68 095 person-years of follow-up, respectively. Event incidence was very low in the GLP-1 RA (0.044 per 100 person-years) and placebo (0.040 per 100 person-years) groups, with no statistically significant difference (rate ratio [RR], 0.76; 95% CI, 0.48-1.21; P = .24). Subgroup analyses did not suggest differences in outcomes based on diabetes status or GLP-1 RA used. Five studies were considered at risk of bias due to the loss of more than 5% of participants to follow-up. Otherwise, studies were not found to be heterogeneous nor at high risk of bias. CONCLUSIONS AND RELEVANCE/UNASSIGNED:There is unlikely to be an increase in the very low incidence of suicide-related adverse events among individuals receiving GLP-1 RAs within the context of RCTs. While these findings may further ease concerns about these adverse effects, continued monitoring is warranted to identify particular patients who may be at risk as extended use of GLP-1 RAs expands.

Existing deep learning algorithms for electrocardiogram (ECG) classification rely on supervised training approaches requiring large volumes of reliably labeled data. This limits their applicability to rare cardiac diseases like Brugada syndrome (BrS), often lacking accurately labeled ECG examples. To address labeled data constraints and the resulting limitations of supervised training approaches, we developed a novel deep learning model for BrS ECG classification using the Variance-Invariance-Covariance Regularization (VICReg) architecture for self-supervised pre-training. The VICReg model outperformed a state-of-the-art neural network in all calculated metrics, achieving an area under the receiver operating and precision-recall curves of 0.88 and 0.82, respectively. We used the VICReg model to identify missed BrS cases and hence refine the previously underestimated institutional BrS prevalence and patient outcomes. Our results provide a novel approach to rare cardiac disease identification and challenge existing BrS prevalence estimates offering a framework for other rare cardiac conditions.

Ongoing research attempts to benchmark large language models (LLM) against physicians' fund of knowledge by assessing LLM performance on medical examinations. No prior study has assessed LLM performance on internal medicine (IM) board examination questions. Limited data exists on how knowledge supplied to the models, derived from medical texts improves LLM performance. The performance of GPT-3.5, GPT-4.0, LaMDA and Llama 2, with and without additional model input augmentation, was assessed on 240 randomly selected IM board-style questions. Questions were sourced from the Medical Knowledge Self-Assessment Program released by the American College of Physicians with each question serving as part of the LLM prompt. When available, LLMs were accessed both through their application programming interface (API) and their corresponding chatbot. Mode inputs were augmented with Harrison's Principles of Internal Medicine using the method of Retrieval Augmented Generation. LLM-generated explanations to 25 correctly answered questions were presented in a blinded fashion alongside the MKSAP explanation to an IM board-certified physician tasked with selecting the human generated response. GPT-4.0, accessed either through Bing Chat or its API, scored 77.5-80.7% outperforming GPT-3.5, human respondents, LaMDA and Llama 2 in that order. GPT-4.0 outperformed human MKSAP users on every tested IM subject with its highest and lowest percentile scores in Infectious Disease (80th) and Rheumatology (99.7th), respectively. There is a 3.2-5.3% decrease in performance of both GPT-3.5 and GPT-4.0 when accessing the LLM through its API instead of its online chatbot. There is 4.5-7.5% increase in performance of both GPT-3.5 and GPT-4.0 accessed through their APIs after additional input augmentation. The blinded reviewer correctly identified the human generated MKSAP response in 72% of the 25-question sample set. GPT-4.0 performed best on IM board-style questions outperforming human respondents. Augmenting with domain-specific information improved performance rendering Retrieval Augmented Generation a possible technique for improving accuracy in medical examination LLM responses.

BACKGROUND/UNASSIGNED:Catheter ablation (CA) for ventricular tachycardia (VT) can be a useful treatment strategy, however, few studies have compared CA to medical therapy (MT) in the sarcoidosis population. OBJECTIVE/UNASSIGNED:To assess in-hospital outcomes and unplanned readmissions following CA for VT compared to MT in patients with sarcoidosis. METHODS/UNASSIGNED:Data was obtained from the Nationwide Readmissions Database between 2010 and 2019 to identify patients with sarcoidosis admitted for VT either undergoing CA or MT during elective and non-elective admission. Primary endpoints were a composite endpoint of inpatient mortality, cardiogenic shock, cardiac arrest and 30-day hospital readmissions. Procedural complications at index admission and causes of readmission were also identified. RESULTS/UNASSIGNED: = 0.343). The most common cause of readmission were ventricular arrhythmias (VA) in both groups, however, those undergoing elective CA were less likely to be readmitted for VA compared to non-elective CA. The most common complication in the CA group was cardiac tamponade (4.8 %). CONCLUSION/UNASSIGNED:VT ablation is associated with similar rates of 30-day readmission compared to MT and does not confer increased risk of harm with respect to inpatient mortality, cardiogenic shock or cardiac arrest. Further research is warranted to determine if a subgroup of sarcoidosis patients admitted with VT are better served with an initial conservative management strategy followed by VT ablation.

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.

BACKGROUND/UNASSIGNED:Sodium azide exposures are rare but can be lethal as the substance inhibits complex IV in the electron transport chain, blocking adenosine-triphosphate (ATP) synthesis. Sodium azide is mostly used as a propellant in vehicular airbags but is also used in laboratory, pharmacy, and industrial settings. No known antidote exists and its cardiotoxic effects are poorly described in the literature. CASE SUMMARY/UNASSIGNED:We describe the case of a 31-year-old patient with major depressive disorder presenting with altered mental status after ingestion of an unknown amount of sodium azide. Although initially chest pain free, she developed pleuritic chest pain 48 h after ingestion. This was accompanied by new diffuse ST elevations on the electrocardiogram and serum troponin elevations concerning for myopericarditis. Treatment was pursued with a 14-day course of colchicine resulting in complete symptom resolution within 4 days of treatment initiation. The patient's transthoracic echocardiogram was only notable for a preserved left ventricular ejection fraction (LVEF). DISCUSSION/UNASSIGNED:Cardiac toxicity after sodium azide ingestion usually occurs days after ingestion and has been previously described in the forms of heart failure with reduced ejection fraction complicated by cardiogenic shock. We describe the first case of sodium azide-induced myopericarditis with a preserved LVEF treated with colchicine. Colchicine is an established treatment for pericarditis, but its inhibition of endocytosis, an ATP-dependent cellular function, could be mechanistically relevant to this case.

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.