Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source.

OBJECTIVES: Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized AF. We pursued this study to determine if the AI-ECG model differentiates between patients with ESUS and those with known causes of stroke, and to evaluate whether the AF prediction by AI-ECG among patients with ESUS was associated with the results of prolonged ambulatory cardiac rhythm monitoring. MATERIALS AND METHODS: We reviewed consecutive patients admitted with acute ischemic stroke to a comprehensive stroke center between January 2018 and August 2019 and employed the TOAST classification to categorize the mechanisms of ischemia. Use and results of ambulatory cardiac rhythm monitoring after discharge were gathered. We ran the AI-ECG model to obtain AF probabilities from all ECGs acquired during the hospitalization and compared those probabilities in patients with ESUS versus those with known stroke causes (apart from AF), and between patients with and without AF detected by ambulatory cardiac rhythm monitoring. RESULTS: The study cohort had 930 patients, including 263 patients (28.3%) with known AF or AF diagnosed during the index hospitalization and 265 cases (28.5%) categorized as ESUS. Ambulatory cardiac rhythm monitoring was performed in 226 (85.3%) patients with ESUS. AF probability by AI-ECG was not associated with ESUS. However, among patients with ESUS, the probability of AF by AI-ECG was associated with a higher likelihood of AF detection by ambulatory monitoring (P = 0.004). A probability of AF by AI-ECG greater than 0.20 was associated with AF detection by ambulatory cardiac rhythm monitoring with an OR of 5.47 (95% CI 1.51-22.51). CONCLUSIONS: AI-ECG may help guide the use of prolonged ambulatory cardiac rhythm monitoring in patients with ESUS to identify those who might benefit from anticoagulation.

Data Analytics of Electronic Medical Record to Study Racial Diversities in Cardiovascular Diagnosis and Treatment.

The study of precision medicine that measures the effects of social, cultural, and environmental influences on health is essential to improve health outcomes. Race is a social concept used historically to divide, track, control populations, and reinforce social hierarchies. Beyond genetics, race is also a surrogate for other socioeconomic factors affecting patient outcomes. Our data analytics study aims to analyze the Electronic Medical Record (EMR) to study patients of different races in diagnosing and treating Coronary Artery Disease (CAD). We found no race discrepancies at the University of California San Francisco Medical Centers. This study opens several new hypotheses for further research in this crucial field.

Data Analytics of Electronic Health Records to Enhance Care of Coronary Artery Disease in Younger Women with Avoiding Possible Delay in Treatment.

Early detection plays a key role to enhance the outcome for Coronary Artery Disease. We utilized a big data analytics platform on ∼32,000 patients to trace patients from the first encounter to CAD treatment. There are significant gender-based differences in patients younger than 60 from the time of the first encounter to Coronary Artery Bypass Grafting with a p-value=0.03. This recognition makes significant changes in outcome by avoiding delay in treatment.

An Ontology for Cardiothoracic Surgical Education and Clinical Data Analytics.

The development of an ontology facilitates the organization of the variety of concepts used to describe different terms in different resources. The proposed ontology will facilitate the study of cardiothoracic surgical education and data analytics in electronic medical records (EMR) with the standard vocabulary.

Simulation of Cerebrospinal Fluid Leak Repair Using a 3-Dimensional Printed Model.

BACKGROUND: Acquiring proficiency for the repair of a cerebrospinal fluid (CSF) leak is challenging in great part due to its relative rarity, which offers a finite number of training opportunities. OBJECTIVE: The purpose of this study was to evaluates the use of a 3-dimensional (3D) printed, anatomically accurate model to simulate CSF leak closure. METHODS: Volunteer participants completed two simulation sessions. Questionnaires to assess their professional qualifications and a standardized 5-point Likert scale to estimate the level of confidence, were completed before and after each session. Participants were also queried on the overall educational utility of the simulation. RESULTS: Thirteen otolaryngologists and 11 neurosurgeons, met the inclusion criteria. A successful repair of the CSF leak was achieved by 20/24 (83.33%), and 24/24 (100%) during the first and second simulation sessions respectively (average time 04:04 ± 1.39 and 02:10 ± 01:11). Time-to-close-the-CSF-leak during the second session was significantly shorter than the first (p < 0.001). Confidence scores increased across the training sessions (3.3 ± 1.0, before the simulation, 3.7 ± 0.6 after the first simulation, and 4.2 ± 0.4 after the second simulation; p < 0.001). All participants reported an increase in confidence and believed that the model represented a valuable training tool. CONCLUSIONS: Despite significant differences with varying clinical scenarios, 3D printed models for cerebrospinal leak repair offer a feasible simulation for the training of residents and novice surgeons outside the constrictions of a clinical environment.

Emergent Phenol Injection of Bilateral Stellate Ganglion for Management of Refractory Malignant Ventricular Arrhythmias.

BACKGROUND Management of incessant electrical storm is poorly defined. These 2 case studies demonstrate a simplified percutaneous approach to achieve stellate ganglion ablation (SGA) and to promptly control malignant ventricular arrhythmias. CASE REPORT This report describes 2 patients with deteriorating hemodynamics, progressive ventricular arrhythmias, and worsening heart failure, managed with emergent percutaneous fluoroscopically-guided bilateral SGA to achieve bilateral cardiac sympathetic denervation. While supine and intubated, the left and then right stellate ganglion were identified guided by anatomic landmarks. Using a 22-guage, 3.5-inch spinal needle, contrast dye was injected with appropriate outline of the stellate ganglion at the uncinate process of the C6 vertebra. Bupivacaine 0.5% was injected, followed by phenol 6%. Successful SGA was confirmed by intentional Horner's syndrome with bilateral eye lag. The procedures were completed in about 30 min without complications and there was a dramatic reduction in ventricular arrhythmias. CONCLUSIONS Emergent percutaneous bilateral SGA can be accomplished with a brief procedure resulting in management of electrical storm.