Citations

Abstract Background The determination of left ventricular diastolic dysfunction (LVDD) and filling pressure in patients with significant (>moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to detect LVDD and estimate LV filling pressures. Methods This retrospective study sought to examine the risk of all-cause mortality across AI-ECG LVDD grades. Patients from the AI-ECG LVDD study test cohort who underwent transthoracic echocardiography confirming significant MR and an ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022 were included. LVDD status was determined based on the index ECG, and patients were categorized into groups representing normal diastolic function (NDF) or LVDD grades 1 (G1), 2 (G2), or 3 (G3). Results Of 4,019 patients with significant MR (mean age 69.8, 49.0% women), 1,175 (29.2%), 1,881 (46.8%), and 963 (24.0%) were classified by AI-ECG as NDF/G1-LVDD, G2-LVDD, and G3-LVDD, respectively. The median mitral effective regurgitant orifice area was 26 mm² (interquartile range 20-36 mm²). Over a median follow-up of 3.5 years, 1,636 (40.7%) patients died. In multivariable survival analysis adjusted for multiple risk factors, a worse AI-ECG LVDD grade was independently associated with an increased death risk [G2DD: adjusted hazard ratio (aHR) 1.98 (95% confidence interval (95% CI) 1.63-2.40), G3DD: aHR 2.59 (95% CI 2.09-3.20)]. These findings were consistent when accounting for mitral valve intervention and among patients with >moderate MR or after mitral valve intervention. Conclusion In patients with significant MR, the grading of LVDD by AI-ECG is independently associated with all-cause mortality.

Abstract Aims Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown. The objective is to assess the effects of GAHT on ECG changes in TG. Methods and results Twelve-lead ECGs of TG on GAHT at the Mayo Clinic were inspected using a validated artificial intelligence (AI) algorithm. The algorithm assigns a patient’s ECG male pattern probability on a scale of 0 (female) to 1 (male). In the primary analysis, done separately for transgender women (TGW) and transgender men (TGM), 12-lead ECGs were used to estimate the male pattern probability before and after GAHT. In a subanalysis, only patients with both pre- and post-GAHT EGCs were included. Further, the autopopulated PR, QRS, and QTc intervals were compared before and after GAHT. Among TGW (n = 86), the probability (mean ± SD) of an ECG male pattern was 0.84 ± 0.25 in the pre-GAHT group, and it was lowered to 0.59 ± 0.36 in the post-GAHT group (n = 173, P < 7.8 × 10−10). Conversely, among TGM, male pattern probability was 0.16 ± 0.28 (n = 47) in the pre-GAHT group, and it was higher at 0.41 ± 0.38 in the post-GAHT group (n = 53, P < 2.4×10−4). The trend persisted in the subanalysis. Furthermore, both the PR (P = 5.68 × 10−4) and QTc intervals (P = 6.65×10−6) prolonged among TGW. Among TGM, the QTc interval shortened (P = 4.8 × 10−2). Conclusion Among TG, GAHT is associated with ECG changes trending towards gender congruence, as determined by the AI algorithm and ECG intervals. Prospective studies are warranted to understand GAHT effects on cardiac structure and function.

Abstract Artificial intelligence (AI)–enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients undergoing hematopoietic cell transplantation (HCT). We retrospectively subjected ECGs obtained before HCT to an externally trained, deep-learning model designed to predict the risk of atrial fibrillation (AF). Included were 1377 patients (849 autologous [auto] HCT and 528 allogeneic [allo] HCT recipients). The median follow-up was 2.9 years. The 3-year cumulative incidence of AF was 9% (95% confidence interval [CI], 7-12) in patients who underwent auto HCT and 13% (10%-16%) in patients who underwent allo HCT. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with the development of clinical AF (hazard ratio [HR], 7.37; 95% CI, 3.53-15.4; P < .001), inferior survival (HR, 2.4; 95% CI, 1.3-4.5; P = .004), and greater risk of nonrelapse mortality (NRM; HR, 95% CI, 3.36; 1.39-8.13; P = .007), without increased risk of relapse. Association with mortality was only noted in allo HCT recipients, where the risk of NRM was greater. The use of cyclophosphamide after transplantation resulted in greater 90-day incidence of AF (13% vs 5%; P = .01) compared to calcineurin inhibitor–based graft-versus-host disease prophylaxis, corresponding to temporal changes in AI-ECG AF prediction after HCT. In summary, AI-ECG can inform risk of posttransplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment.

BACKGROUND: Differentiating wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide tachycardia via 12-lead ECG interpretation is a crucial but difficult task. Automated algorithms show promise as alternatives to manual ECG interpretation, but direct comparison of their diagnostic performance has not been undertaken. METHODS: Two electrophysiologists applied 3 manual WCT differentiation approaches (ie, Brugada, Vereckei aVR, and VT score). Simultaneously, computerized data from paired WCT and baseline ECGs were processed by 5 automated WCT differentiation algorithms (WCT Formula, WCT Formula II, VT Prediction Model, Solo Model, and Paired Model). The diagnostic performance of automated algorithms was compared with manual ECG interpretation approaches. RESULTS: A total of 212 WCTs (111 VT and 101 supraventricular wide tachycardia) from 104 patients were analyzed. WCT Formula demonstrated superior accuracy (85.8%) and specificity (87.1%) compared with Brugada (75.2% and 57.4%, respectively) and Vereckei aVR (65.3% and 36.4%, respectively). WCT Formula II achieved higher accuracy (89.6%) and specificity (85.1%) against Brugada and Vereckei aVR. Performance metrics of the WCT Formula (accuracy 85.8%, sensitivity 84.7%, and specificity 87.1%) and WCT Formula II (accuracy 89.8%, sensitivity 89.6%, and specificity 85.1%) were similar to the VT score (accuracy 84.4%, sensitivity 93.8%, and specificity 74.2%). Paired Model was superior to Brugada in accuracy (89.6% versus 75.2%), specificity (97.0% versus 57.4%), and F1 score (0.89 versus 0.80). Paired Model surpassed Vereckei aVR in accuracy (89.6% versus 65.3%), specificity (97.0% versus 75.2%), and F1 score (0.89 versus 0.74). Paired Model demonstrated similar accuracy (89.6% versus 84.4%), inferior sensitivity (79.3% versus 93.8%), but superior specificity (97.0% versus 74.2%) to the VT score. Solo Model and VT Prediction Model accuracy (82.5% and 77.4%, respectively) was superior to the Vereckei aVR (65.3%) but similar to Brugada (75.2%) and the VT score (84.4%). CONCLUSIONS: Automated WCT differentiation algorithms demonstrated favorable diagnostic performance compared with traditional manual ECG interpretation approaches.

BACKGROUND: Whether vigorous exercise increases risk of ventricular arrhythmias for individuals diagnosed and treated for congenital long QT syndrome (LQTS) remains unknown. METHODS: The National Institutes of Health–funded LIVE-LQTS study (Lifestyle and Exercise in the Long QT Syndrome) prospectively enrolled individuals 8 to 60 years of age with phenotypic and/or genotypic LQTS from 37 sites in 5 countries from May 2015 to February 2019. Participants (or parents) answered physical activity and clinical events surveys every 6 months for 3 years with follow-up completed in February 2022. Vigorous exercise was defined as ≥6 metabolic equivalents for >60 hours per year. A blinded Clinical Events Committee adjudicated the composite end point of sudden death, sudden cardiac arrest, ventricular arrhythmia treated by an implantable cardioverter defibrillator, and likely arrhythmic syncope. A National Death Index search ascertained vital status for those with incomplete follow-up. A noninferiority hypothesis (boundary of 1.5) between vigorous exercisers and others was tested with multivariable Cox regression analysis. RESULTS: Among the 1413 participants (13% <18 years of age, 35% 18–25 years of age, 67% female, 25% with implantable cardioverter defibrillators, 90% genotype positive, 49% with LQT1, 91% were treated with beta-blockers, left cardiac sympathetic denervation, and/or implantable cardioverter defibrillator), 52% participated in vigorous exercise (55% of these competitively). Thirty-seven individuals experienced the composite end point (including one sudden cardiac arrest and one sudden death in the nonvigorous group, one sudden cardiac arrest in the vigorous group) with overall event rates at 3 years of 2.6% in the vigorous and 2.7% in the nonvigorous exercise groups. The unadjusted hazard ratio for experience of events for the vigorous group compared with the nonvigorous group was 0.97 (90% CI, 0.57–1.67), with an adjusted hazard ratio of 1.17 (90% CI, 0.67–2.04). The upper 95% one-sided confidence level extended beyond the 1.5 boundary. Neither vigorous or nonvigorous exercise was found to be superior in any group or subgroup. CONCLUSIONS: Among individuals diagnosed with phenotypic and/or genotypic LQTS who were risk assessed and treated in experienced centers, LQTS-associated cardiac event rates were low and similar between those exercising vigorously and those not exercising vigorously. Consistent with the low event rate, CIs are wide, and noninferiority was not demonstrated. These data further inform shared decision-making discussions between patient and physician about exercise and competitive sports participation. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02549664.

Background The study aimed to describe the patterns and trends of initiation, discontinuation, and adherence of oral anticoagulation (OAC) in patients with new‐onset postoperative atrial fibrillation (POAF), and compare with patients newly diagnosed with non‐POAF. Methods and Results This retrospective cohort study identified patients newly diagnosed with atrial fibrillation or flutter between 2012 and 2021 using administrative claims data from OptumLabs Data Warehouse. The POAF cohort included 118 366 patients newly diagnosed with atrial fibrillation or flutter within 30 days after surgery. The non‐POAF cohort included the remaining 315 832 patients who were newly diagnosed with atrial fibrillation or flutter but not within 30 days after a surgery. OAC initiation increased from 28.9% to 44.0% from 2012 to 2021 in POAF, and 37.8% to 59.9% in non‐POAF; 12‐month medication adherence increased from 47.0% to 61.8% in POAF, and 59.7% to 70.4% in non‐POAF. The median time to OAC discontinuation was 177 days for POAF, and 242 days for non‐POAF. Patients who saw a cardiologist within 90 days of the first atrial fibrillation or flutter diagnosis, regardless of POAF or non‐POAF, were more likely to initiate OAC (odds ratio, 2.92 [95% CI, 2.87–2.98]; P <0.0001), adhere to OAC (odds ratio, 1.08 [95% CI, 1.04–1.13]; P <0.0001), and less likely to discontinue (odds ratio, 0.83 [95% CI, 0.82–0.85]; P <0.0001) than patients who saw a surgeon or other specialties. Conclusions The use of and adherence to OAC were higher in non‐POAF patients than in POAF patients, but they increased over time in both groups. Patients managed by cardiologists were more likely to use and adhere to OAC, regardless of POAF or non‐POAF.

Abstract Introduction Postoperative atrial fibrillation (POAF) after noncardiac surgery constitutes 10-15% of all cases of incident atrial fibrillation (AF) in the community and has been associated with an increased risk of stroke. The risk of subsequent AF among patients with POAF is not well defined. Purpose To evaluate the role of systematic 30-day electrocardiographic (ECG) monitoring in the detection of AF recurrence after POAF. Methods In a prospective interventional study, patients who underwent major noncardiac surgeries and had new AF documented by ECG in the post-operative period between May 2022 and April 2023 were identified. For these patients, a 30-day ambulatory ECG monitor was recommended upon discharge, with final ordering deferred to the primary inpatient teams. Rates of recurrent AF of any duration detected by 30-day monitoring plus routine care versus routine care alone after the surgery were compared. Results Eighty-five patients with new-onset POAF were included (mean age 72 years, male 67%). Of these, 35 (41%) patients underwent up to 30 days [median 23 (IQR 6-27) days] of continuous ECG monitoring after hospital discharge. There were no significant differences in baseline characteristics between patients who did or did not undergo monitoring (Table 1). During median 281 (IQR 197-385) days of follow-up, recurrent AF was documented in 14/35 (40%) patients in the monitoring group as opposed to 7/50 (14%) patients in the usual care group (log-rank p=0.003, Figure 1). The median time from day of surgery until documented AF recurrence was 25 (IQR 6-53) days in the monitoring group and 66 (IQR 25-145) in the usual care group. Overall, 81% of recurrent AF events in both groups were detected in the first 3 months after surgery. The median burden of AF in the cardiac monitoring group was 7% (IQR 1-15), with the longest AF event lasting a median of 6 (IQR 2-18) hours. Anticoagulation for AF was prescribed in 24/64 (37%) patients with POAF without recurrence and in 16/21 (76%) patients with recurrent AF. Conclusion Recurrent AF following POAF is common and detected most frequently in the first 3 months after noncardiac surgery. Systematic ambulatory rhythm monitoring is associated with a higher rate of AF detection following hospital discharge and may identify a patient subgroup who might benefit from anticoagulation therapy.Table 1Figure 1

Introduction: Graves’ disease (GD) is known to cause menstrual changes and has been linked to infertility in women. This can be due to the increased production of androstenedione and testosterone induced by the excess thyroid hormone. However, screening for sex hormone levels in all women diagnosed with GD to identify patients more likely to have infertility is not cost-effective and not clinically indicated. AI-augmented ECG can identify the signature of female with an AUC of 0.97. In this model, AI- ECG gives a probability number between 0-100% of being female, with higher numbers indicating high probability of being female. Hypothesis: We hypothesized that this inexpensive AI ECG tool is reflective of hormonal levels and the resulting changes in menstrual cycle and fertility associated with GD in women. Methods: Patients diagnosed with GD (2009-2019) at our institution were included. Three intervals were identified: 1) Before GD: >2 months before GD diagnosis 2) During GD: within 2 months before GD and before attaining euthyroidism 3) After GD: after attaining euthyroidism. The matched-pairs t-test was used to compare probability of being a female; patients acted as their own controls. A subset analysis was done on females <45 years at GD diagnosis to study the association between female probability during GD and menstrual changes (defined as shorter, lighter, or newly irregular menstrual cycles). Results: 154 females with ECGs both before and during GD were identified. AI ECG female probability decreased with GD diagnosis (85% vs. 77%, P<.001). On the other hand, 137 females with GD had ECGs during and after GD. Female probability increased after attaining euthyroidism (76% vs 83%, P=.005). Moreover, 72 females <45 years were identified; 30 (42%) developed menstrual changes and had significantly lower AI-ECG female probability [77 (58-94)% vs 98 (92-100)%, P<.001] Conclusions: AI-enabled ECG signature of female gender in women was altered by GD and was associated with menstrual changes, likely reflecting changes in sex hormone levels. Future studies to confirm its role in screening for infertility in women with GD are needed.

Introduction: Asymptomatic LV systolic dysfunction is often underdiagnosed, leading to missed opportunities for treatment. In a recently completed pragmatic RCT, an AI algorithm performed on the ECGs ordered as part of routine practice increased early diagnosis of low EF. The current study aimed to understand whether the effectiveness of the AI algorithm integrated into routine care differed by the socioeconomic status of the patient. Methods: The main findings of the RCT were previously published. Clinicians in the intervention group had access to AI results (i.e., a positive or negative screening result, with positive results indicating a high likelihood of low EF). When a positive result was found, clinicians in the intervention group received an alert that encouraged them to order TTE if considered clinically appropriate. Clinicians in the control group did not have access or received alerts. Area deprivation index (ADI) was used to measure area-level social determinants of health. This study included 16,992 patients (control 8,225; intervention: 8,767) who had ECG performed for any indications between 8/5/2019 and 3/31/2020, no prior HF, and a valid address to derive ADI. The primary endpoint was a new diagnosis of EF≤50% within 3 months. Results: The mean age was 60.5±17.6 y. AI increased the diagnosis of low EF in the overall cohort (OR 1.42 [1.13, 1.78], p=0.01). The magnitude of benefit was greater in patients with higher socioeconomic status, e.g., living in an area that is less deprived (OR 1.88 [1.16, 3.05]), with higher median income, lower poverty rate, higher education level, better housing conditions, and higher proportion of white-collar workers, although the interaction was not statistically significant (Figure). Conclusions: AI enables early diagnosis of low EF and the magnitude of benefit might be numerically greater in patients with higher socioeconomic status, possibly due to variations in access to or referral for subsequent testing.