Kashou, Anthony H.

Introduction: Cardiac resynchronization therapy (CRT) has known benefit for heart failure (HF) with reduced left ventricular ejection fraction (LVEF) and dyssynchrony. However, very little is known about its benefit in infiltrative cardiomyopathies such as cardiac amyloidosis (CA) due to relative rarity of these conditions. Methods: This retrospective case series (2003-2021) comprised patients with HF due to transthyretin wild-type CA with (case) or without (control) CRT implant. Only controls with an indication for CRT or LVEF <40% were included. To further match disease severity, cases were 1:1 matched (propensity score tolerance 0.04) to controls for age, NYHA class, and CA stage (UK Staging). Primary outcome was MACE (major adverse cardiac events) which included stroke, myocardial infarction, HF hospitalization and cardiac mortality during a 3-year follow-up. Loss to follow-up was censored as per last visit date. LVEF and NYHA were noted closest to 1-year follow-up. Survival analysis for MACE was performed with Kaplan-Meier (KM) and multivariable cox regression models. Results: Study Cohort (n=160) had a mean age of 75.5 (SD 7.8) years and 97.5% were males. A majority had NYHA class 2 or 3 (92.3%) and CA stage 2 (42.2.%). Notably, median LVEF at baseline was lower in controls (33.5 vs 36.5; P = .001) and 77.5% of controls had an indication for CRT device. A total of 79 MACE events occurred, most frequent being HF hospitalization (n=45). KM curves were comparable for MACE events in the two groups (log-rank test; P = .059). Even after adjusting, as in table, CRT device was not associated with MACE events (adjusted HR 0.66; 95% CI 0.40-1.09; P = .106). Lastly, change in LVEF ( P = .84) and NYHA ( P = .86) at follow-up was similar in cases vs controls. Conclusions: The findings of this largest yet observational study to examine this question suggest that CRT in patients with transthyretin wild-type CA does not favorably impact outcomes for MACE events, improve LVEF or NYHA functional class.

AbstractBackgroundAccurate 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.ObjectiveDevelop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG.MethodsWe 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).ResultsOf 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.ConclusionAccurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

Abstract Background Accurate automated wide QRS complex tachycardia (WCT) discrimination between ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using novel calculations derived from computerized electrocardiogram (ECG) data from paired WCT and baseline ECGs. Purpose Our aim was to develop and trial novel WCT discrimination approaches for WCT patients with and without a corresponding baseline ECG. Central to this analysis was the creation and use of a novel parameter (i.e., percent monophasic time-voltage area [PMonoTVA] [%]) that may be derived from computerized ECG measurements present on the WCT ECG alone. Methods In a two-part study, we derived and tested WCT differentiation models comprised of novel and previously established parameters formulated from computerized data of paired WCT and baseline ECGs. In Part 1, novel and established parameters generated from WCT and baseline ECG data were used to derive, validate, and compare five different binary classification models: (i) logistic regression [LR], (ii) artificial neural network [ANN], (iii) Random Forests [RF], (iv) support vector machine [SVM], and (v) ensemble learning (EL). In Part 2, two unique LR models were derived, validated, and compared using parameters generated from computerized data of the (i) WCT ECG alone (i.e., Solo Model) and (ii) paired WCT and baseline ECGs (i.e., Paired Model). Results In Part 1, among 103 patients with VT or SWCT diagnoses established from corroborating electrophysiology studies or intra-cardiac device recordings, favorable diagnostic performance was achieved by all modeling technique subtypes: LR (area under the receiver operating characteristic curve [AUC] 0.95), ANN (AUC 0.91), RF (AUC 0.97), SVM (AUC 0.98), and EL (AUC 0.97). In Part 2, among 235 patients with a VT or SWCT diagnosis established with (Gold Standard cohort) or without (Non-Gold Standard cohort) a corroborating electrophysiology procedure or intra-cardiac device recording, favorable diagnostic performance was achieved by the Solo Model (AUC 0.86) and Paired Model (AUC 0.95) (Table). Conclusion Accurate WCT discrimination may be accomplished using novel parameters derived from computerized data of the WCT ECG alone and paired WCT and baseline ECGs. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health

Abstract 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.

In the context of atrial fibrillation (AF), traditional clinical practices have thus far 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. In this review we focus on the roles of artificial intelligence-enabled 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. One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Further, we overview some of the challenges facing AF screening and the harms and benefits of screening. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm and is discussed in detail with its potential uses. Knowledge gaps also remain regarding the best ways to monitor patients with ESUS and 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. 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, role of adequate external validation of the models and clinical trials to study true performance is discussed.

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes—termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence–enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

Since its inception, the electrocardiogram (ECG) has been an essential tool in medicine. The ECG is more than a mere tracing of cardiac electrical activity; it can detect and diagnose various pathologies including arrhythmias, pericardial and myocardial disease, electrolyte disturbances, and pulmonary disease. The ECG is a simple, non-invasive, rapid, and cost-effective diagnostic tool in medicine; however, its clinical utility relies on the accuracy of its interpretation. Computer ECG analysis has become so widespread and relied upon that ECG literacy among clinicians is waning. With recent technological advances, the application of artificial intelligence-augmented ECG (AI-ECG) algorithms has demonstrated the potential to risk stratify, diagnose, and even interpret ECGs—all of which can have a tremendous impact on patient care and clinical workflow. In this review, we examine (i) the utility and importance of the ECG in clinical practice, (ii) the accuracy and limitations of current ECG interpretation methods, (iii) existing challenges in ECG education, and (iv) the potential use of AI-ECG algorithms for comprehensive ECG interpretation.

AbstractBackgroundAutomated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification.MethodsA derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one “all‐inclusive” model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance.ResultsThe VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X‐lead QRS amplitude change, Y‐lead QRS amplitude change, and Z‐lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95).ConclusionCustom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.

Abstract Aims An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE). Methods and results We included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02–1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93–0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG. Conclusion The AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.