Citations

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.

Background: Transvenous implantable cardiac defibrillators (TV ICD) provide life-saving therapy for millions of patients worldwide. However, they are susceptible to several potential short- and long- term complications including cardiac perforation and pneumothorax, lead dislodgement, venous obstruction, and infection. The extravascular ICD (EV ICD) system’s novel design and substernal implant approach avoids the risks associated with TV ICDs while still providing pacing features and similar generator size to TV ICDs. Study Design: The EV ICD pivotal study is a prospective, multi-center, single-arm, non-randomized, pre-market clinical study designed to examine the safety and acute efficacy of the system. This study will enroll up to 400 patients with a Class I or IIa indication for implantation of an ICD. Implanted subjects will be followed up to approximately 3.5 years, depending on when the patient is enrolled. Objective: The clinical trial is designed to demonstrate safety and effectiveness of the EV ICD system in human use. The safety endpoint is freedom from major complications, while the efficacy endpoint is defibrillation success. Both endpoints will be assessed against prespecified criteria. Additionally, this study will evaluate antitachycardia pacing (ATP) performance, electrical performance, extracardiac pacing sensation, asystole pacing, appropriate and inappropriate shocks, as well as a summary of adverse events. Conclusion: The EV ICD pivotal study is designed to provide clear evidence addressing the safety and efficacy performance of the EV ICD System.

Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score > 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score > 0.5 was associated with infarcts ( p < 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.

Abstract Aims Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. Methods and results Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90–2.50). Conclusion An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.

AbstractBackgroundPulmonary vein isolation (PVI) with autonomic modulation may be more successful than PVI alone for atrial fibrillation (AF) ablation and may be signaled by changes in sinus rhythm heart rate (HR) post ablation. We sought to determine if a change in sinus rhythm HR predicted AF recurrence post PVI.MethodsPatients who underwent AF ablation from 2000 to 2011 were included if sinus rhythm was noted on ECG within 90 days pre and 7 days post ablation. Basic ECG interval and HR changes were analyzed and outcomes determined.ResultsA total of 1152 patients were identified (74.3% male, mean age 57 ± 11 years). Mean AF duration was 5.2 ± 5.3 years. Paroxysmal AF was noted in 712 (61.8%) of the patients. Mean EF was 61% ± 6%. Sinus rhythm HR was 61 ± 11 pre‐ablation and 76 ± 13 bpm post‐ablation (27% ± 24% increase, p < .001). The ability of relative HR change post‐ablation to predict AF recurrence was borderline (hazard ratio 0.65 [0.41–1.01], p = .067). With patients separated into quartiles based on the relative HR change, the upper quartile with the largest relative increase in HR had a significantly lower rate of AF recurrence compared to the lowest quartile following multi variable modeling (p = .038). There were significant changes in PR (171 ± 28 to 167 ± 30 ms) and QTc (424 ± 25 to 434 ± 29 ms) intervals (both p < .001) but these were not predictive of outcome.ConclusionRelative changes in HR post AF ablation correlates with AF recurrence. Further prospective studies are needed to confirm this relationship.

Background: Heart rate–corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2–mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)–enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the “gold standard”. The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (−1.76±23.14 ms). Similarly, within the prospective, genetic heart disease–enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (−0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN’s ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

AbstractBackgroundThe subcutaneous implantable cardioverter‐defibrillator (S‐ICD) has a larger generator and its implantation involves more dissection and tunneling compared to traditional transvenous defibrillator system. Liposomal bupivacaine, an extended‐release bupivacaine with 72 h of duration has been used for postoperative pain management in patients undergoing S‐ICD implantation. Our aim was to compare postoperative pain and opioid prescription patterns among patients undergoing S‐ICD implantation who received intraprocedural liposomal bupivacaine and those who did not.MethodsWe performed a retrospective analysis of all patients who underwent subcutaneous ICD implantation from January 1, 2013 to March 30, 2018 at the Mayo Clinic in Rochester, Minnesota. Patients were categorized into those who received liposomal bupivacaine and those who did not. Data on inpatient pain score, outpatient opioid prescription rates at discharge, and doses based on oral morphine equivalents (OME) were collected.ResultsA total of 104 patients underwent S‐ICD implantation. Intraprocedural liposomal bupivacaine was used in 69% of patients. Patients who received intraprocedural liposomal bupivacaine had similar mean inpatient pain scores (2.9 vs. 2.9, p = .786). There was also no difference in the rate of inpatient opioid administration (79.2% vs. 87.5%, p = .4139), outpatient opioid prescription (23.6% vs. 12.5%, p = .29), or mean OME (41.7‐mg vs. 16.6‐mg, p = .188) when comparing patients those who received intraprocedural liposomal bupivacaine and those who did not.ConclusionIntraprocedural liposomal bupivacaine administration was not associated with any significant impact on postoperative pain scores, inpatient opioid administration, and outpatient opioid prescription rates or OME amounts at discharge.

Background An artificial intelligence algorithm that detects age using the 12‐lead ECG has been suggested to signal “physiologic age.” This study aimed to investigate the association of peripheral microvascular endothelial function (PMEF) as an index of vascular aging, with accelerated physiologic aging gauged by ECG‐derived artificial intelligence–estimated age. Methods and Results This study included 531 patients who underwent ECG and a noninvasive PMEF assessment using reactive hyperemia peripheral arterial tonometry. Abnormal PMEF was defined as reactive hyperemia peripheral arterial tonometry index ≤2.0. Accelerated or delayed physiologic aging was calculated by the Δ age (ECG‐derived artificial intelligence–estimated age minus chronological age), and the association between Δ age and PMEF as well as its impact on composite major adverse cardiovascular events were investigated. Δ age was higher in patients with abnormal PMEF than in patients with normal PMEF (2.3±7.8 versus 0.5±7.7 years; P =0.01). Reactive hyperemia peripheral arterial tonometry index was negatively associated with Δ age after adjustment for cardiovascular risk factors (standardized β coefficient, –0.08; P =0.048). The highest quartile of Δ age was associated with an increased risk of major adverse cardiovascular events compared with the first quartile of Δ age in patients with abnormal PMEF, even after adjustment for cardiovascular risk factors (hazard ratio, 4.72; 95% CI, 1.24–17.91; P =0.02). Conclusions Vascular aging detected by endothelial function is associated with accelerated physiologic aging, as assessed by the artificial intelligence–ECG Δ age. Patients with endothelial dysfunction and the highest quartile of accelerated physiologic aging have a marked increase in risk for cardiovascular events.

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.