Citations

Abstract Aims Electrocardiogram (ECG)-enabled stethoscope (ECG-Scope) acquires a single-lead ECGs during cardiac auscultation and may facilitate real-time screening for pathologies not routinely identified by cardiac auscultation alone. We previously demonstrated an artificial intelligence (AI) algorithm can identify left ventricular dysfunction (LVSD) [defined as ejection fraction (EF) ≤ 40%] with an area under the curve (AUC) of 0.91 using a 12-lead ECG. Methods and results One hundred patients referred for clinically indicated echocardiography were prospectively recruited. ECG-Scope recordings with the patient supine and sitting were obtained in multiple electrode locations at the time of the echocardiogram. The AI algorithm for the detection of LVSD was retrained using single leads from ECG-12 and validated against ECG-Scope to determine accuracy for low EF detection (≤35%, <40%, or <50%). We evaluated the algorithm with respect to body position and lead location. Amongst 100 patients (aged 61.3 ± 13.8; 61% male, BMI: 30.0 ± 5.4), eight had EF≤40%, and six had EF 40–50%. The best single recording position was V2 with the patient supine [AUC: 0.88 (CI: 0.80–0.97) for EF≤35%, 0.85 (CI: 0.75–0.95) for EF≤40%, and 0.81 (CI: 0.71–0.90) for EF < 50%]. When using an AI model to select the recording automatically, AUC was 0.91 (CI: 0.84–0.97) for EF≤35%, 0.89 (CI: 0.83–0.96) for EF≤40%, and 0.84 (CI: 0.73–0.94) for EF < 50%. Conclusion An AI algorithm applied to an ECG-enabled stethoscope recording in standard auscultation positions reliably detected the presence of a low EF in this prospective study of patients referred for echocardiography. The ability to screen patients with a possible low EF during routine physical examination may facilitate rapid detection of LVSD.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Abstract Aims Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2–5 years prior to AF, 23.6% 1–2 years prior to AF, 34.0% 0–3 months prior to AF, 40.9% 0–3 months after AF, 35.2% 1–2 years after AF, and 42.2% 2–5 years after AF (P < 0.001). The rate of increase prior to AF was higher for age >50 years CHA2DS2-VASc score ≥4. Conclusion The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1–2 years following AF and continues to increase thereafter.

Antiarrhythmic drugs (AAD) play an important role in the management of arrhythmias. Drug interactions involving AAD are common in clinical practice. As AADs have a narrow therapeutic window, both pharmacokinetic as well as pharmacodynamic interactions involving AAD can result in serious adverse drug reactions ranging from arrhythmia recurrence, failure of device-based therapy, and heart failure, to death. Pharmacokinetic drug interactions frequently involve the inhibition of key metabolic pathways, resulting in accumulation of a substrate drug. Additionally, over the past 2 decades, the P-gp (permeability glycoprotein) has been increasingly cited as a significant source of drug interactions. Pharmacodynamic drug interactions involving AADs commonly involve additive QT prolongation. Amiodarone, quinidine, and dofetilide are AADs with numerous and clinically significant drug interactions. Recent studies have also demonstrated increased morbidity and mortality with the use of digoxin and other AAD which interact with P-gp. QT prolongation is an important pharmacodynamic interaction involving mainly Vaughan-Williams class III AAD as many commonly used drug classes, such as macrolide antibiotics, fluoroquinolone antibiotics, antipsychotics, and antiemetics prolong the QT interval. Whenever possible, serious drug-drug interactions involving AAD should be avoided. If unavoidable, patients will require closer monitoring and the concomitant use of interacting agents should be minimized. Increasing awareness of drug interactions among clinicians will significantly improve patient safety for patients with arrhythmias.

Introduction: OAC are recommended for AF patients with CHA 2 DS 2 -VASc ≥2 in men or ≥3 in women (high-risk group), may be considered in patients with CHA 2 DS 2 -VASc 1 in men or 2 in women (medium-risk group), and are reasonable to omit in patients with CHA 2 DS 2 -VASc 0 in men or 1 in women (low-risk group). Furthermore, NOACs are recommended over warfarin in most AF patients. This study aimed to investigate AF stroke prevention treatment patterns in patients managed in routine practice, in particular, the under-use of OAC in the high-risk group and over-use in the low-risk group. Methods: Adult patients with AF were identified using de-identified claims for commercial and Medicare Advantage enrollees in the OptumLabs Data Warehouse. Prescription fills were assessed within 90 days of a patient’s AF encounter between 1/1/2016-12/31/2020. Patients with other indications for OAC, such as VTE, recent joint replacement, prior mechanical or bioprosthetic valve replacement, rheumatic mitral stenosis, and patients who underwent LAAO were excluded. Results: Among the 339,197 AF patients, 4.4%, 8.0%, and 87.6% were in the low-, medium-, and high-risk groups, respectively. The mean age was 51.2 ±10.5 yr, 59.9 ±9.4 yr, and 75.1 ±8.8 yr. In the low-risk group, 29.6% were treated with OAC, whereas in the high-risk group, 40.3% were not treated with any antithrombotic therapy and 7.5% used thienopyridine only. Although 71% of patients on OAC were on a NOAC, the proportion of patients on NOAC decreased from 91% in the low-risk group to 69% in the high-risk group. Conclusions: There are potentially both under-use of guideline-recommended stroke prevention therapies in the high-risk patients and over-use in the low-risk group. Because patients at a high risk of stroke are also at a high risk of major and intracranial bleeding who might benefit from NOACs, the decreased use of NOAC in high-risk patients might highlight the need for future quality improvement efforts.

Background Electronic medical records are increasingly used to identify disease cohorts; however, computable phenotypes using electronic medical record data are often unable to distinguish between prevalent and incident cases. Methods and Results We identified all Olmsted County, Minnesota residents aged ≥18 with a first‐ever International Classification of Diseases, Ninth Revision (ICD‐9) diagnostic code for atrial fibrillation or atrial flutter from 2000 to 2014 (N=6177), and a random sample with an International Classification of Diseases, Tenth Revision (ICD‐10) code from 2016 to 2018 (N=200). Trained nurse abstractors reviewed all medical records to validate the events and ascertain the date of onset (incidence date). Various algorithms based on number and types of codes (inpatient/outpatient), medications, and procedures were evaluated. Positive predictive value (PPV) and sensitivity of the algorithms were calculated. The lowest PPV was observed for 1 code (64.4%), and the highest PPV was observed for 2 codes (any type) >7 days apart but within 1 year (71.6%). Requiring either 1 inpatient or 2 outpatient codes separated by >7 days but within 1 year had the best balance between PPV (69.9%) and sensitivity (95.5%). PPVs were slightly higher using ICD‐10 codes. Requiring an anticoagulant or antiarrhythmic prescription or electrical cardioversion in addition to diagnostic code(s) modestly improved the PPVs at the expense of large reductions in sensitivity. Conclusions We developed simple, exportable, computable phenotypes for atrial fibrillation using structured electronic medical record data. However, use of diagnostic codes to identify incident atrial fibrillation is prone to some misclassification. Further study is warranted to determine whether more complex phenotypes, including unstructured data sources or using machine learning techniques, may improve the accuracy of identifying incident atrial fibrillation.

Background: Artificial intelligence (AI)-based analysis of the electrocardiogram (ECG) can estimate the probability of having atrial fibrillation (AF) while in sinus rhythm. We evaluated the association between the probability of AF using AI-ECG and incident ischemic stroke (I-Stroke) and cardiovascular (CV) mortality. Methods: We evaluated a community-based cohort of consecutive patients that sought primary care in Olmsted County, MN, from the years 1998 to 2000 followed-up through March 2016. Inclusion criteria were ages 40-79, an electrocardiogram within 2 years and complete CHA2DS2-VASc data. We excluded those with known atherosclerotic CV disease or AF. The AI network was developed in 180,000 patients (649,931 ECG). The primary endpoint was incident I-Stroke; secondary endpoint was I-Stroke or CV mortality; validated in duplicate. Multivariate cox models adjusted for age, sex, history of hypertension, diabetes, heart failure, and in CHA2DS2-VASc Score subgroups (categorized as low=0, moderate=1, and high risk ≥2). Results: We included 14,601 patients, mean age 53.2±13.08 (51% women, 95% whites), CHA2DS2-VASc score 1.3±1.2, median follow-up of 16.3 years, IQR 14.5-17.5, 727 (4.9%) had an I-Stroke and 1309 (8.9%) had I-Stroke or died from CV causes. When comparing the 5 th vs the 1 st quintile, those with high AI-ECG AF probability had increased risk for I-Stroke (HR 3.25, 95%CI 2.55-4.14, P-for trend <.0001 Figure-A ) and ischemic stroke or CV mortality (HR 5.73, 95%CI 4.69-6.99, P-for trend <.0001 Figure-B ). This remained significant after adjustments. Furthermore, AI-ECG AF probability was associated with I-Stroke across CHA2DS2-VASc risk levels (HR 2.43 for high, 2.33 for moderate, 1.98 for low CHA2DS2-VASc risk, all P<0.01). Conclusion: The AI-ECG AF algorithm predicts long-term I-Stroke and CV mortality and may refine CHA2DS2-VASc. These results highlight the utility of the AI enabled ECG when estimating I-Stroke or CV mortality risk.

Background: Transcatheter aortic valve replacement (TAVR) is an approved alternative to surgical aortic valve replacement. However, as the clinical indication and adoption of TAVR expands, it remains important to identify predictors of mortality after TAVR. We aimed to study the impact of aortic valve calcium score on long term mortality following TAVR in a large patient population. Methods: We included consecutive patients who successfully underwent TAVR from June 2010 – June 2019 across all Mayo Clinic sites. Patients were followed through February 2021. Aortic valve calcium scores were obtained from preoperative computed tomography angiography of the chest. Additional data was abstracted from medical records. Cox-proportional hazard regression models were employed to evaluate the independent effect of aortic valve calcium score on mortality. Results: A total of 1988 patients were evaluated in the final analysis. Forty-two percent were women, median age was 83 years (Q1: 77, Q3: 87), 16% received a permanent pacemaker following TAVR and 83% received an Edwards Sapien valve. We demonstrate an incremental increase in mortality risk as aortic valve calcium score values increased after adjusting for age, gender, pacemaker implantation, left bundle branch block, and Charlson Comorbidity Index. Selected calcium score cut-points with adjusted hazard ratios (aHR) are displayed: 1200 AU (aHR: 1.13, 95% CI 1.06, 1.20); 2000 AU (aHR: 1.22, 95% CI 1.09, 1.35); 3000 AU (aHR: 1.34, 95% CI: 1.14, 1.58). Conclusion: This study demonstrates an association between aortic valve calcium score and long-term mortality in a large patient population. This highlights the potential predictive value of the calcium score in preoperative planning and may provide clinicians with an additional measure for risk stratification prior to TAVR.