Publications by authors named "Paul A Friedman"

385 Publications

Application of artificial intelligence to the electrocardiogram.

Eur Heart J 2021 Sep 17. Epub 2021 Sep 17.

Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.

Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.
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http://dx.doi.org/10.1093/eurheartj/ehab649DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500024PMC
September 2021

Direct Intramyocardial Ethanol Injection for Premature Ventricular Contraction Arising From the Inaccessible Left Ventricular Summit.

JACC Clin Electrophysiol 2021 Aug 17. Epub 2021 Aug 17.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA. Electronic address:

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http://dx.doi.org/10.1016/j.jacep.2021.07.008DOI Listing
August 2021

First-in-Human Use of a Novel Live 3D Intracardiac Echo Probe to Guide Left Atrial Appendage Closure.

JACC Cardiovasc Interv 2021 Aug 18. Epub 2021 Aug 18.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

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http://dx.doi.org/10.1016/j.jcin.2021.07.024DOI Listing
August 2021

Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents.

Int J Cardiol 2021 Oct 19;340:42-47. Epub 2021 Aug 19.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Department of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology, Mayo Clinic, Rochester, MN, United States of America; Department of Molecular Pharmacology & Experimental Therapeutics; Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, MN, United States of America. Electronic address:

Background: There is no established screening approach for hypertrophic cardiomyopathy (HCM). We recently developed an artificial intelligence (AI) model for the detection of HCM based on the 12‑lead electrocardiogram (AI-ECG) in adults. Here, we aimed to validate this approach of ECG-based HCM detection in pediatric patients (age ≤ 18 years).

Methods: We identified a cohort of 300 children and adolescents with HCM (mean age 12.5 ± 4.6 years, male 68%) who had an ECG and echocardiogram at our institution. Patients were age- and sex-matched to 18,439 non-HCM controls. Diagnostic performance of the AI-ECG model for the detection of HCM was estimated using the previously identified optimal diagnostic threshold of 11% (the probability output derived by the model above which an ECG is considered to belong to an HCM patient).

Results: Mean AI-ECG probabilities of HCM were 92% and 5% in the case and control groups, respectively. The area under the receiver operating characteristic curve (AUC) of the AI-ECG model for HCM detection was 0.98 (95% CI 0.98-0.99) with corresponding sensitivity 92% and specificity 95%. The positive and negative predictive values were 22% and 99%, respectively. The model performed similarly in males and females and in genotype-positive and genotype-negative HCM patients. Performance tended to be superior with increasing age. In the age subgroup <5 years, the test's AUC was 0.93. In comparison, the AUC was 0.99 in the age subgroup 15-18 years.

Conclusions: A deep-learning, AI model can detect pediatric HCM with high accuracy from the standard 12‑lead ECG.
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http://dx.doi.org/10.1016/j.ijcard.2021.08.026DOI Listing
October 2021

Coronary Microvascular Dysfunction and the Risk of Atrial Fibrillation From an Artificial Intelligence-Enabled Electrocardiogram.

Circ Arrhythm Electrophysiol 2021 Aug 16;14(8):e009947. Epub 2021 Aug 16.

Department of Cardiovascular Medicine (A.A., M.T.C., T.T., Z.I.A., P.A.N., F.L.-J., M.S.C., J.D.S., I.O., S.K., P.A.F., A.L.), Mayo Clinic, Rochester, MN.

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http://dx.doi.org/10.1161/CIRCEP.121.009947DOI Listing
August 2021

Use of Artificial Intelligence Tools Across Different Clinical Settings: A Cautionary Tale.

Circ Cardiovasc Qual Outcomes 2021 Sep 16;14(9):e008153. Epub 2021 Aug 16.

Department of Cardiovascular Medicine (K.C.S., P.A.N., Z.I.A., P.A.F., B.J.G.), Mayo Clinic, Rochester, MN.

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http://dx.doi.org/10.1161/CIRCOUTCOMES.121.008153DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455449PMC
September 2021

Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.

Mayo Clin Proc 2021 08;96(8):2081-2094

Department of Cardiovascular Medicine, The University of Kansas Health System, Kansas City, KS.

Objective: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).

Methods: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site.

Results: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%.

Conclusion: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.
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http://dx.doi.org/10.1016/j.mayocp.2021.05.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8327278PMC
August 2021

The Role of Artificial Intelligence in Arrhythmia Monitoring.

Card Electrophysiol Clin 2021 09 8;13(3):543-554. Epub 2021 Jul 8.

Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. Electronic address:

Arrhythmia management has been revolutionized by the ability to monitor the cardiac rhythm in a patient's home environment in real-time using high-fidelity prescription-grade and commercially available wearable electrodes. The vast amount of digitally acquired electrophysiological signals has generated the need for scalable and efficient data processing with actionable output that can be provided directly to clinicians and patients. In this setting, artificial intelligence applications are increasingly important in arrhythmia monitoring, ranging from conventional algorithmic analysis for rhythm determination to more complex deep machine learning methods that have led to the realization of fully automated humanlike rhythm determination in real-time.
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http://dx.doi.org/10.1016/j.ccep.2021.04.011DOI Listing
September 2021

Artificial Intelligence-Enabled Electrocardiography to Screen Patients with Dilated Cardiomyopathy.

Am J Cardiol 2021 09 24;155:121-127. Epub 2021 Jul 24.

Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota. Electronic address:

Undiagnosed dilated cardiomyopathy (DC) can be asymptomatic or present as sudden cardiac death, therefore pre-emptively identifying and treating patients may be beneficial. Screening for DC with echocardiography is expensive and labor intensive and standard electrocardiography (ECG) is insensitive and non-specific. The performance and applicability of artificial intelligence-enabled electrocardiography (AI-ECG) for detection of DC is unknown. Diagnostic performance of an AI algorithm in determining reduced left ventricular ejection fraction (LVEF) was evaluated in a cohort that comprised of DC and normal LVEF control patients. DC patients and controls with 12-lead ECGs and a reference LVEF measured by echocardiography performed within 30 and 180 days of the ECG respectively were enrolled. The model was tested for its sensitivity, specificity, negative predictive (NPV) and positive predictive values (PPV) based on the prevalence of DC at 1% and 5%. The cohort consisted of 421 DC cases (60% males, 57±15 years, LVEF 28±11%) and 16,025 controls (49% males, age 69 ±16 years, LVEF 62±5%). For detection of LVEF≤45%, the area under the curve (AUC) was 0.955 with a sensitivity of 98.8% and specificity 44.8%. The NPV and PPV were 100% and 1.8% at a DC prevalence of 1% and 99.9% and 8.6% at a prevalence of 5%, respectively. In conclusion AI-ECG demonstrated high sensitivity and negative predictive value for detection of DC and could be used as a simple and cost-effective screening tool with implications for screening first degree relatives of DC patients.
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http://dx.doi.org/10.1016/j.amjcard.2021.06.021DOI Listing
September 2021

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

J Stroke Cerebrovasc Dis 2021 Sep 22;30(9):105998. Epub 2021 Jul 22.

Cardiovascular Diseases, Mayo Clinic, Rochester, MN 55905, USA. Electronic address:

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.
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http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2021.105998DOI Listing
September 2021

Anatomic Approach to Transseptal Puncture for Structural Heart Interventions.

JACC Cardiovasc Interv 2021 Jul;14(14):1509-1522

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA. Electronic address:

The use of transseptal puncture continues to rise given the increase in left atrial cardiac interventions. The authors review an anatomic approach to transseptal puncture incorporating multimodality imaging both pre- and intraprocedurally with stepwise escalation algorithms to ensure safe and efficacious large-bore transseptal puncture.
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http://dx.doi.org/10.1016/j.jcin.2021.04.037DOI Listing
July 2021

Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization.

J Cardiovasc Electrophysiol 2021 09 27;32(9):2504-2514. Epub 2021 Jul 27.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Introduction: The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders.

Methods: We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response.

Results: We compared the performance of EoE against traditional ML methods and the state-of-the-art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1-score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results.

Conclusion: The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high-risk patients.
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http://dx.doi.org/10.1111/jce.15171DOI Listing
September 2021

The effect of cardiac rhythm on artificial intelligence-enabled ECG evaluation of left ventricular ejection fraction prediction in cardiac intensive care unit patients.

Int J Cardiol 2021 Sep 7;339:54-55. Epub 2021 Jul 7.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America. Electronic address:

The presence of left ventricular systolic dysfunction (LVSD) alters clinical management and prognosis in most acute and chronic cardiovascular conditions. While transthoracic echocardiography (TTE) remains the most common diagnostic tool to screen for LVSD, it is operator-dependent, time-consuming, effort-intensive, and relatively expensive. Recent work has demonstrated the ability of an artificial intelligence-augment ECG (AI-ECG) model to accurately predict LVSD in critical intensive care unit (CICU) patients. We demonstrate that the AI-ECG algorithm can maintain its performance in these patients with and without AF despite their clinical differences. An AI-ECG algorithm can serve as a non-invasive, inexpensive, and rapid screening tool for early detection of LVSD in resource-limited settings, and potentially expedite clinical decision making and guideline-directed therapies in the acute care setting.
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http://dx.doi.org/10.1016/j.ijcard.2021.07.001DOI Listing
September 2021

Artificial Intelligence-Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis.

Mayo Clin Proc 2021 Jul 1. Epub 2021 Jul 1.

Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN.

Objective: To develop an artificial intelligence (AI)-based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG).

Methods: We collected 12-lead ECG data from 2541 patients with light chain or transthyretin CA seen at Mayo Clinic between 2000 and 2019. Cases were nearest neighbor matched for age and sex, with 2454 controls. A subset of 2997 (60%) cases and controls were used to train a deep neural network to predict the presence of CA with an internal validation set (n=999; 20%) and a randomly selected holdout testing set (n=999; 20%). We performed experiments using single-lead and 6-lead ECG subsets.

Results: The area under the receiver operating characteristic curve (AUC) was 0.91 (CI, 0.90 to 0.93), with a positive predictive value for detecting either type of CA of 0.86. By use of a cutoff probability of 0.485 determined by the Youden index, 426 (84%) of the holdout patients with CA were detected by the model. Of the patients with CA and prediagnosis electrocardiographic studies, the AI model successfully predicted the presence of CA more than 6 months before the clinical diagnosis in 59%. The best single-lead model was V5 with an AUC of 0.86 and a precision of 0.78, with other single leads performing similarly. The 6-lead (bipolar leads) model had an AUC of 0.90 and a precision of 0.85.

Conclusion: An AI-driven ECG model effectively detects CA and may promote early diagnosis of this life-threatening disease.
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http://dx.doi.org/10.1016/j.mayocp.2021.04.023DOI Listing
July 2021

Artificial Intelligence-Augmented Electrocardiogram Detection of Left Ventricular Systolic Dysfunction in the General Population.

Mayo Clin Proc 2021 Oct 10;96(10):2576-2586. Epub 2021 Jun 10.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN. Electronic address:

Objective: To validate an artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm for the detection of preclinical left ventricular systolic dysfunction (LVSD) in a large community-based cohort.

Methods: We identified a randomly selected community-based cohort of 2041 subjects age 45 years or older in Olmsted County, Minnesota. All participants underwent a study echocardiogram and ECG. We first assessed the performance of the AI-ECG to identify LVSD (ejection fraction ≤40%). After excluding participants with clinical heart failure, we further assessed the AI-ECG to detect preclinical LVSD among all patients (n=1996) and in a high-risk subgroup (n=1348). Next we modelled an imputed screening program for preclinical LVSD detection where a positive AI-ECG triggered an echocardiogram. Finally, we assessed the ability of the AI-ECG to predict future LVSD. Participants were enrolled between January 1, 1997, and September 30, 2000; and LVSD surveillance was performed for 10 years after enrollment.

Results: For detection of LVSD in the total population (prevalence, 2.0%), the area under the receiver operating curve for AI-ECG was 0.97 (sensitivity, 90%; specificity, 92%); in the high-risk subgroup (prevalence 2.7%), the area under the curve was 0.97 (sensitivity, 92%; specificity, 93%). In an imputed screening program, identification of one preclinical LSVD case would require 88.3 AI-ECGs and 8.7 echocardiograms in the total population and 65.7 AI-ECGs and 5.5 echocardiograms in the high-risk subgroup. The unadjusted hazard ratio for a positive AI-ECG for incident LVSD over 10 years was 2.31 (95% CI, 1.32 to 4.05; P=.004).

Conclusion: Artificial intelligence-augmented ECG can identify preclinical LVSD in the community and warrants further study as a screening tool for preclinical LVSD.
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http://dx.doi.org/10.1016/j.mayocp.2021.02.029DOI Listing
October 2021

Cost Effectiveness of an Electrocardiographic Deep Learning Algorithm to Detect Asymptomatic Left Ventricular Dysfunction.

Mayo Clin Proc 2021 07 9;96(7):1835-1844. Epub 2021 Jun 9.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.

Objective: To evaluate the cost-effectiveness of an artificial intelligence electrocardiogram (AI-ECG) algorithm under various clinical and cost scenarios when used for universal screening at age 65.

Patients And Methods: We used decision analytic modeling to perform a cost-effectiveness analysis of the use of AI-ECG to screen for asymptomatic left ventricular dysfunction (ALVD) once at age 65 compared with no screening. This screening consisted of an initial screening decision tree and subsequent construction of a Markov model. One-way sensitivity analysis on various disease and cost parameters to evaluate cost-effectiveness at both $50,000 per quality-adjusted life year (QALY) and $100,000 per QALY willingness-to-pay threshold.

Results: We found that for universal screening at age 65, the novel AI-ECG algorithm would cost $43,351 per QALY gained, test performance, disease characteristics, and testing cost parameters significantly affect cost-effectiveness, and screening at ages 55 and 75 would cost $48,649 and $52,072 per QALY gained, respectively. Overall, under most of the clinical scenarios modeled, coupled with its robust test performance in both testing and validation cohorts, screening with the novel AI-ECG algorithm appears to be cost-effective at a willingness-to-pay threshold of $50,000.

Conclusion: Universal screening for ALVD with the novel AI-ECG appears to be cost-effective under most clinical scenarios with a cost of <$50,000 per QALY. Cost-effectiveness is particularly sensitive to both the probability of disease progression and the cost of screening and downstream testing. To improve cost-effectiveness modeling, further study of the natural progression and treatment of ALVD and external validation of AI-ECG should be undertaken.
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http://dx.doi.org/10.1016/j.mayocp.2020.11.032DOI Listing
July 2021

Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial.

Am Heart J 2021 09 24;239:73-79. Epub 2021 May 24.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.

Background: Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive.

Objectives: To build infrastructure for digital trials to improve efficiency and generalizability and test it using a study to validate an artificial intelligence algorithm to detect atrial fibrillation (AF).

Design: We will prospectively enroll 1,000 patients who underwent an electrocardiogram for any clinical reason in routine practice, do not have a previous diagnosis of AF or atrial flutter and would be eligible for anticoagulation if AF is detected. Eligible patients will be identified using digital phenotyping algorithms, including natural language processing that runs on the electronic health records. Study invitations will be sent in batches via patient portal or letter, which will direct patients to a website to verify eligibility, learn about the study (including video-based informed consent), and consent electronically. The method aims to enroll participants representative of the general patient population, rather than a convenience sample of patients presenting to clinic. A device will be mailed to patients to continuously monitor for up to 30 days. The primary outcome is AF diagnosis and burden; secondary outcomes include patients' experience with the trial conduct methods and the monitoring device. The enrollment, intervention, and follow-up will be conducted remotely, ie, a patient-centered site-less trial.

Summary: This is among the first wave of trials to adopt digital technologies, artificial intelligence, and other pragmatic features to create efficiencies, which will pave the way for future trials in a broad range of disease and treatment areas. Clinicaltrials.gov: NCT04208971.
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http://dx.doi.org/10.1016/j.ahj.2021.05.006DOI Listing
September 2021

Temporal Incidence and Predictors of High-Grade Atrioventricular Block After Transcatheter Aortic Valve Replacement.

J Am Heart Assoc 2021 05 7;10(10):e020033. Epub 2021 May 7.

Department of Cardiovascular Medicine Mayo Clinic Rochester MN.

Background The temporal incidence of high-grade atrioventricular block (HAVB) after transcatheter aortic valve replacement (TAVR) is uncertain. As a result, periprocedural monitoring and pacing strategies remain controversial. This study aimed to describe the temporal incidence of initial episode of HAVB stratified by pre- and post-TAVR conduction and identify predictors of delayed events. Methods and Results Consecutive patients undergoing TAVR at a single center between February 2012 and June 2019 were retrospectively assessed for HAVB within 30 days. Patients with prior aortic valve replacement, permanent pacemaker (PPM), or conversion to surgical replacement were excluded. Multivariable logistic regression was performed to assess predictors of delayed HAVB (initial event >24 hours post-TAVR). A total of 953 patients were included in this study. HAVB occurred in 153 (16.1%). After exclusion of those with prophylactic PPM placed post-TAVR, the incidence of delayed HAVB was 33/882 (3.7%). Variables independently associated with delayed HAVB included baseline first-degree atrioventricular block or right bundle-branch block, self-expanding valve, and new left bundle-branch block. Forty patients had intraprocedural transient HAVB, including 16 who developed HAVB recurrence and 6 who had PPM implantation without recurrence. PPM was placed for HAVB in 130 (13.6%) (self-expanding valve, 23.7% versus balloon-expandable valve, 11.9%; <0.001). Eight (0.8%) patients died by 30 days, including 1 unexplained without PPM present. Conclusions Delayed HAVB occurs with higher frequency in patients with baseline first-degree atrioventricular block or right bundle-branch block, new left bundle-branch block, and self-expanding valve. These findings provide insight into optimal monitoring and pacing strategies based on periprocedural ECG findings.
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http://dx.doi.org/10.1161/JAHA.120.020033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200694PMC
May 2021

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

Nat Med 2021 05 6;27(5):815-819. Epub 2021 May 6.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01-1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08-1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.
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http://dx.doi.org/10.1038/s41591-021-01335-4DOI Listing
May 2021

Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review.

J Clin Med 2021 Mar 30;10(7). Epub 2021 Mar 30.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA.

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.
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http://dx.doi.org/10.3390/jcm10071391DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8037652PMC
March 2021

Electrocardiogram screening for aortic valve stenosis using artificial intelligence.

Eur Heart J 2021 08;42(30):2885-2896

Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.

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.
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http://dx.doi.org/10.1093/eurheartj/ehab153DOI Listing
August 2021

Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.

Eur Heart J Acute Cardiovasc Care 2021 Jun;10(5):532-541

Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.

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.
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http://dx.doi.org/10.1093/ehjacc/zuaa021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245143PMC
June 2021

Sinus rhythm heart rate increase after atrial fibrillation ablation is associated with lower risk of arrhythmia recurrence.

Pacing Clin Electrophysiol 2021 04 25;44(4):651-656. Epub 2021 Feb 25.

Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA.

Background: Pulmonary 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.

Methods: Patients 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.

Results: A 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.

Conclusion: Relative changes in HR post AF ablation correlates with AF recurrence. Further prospective studies are needed to confirm this relationship.
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http://dx.doi.org/10.1111/pace.14197DOI Listing
April 2021

Sound wave balloon-assisted device implantation: a novel approach that merits consideration.

Europace 2021 05;23(5):747

Department of Cardiovascular Diseases, Mayo Clinic, 200 First St SW, Rochester, MN, USA.

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http://dx.doi.org/10.1093/europace/euaa281DOI Listing
May 2021

Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram.

JAMA Cardiol 2021 May;6(5):532-538

Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota.

Importance: Long QT syndrome (LQTS) is characterized by prolongation of the QT interval and is associated with an increased risk of sudden cardiac death. However, although QT interval prolongation is the hallmark feature of LQTS, approximately 40% of patients with genetically confirmed LQTS have a normal corrected QT (QTc) at rest. Distinguishing patients with LQTS from those with a normal QTc is important to correctly diagnose disease, implement simple LQTS preventive measures, and initiate prophylactic therapy if necessary.

Objective: To determine whether artificial intelligence (AI) using deep neural networks is better than the QTc alone in distinguishing patients with concealed LQTS from those with a normal QTc using a 12-lead electrocardiogram (ECG).

Design, Setting, And Participants: A diagnostic case-control study was performed using all available 12-lead ECGs from 2059 patients presenting to a specialized genetic heart rhythm clinic. Patients were included if they had a definitive clinical and/or genetic diagnosis of type 1, 2, or 3 LQTS (LQT1, 2, or 3) or were seen because of an initial suspicion for LQTS but were discharged without this diagnosis. A multilayer convolutional neural network was used to classify patients based on a 10-second, 12-lead ECG, AI-enhanced ECG (AI-ECG). The convolutional neural network was trained using 60% of the patients, validated in 10% of the patients, and tested on the remaining patients (30%). The study was conducted from January 1, 1999, to December 31, 2018.

Main Outcomes And Measures: The goal of the study was to test the ability of the convolutional neural network to distinguish patients with LQTS from those who were evaluated for LQTS but discharged without this diagnosis, especially among patients with genetically confirmed LQTS but a normal QTc value at rest (referred to as genotype positive/phenotype negative LQTS, normal QT interval LQTS, or concealed LQTS).

Results: Of the 2059 patients included, 1180 were men (57%); mean (SD) age at first ECG was 21.6 (15.6) years. All 12-lead ECGs from 967 patients with LQTS and 1092 who were evaluated for LQTS but discharged without this diagnosis were included for AI-ECG analysis. Based on the ECG-derived QTc alone, patients were classified with an area under the curve (AUC) value of 0.824 (95% CI, 0.79-0.858); using AI-ECG, the AUC was 0.900 (95% CI, 0.876-0.925). Furthermore, in the subset of patients who had a normal resting QTc (<450 milliseconds), the QTc alone distinguished those with LQTS from those without LQTS with an AUC of 0.741 (95% CI, 0.689-0.794), whereas the AI-ECG increased this discrimination to an AUC of 0.863 (95% CI, 0.824-0.903). In addition, the AI-ECG was able to distinguish the 3 main genotypic subgroups (LQT1, LQT2, and LQT3) with an AUC of 0.921 (95% CI, 0.890-0.951) for LQT1 compared with LQT2 and 3, 0.944 (95% CI, 0.918-0.970) for LQT2 compared with LQT1 and 3, and 0.863 (95% CI, 0.792-0.934) for LQT3 compared with LQT1 and 2.

Conclusions And Relevance: In this study, the AI-ECG was found to distinguish patients with electrocardiographically concealed LQTS from those discharged without a diagnosis of LQTS and provide a nearly 80% accurate pregenetic test anticipation of LQTS genotype status. This model may aid in the detection of LQTS in patients presenting to an arrhythmia clinic and, with validation, may be the stepping stone to similar tools to be developed for use in the general population.
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http://dx.doi.org/10.1001/jamacardio.2020.7422DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876623PMC
May 2021

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.

Nat Rev Cardiol 2021 Jul 1;18(7):465-478. Epub 2021 Feb 1.

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular medicine. Although the ECG has long offered valuable insights into cardiac and non-cardiac health and disease, its interpretation requires considerable human expertise. Advanced AI methods, such as deep-learning convolutional neural networks, have enabled rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation and hypertrophic cardiomyopathy, as well as the determination of a person's age, sex and race, among other phenotypes. The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making in patients with cardiovascular disease and critically appraise potential limitations and unknowns.
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http://dx.doi.org/10.1038/s41569-020-00503-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7848866PMC
July 2021

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

Circulation 2021 03 1;143(13):1274-1286. Epub 2021 Feb 1.

Division of Heart Rhythm Services, Windland Smith Rice Genetic Heart Rhythm Clinic (L.W.D., Z.I.A., P.A.N., P.A.F., M.J.A.), Mayo Clinic, Rochester, MN.

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.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.120.050231DOI Listing
March 2021

Liposomal bupivacaine during subcutaneous implantable cardioverter defibrillator implantation for pain management.

Pacing Clin Electrophysiol 2021 03 3;44(3):513-518. Epub 2021 Feb 3.

Department of cardiovascular diseases, Mayo Clinic Rochester, Rochester, Minnesota, USA.

Background: The 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.

Methods: We 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.

Results: A 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.

Conclusion: Intraprocedural 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.
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http://dx.doi.org/10.1111/pace.14175DOI Listing
March 2021

Injectable Flexible Subcutaneous Electrode Array Technology for Electrocardiogram Monitoring Device.

ACS Biomater Sci Eng 2020 05 15;6(5):2652-2658. Epub 2019 Nov 15.

Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.

Implantable cardiac monitors have undergone considerable miniaturization. However, they continue to be associated with complications such as infection, bleeding/bruising, and device extrusion or migration. In this paper, we demonstrate the feasibility of using a small, flexible, injectable, subcutaneous microelectrode-based device to record electrocardiograms (ECGs). We describe the fabrication process and demonstrate the ease of insertion of the injectable ECG device in vivo swine model. We also demonstrate our device's high-density channel microelectrode array's ability to detect the P, R, and T waves. The amplitude of these waves showed excellent correlation with distance of the bipolar electrodes used to detect them. Given the success of our initial studies, this device has the potential to improve the safety profile of implantable cardiac monitors and simplify the implantation procedure to allow for placement in a primary care setting.
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http://dx.doi.org/10.1021/acsbiomaterials.9b01102DOI Listing
May 2020

Vascular Aging Detected by Peripheral Endothelial Dysfunction Is Associated With ECG-Derived Physiological Aging.

J Am Heart Assoc 2021 02 17;10(3):e018656. Epub 2021 Jan 17.

Department of Cardiovascular Medicine Mayo Clinic Rochester MN.

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; =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; =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; =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.
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http://dx.doi.org/10.1161/JAHA.120.018656DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955452PMC
February 2021
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