Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias.

BACKGROUND: Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. OBJECTIVE: Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. METHODS: We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). RESULTS: Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. CONCLUSION: Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

Saliency maps provide insights into artificial intelligence-based electrocardiography models for detecting hypertrophic cardiomyopathy.

INTRODUCTION: A 12­lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives their performance. We hypothesized that saliency maps could be used to visually identify ECG segments that contribute to a CNN's robust classification of HCM. METHODS: We derived a new one­lead (lead I) CNN model based on median beats using the same methodology and cohort used for the original 12­lead CNN model (3047 patients with HCM, and 63,926 sex- and age-matched non-HCM controls). One­lead, median-beat saliency maps were generated and visually evaluated in an independent cohort of 100 patients with a diagnosis of HCM and a high artificial intelligence (AI)-ECG-HCM probability score to determine which ECG segments contributed to the model's detection of HCM. RESULTS: The one­lead, median-beat CNN had an AUC of 0.90 (95% CI 0.89-0.92) for HCM detection, similar to the original 12­lead ECG model. In the independent HCM cohort (n = 100), saliency maps highlighted the ST-T segment in 92 ECGs, the atrial depolarization segment in 12 ECGs, and the QRS complex in 5 ECGs. CONCLUSIONS: Saliency maps of a one­lead, median-beat-based CNN model identified perturbations in ventricular repolarization as the main region of interest in detecting HCM.

Artificial intelligence and atrial fibrillation.

BACKGROUND: In the context of atrial fibrillation (AF), traditional clinical practices have thus fallen short in several domains, such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. AIMS: To discuss the roles of artificial intelligence (AI)-enabled electrocardiogram (ECG) pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. MATERIALS & METHODS: An extensive search and review of the currently available literature on the topics. RESULTS: One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Challenges with regards to the benefits and harms of AF screening remain. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm. DISCUSSION: Knowledge gaps remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and identifying those who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. The role of DL models assessing AF burden from long-duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, the role of adequate external validation of the models and clinical trials to study true performance is discussed. CONCLUSION: Algorithms using AI to interpret ECGs in various new ways have been developed. While still, much work needs to be done, these technologies have shown enormous potential in a short span of time. With further advancements and continuous research, these novel ways of interpretation may well become part of everyday clinical workflow.

Cardiac resynchronization therapy response in cardiac sarcoidosis.

INTRODUCTION: Cardiac sarcoidosis (CS) is a nonischemic cardiomyopathy (NICM) characterized by infiltration of noncaseating granulomas involving the heart with highly variable clinical manifestations that can include conduction abnormalities and systolic heart failure. Cardiac resynchronization therapy (CRT) has shown significant promise in NICM, though little is known about its efficacy in patients with CS. OBJECTIVE: To determine if CRT improved cardiac remodeling in patients with CS. METHODS: We retrospectively reviewed all patients with a clinical or histological diagnosis of CS who underwent CRT implantation at the Mayo Clinic enterprise from 2000 to 2021. Baseline characteristics, imaging parameters, heart failure hospitalizations and need for advanced therapies, and major adverse cardiac events (MACE) were assessed. RESULTS: Our cohort was comprised of 55 patients with 61.8% male and a mean age of 58.7 ± 10.9 years. Eighteen (32.7%) patients had definite CS, 21 (38.2%) had probable CS, while 16 (29.1%) had presumed CS, and 26 (47.3%) with extracardiac sarcoidosis. The majority underwent CRT-D implantation (n = 52, 94.5%) and 3 (5.5%) underwent CRT-P implantation with 67.3% of implanted devices being upgrades from prior pacemakers or implantable cardioverter defibrillators. At 6 months postimplantation there was no significant improvement in ejection fraction (34.8 ± 10.9% vs. 37.7 ± 14.2%, p = .331) or left ventricular end-diastolic diameter (58.5 ± 10.2 vs. 57.5 ± 8.1 mm, p = .236), though mild improvement in left ventricular end systolic diameter (49.1 ± 9.9 vs. 45.7± 9.9 mm, p < .0001). Within the first 6 months postimplantation, 5 (9.1%) patients sustained a heart failure hospitalization. At a mean follow-up of 4.1± 3.7 years, 14 (25.5%) patients experienced a heart failure hospitalization, 11 (20.0%) underwent cardiac transplantation, 1 (1.8%) underwent left ventricular assist device implantation and 7 (12.7%) patients died. CONCLUSIONS: Our findings suggest variable response to CRT in patients with CS with no overall improvement in ventricular function within 6 months and a substantial proportion of patients progressing to advanced heart failure therapies.

Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation.

AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.

Data-Driven and Technology-Enabled Trial Innovations Toward Decentralization of Clinical Trials: Opportunities and Considerations.

Traditional trial designs have well-recognized inefficiencies and logistical barriers to participation. Decentralized trials and digital health solutions have been suggested as potential solutions and have certainly risen to the challenge during the pandemic. Clinical trial designs are now increasingly data driven. The use of distributed clinical data networks and digitization has helped to fundamentally upgrade existing research systems. A trial design may vary anywhere from fully decentralized to hybrid to traditional on-site. Various decentralization components are available for stakeholders to increase the reach and pace of their trials, such as electronic informed consent, remote interviews, administration, outcome assessment, monitoring, and laboratory and imaging modalities. Furthermore, digital health technologies can be included to enrich study conduct. However, careful consideration is warranted, including assessing verification and validity through usability studies and having various contingencies in place through dedicated risk assessment. Selecting the right combination depends not just on the ability to handle patient care and the medical know-how but also on the availability of appropriate technologic infrastructure, skills, and human resources. Throughout this process, quality of evidence generation and physician-patient relation must not be undermined. Here we also address some knowledge gaps, cost considerations, and potential impact of decentralization and digitization on inclusivity, recruitment, engagement, and retention. Last, we mention some future directions that may help drive the necessary change in the right direction.

Association Between Coffee Consumption and Incident Atrial Fibrillation (from the Multi-Ethnic Study of Atherosclerosis [MESA]).

This study aimed to elucidate a potential dose-dependent relation between coffee intake and atrial fibrillation (AF) incidence in a multi-ethnic setting. Previous studies were comprised mainly of White populations, and an exploration of dose dependency is limited. To address these gaps, we analyzed the Multi-Ethnic Study of Atherosclerosis data, a prospective cohort study. In the primary analysis, we crudely divided patients into 3 groups: nonconsumers, 1 to 3 cups/month, and ≥1 cup/week. For the secondary analysis, we stratified the cohort into 9 groups of gradual increments for coffee consumption. A multivariable cox proportional hazards regression model was adjusted for 6 potential confounders: age, gender, smoking, hypertension, diabetes mellitus, and alcohol. Subjects who drank ≥1 cup of coffee/week had a higher incidence of AF (adjusted hazard ratio 1.40, p = 0.015) than nonconsumers. Furthermore, in the secondary analysis, there was an overall trend, albeit not consistent, of increasing adjusted hazard ratio with progressively increasing doses of coffee in the following groups: 1 to 3 cups/month, 2 to 4 cups/week, 2 to 3 cups/day and ≥6 cups/day. Notably, AF incidence was highest (9.8%) for the group consuming the most coffee, that is, ≥6 cups/day (p = 0.02). Stratification by race/ethnicity suggested the results may be driven by White and Hispanic rather than Black or Chinese-American subgroups. In conclusion, the findings suggest an association between coffee consumption and incident AF in contrast to most previous studies.

Contemporary trends and barriers to oral anticoagulation therapy in Non-valvular atrial fibrillation during DOAC predominant era.

There is a need to reassess contemporary oral anticoagulation (OAC) trends and barriers against guideline directed therapy in the United States. Most previous studies were performed before major guideline changes recommended direct oral anticoagulant (DOAC) use over warfarin or have otherwise lacked patient level data. Data on overuse of OAC in low-risk group is also limited. To address these knowledge gaps, we performed a nationwide analysis to analyze current trends. This is a retrospective cohort study assessing non-valvular AF identified using a large United States de-identified administrative claims database, including commercial and Medicare Advantage enrollees. Prescription fills were assessed within a 90-day follow-up from the patient's index AF encounter between January 1, 2016, and December 31, 2020. Among the 339,197 AF patients, 4.4%, 8.0%, and 87.6% were in the low-, moderate-, and high-risk groups (according to CHA2DS2-VASc score). An over (29.6%) and under (52.2%) utilization of OAC was reported in low- and high-risk AF patients. A considerably high frequency for warfarin use was also noted among high-risk group patients taking OAC (33.1%). The results suggest that anticoagulation use for stroke prevention in the United States is still comparable to the pre-DOAC era studies. About half of newly diagnosed high-risk non-valvular AF patients remain unprotected against stroke risk. Several predictors of OAC and DOAC use were also identified. Our findings may identify a population at risk of complications due to under- or over-treatment and highlight the need for future quality improvement efforts.

Prevalence and Pattern of Congenital Coronary Artery Anomalies in Patients Undergoing Coronary Angiography at a Tertiary Care Hospital of Northern India.

OBJECTIVES: To evaluate the prevalence and pattern of congenital coronary artery anomalies (CAAs) in the adult population undergoing catheter coronary angiography. METHODS: The coronary angiograms done between October 2015 and September 2020 were reviewed for the presence of coronary anomalies based upon Angelini's classification. The medical record of patients with anomalies was reviewed for symptomatology and indication of angiography. RESULTS: CAAs were found in 129 (87 males and 42 females) of 6,258 patients giving a prevalence of 2.06%. The mean age was 57.8 ± 11.8 (range 32-81) years. Among these, the anomalous origin and course of the coronaries were the most common anomaly seen in 81 (1.29%) patients, followed by intrinsic anomalies of the coronary arterial system in 44 (0.7%) patients and anomalies of coronary termination and anomalous anastomotic vessels in 2 (0.03%) patients each. Overall, the absence of the left main trunk with a separate origin of the left anterior descending (LAD) and the circumflex artery was the commonest anomaly seen in 46 (0.74%) patients, followed by dual LAD in 35 (0.56%) patients. The anomalous origin of the right coronary artery (RCA) from the left sinus was seen in 14 patients (0.22%) and that of the circumflex artery from the right sinus or right coronary artery was seen in 11 patients (0.17%). The origin of the left main and RCA from ascending aorta was found in eight (0.13%) patients. One (0.02%) patient had a single coronary artery, and another one (0.02%) had all the three coronary arteries arising from the right sinus; however, with separate ostia. The split RCA was seen in nine (0.14%) patients and there were two (0.03%) patients each of coronary artery fistulae, and of anomalous anastomotic vessels. CONCLUSIONS: The prevalence of congenital coronary anomalies in this study was 2.06%. The commonest anomaly was that of origin and courses of the vessels, however, the pattern of anomalies is different from previous studies.