Citations

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.

Introduction: The treatment options for high and extreme-risk patients with severe symptomatic aortic stenosis have changed significantly over the past decade because of advances in transcatheter aortic valve replacement (TAVR). However new permanent pacemaker implant (PPI) remains an impending concern after TAVR. Hypothesis: We sought to determine the frequency, predictors and outcomes of patients requiring PPI after TAVR. Methods: We queried the National Inpatient Sample (NIS) database using the ICD-9-CM procedure code of 35.05 for transfemoral/transaortic and 35.06 for transapical replacement of aortic valve .We identified TAVR procedures associated with new permanent pacemaker implants using appropriate validated ICD-9-CM codes. Using multivariate analysis we further identified predictors of PPM implants Results: An estimated 8828 TAVR procedures were performed in United States between 2011-2012. 773 (8.7%) TAVR procedures were associated with PPIs. PPI were higher in older (age>80 years), White race, diabetics, left bundle branch block (LBBB), right bundle branch block (RBBB), transfemoral approach. The inhospital mortality ( 5.2% vs.4.6% ) , mean length of stay ( 9.8 vs. 8.3 days ) disposition to short term nursing facility was higher (38.2% vs. 32.1%) and mean total costs (82217$ vs. 52283$) was higher in TAVR + PPI group . On multivariate analysis, Age in 5 year increment [Odds ratio (OR) 1.11; Confidence interval (CI) 1.05-1.17, p<0.001], diabetes [OR 1.26; CI 1.07-1.49, p<0.001], LBBB [OR 1.99; CI 1.55-2.56, p<0.001], RBBB [OR 5.36; CI 3.80-7.55, p<0.001] were independent predictors of PPI. Transfemoral approach was associated with more PPI implants [OR 1.41; CI 1.12 -1.78, p<0.001] Conclusion: Age, baseline conduction system disturbances, diabetes, transfemoral approach emerged as independent predictors of PPI after TAVR. This study provides useful tools to identify high-risk patients for PPI after TAVR.

Introduction: Electro-anatomic mapping (EAM) has been studied as a modality to improve the sensitivity and reduce false negative rates with endomyocardial biopsy (EMB). We systematically reviewed the literature describing EAM guided EMB. Methods: An experienced librarian developed search strategies for Ovid MEDLINE, Ovid Embase, Ovid CDR, Cochrane Central, Scopus and Web of Science. There were no restrictions on language or publication date. Animal studies were excluded. Search strategy included MESH headings and keywords including endomyocardial biopsy and electroanatomic mapping. Results: Out of 139 studies initially identified, 16 (9 case series, 7 case reports) were included in this systematic review. EAM guided EMB was performed in 152 patients and results of 241 biopsies were available for analysis. The most common indications for EAM guided EMB were suspected arrhythmogenic right ventricular cardiomyopathy (ARVC), myocarditis and cardiac sarcoidosis (CS). While fluoroscopic visualization of the bioptome close to mapping catheter was the most commonly utilized biopsy technique (n=7 studies), direct visualization of bioptome on CARTO (n=4) and on NavX (n=2) were used as well. Low voltage areas had abnormal myocardial histology more commonly (range 60-100%) than normal voltage areas (range 11-52%). Sensitivity of low voltage areas for abnormal myocardium ranged from 60% to 82%. EAM guided EMB appears safe and adverse events were noted in only one study (remote hemopericardium). Conclusions: EAM guided EMB is a safe and efficacious method to improve the sensitivity and reduce the false negative rate of conventional fluoroscopy guided biopsy.

Abstract Aims Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Abstract Aims Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P &lt; 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Abstract Introduction Transsubclavian venous implantation of the Aveir leadless cardiac pacemaker (LCP) has not been previously reported. Methods and Results Three cases of transsubclavian implantation of the Aveir LCP are reported. Two cases were postbilateral orthotopic lung transplant, without appropriate femoral or jugular access due to recent ECMO cannulation and jugular central venous catheters. In one case, there was strong patient preference for same‐day discharge. Stability testing confirmed adequate fixation and electrical testing confirmed stable parameters in all cases. All patients tolerated the procedure well without significant immediate complications. Conclusions We demonstrate the feasibility of transsubclavian implantation of the Aveir LCP.