Estrada-Magana, Andres

Abstract Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent and paroxysmal nature. To address this, multiple risk scores have been developed to predict incident AF. This study aimed to evaluate the predictive accuracy of five widely used clinical AF prediction tools (i.e., CHARGE-AF, CHA2DS2-VASc, mC2HEST, HATCH, and HAVOC) among patients undergoing long-term continuous monitoring with implantable loop recorders (ILRs). Methods We conducted a retrospective cohort study across three major US academic medical centers, including patients with ILRs and no prior diagnosis or ECG evidence of AF. Clinical variables used to calculate each risk score were extracted from electronic health records. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate the association between each score’s risk categories and incident AF (defined as any episode lasting ≥6 minutes). Calibration was assessed using calibration plots and Brier scores. Results A total of 3,895 patients were included (mean age 61 ± 17 yrs.; 51% male). Incident AF was detected in 879 patients (22%) over a median follow up of 17 months (IQR 8-35). Table 1 presents baseline characteristics and device detected AF event data during follow up. CHARGE-AF had the highest AUC (0.68) but also the highest Brier score (Fig. 1). Failure curves showed clear risk stratification across all tools (Fig. 2). Calibration plots (Fig. 3) demonstrated modest agreement between predicted and observed risk across all models. Using CHARGE-AF as the reference, pairwise comparisons of Brier scores via the Wilcoxon signed-rank test showed lower calibration accuracy than CHA2DS2-VASc (p = 0.03), mC2HEST (p = 0.001), and HAVOC (p = 0.001), with no significant difference compared to HATCH (p = 0.15). Conclusions In patients undergoing continuous rhythm monitoring with ILRs, commonly used AF prediction scores demonstrated modest discriminatory ability and similar calibration performance. These findings suggest that existing risk models, which were largely developed in cohorts with clinically diagnosed AF, may not optimally translate to continuously monitored populations. Future work is needed to develop and validate AF prediction models specifically in cohorts undergoing long-term continuous cardiac monitoring.Figure 1Figure 2-3

Abstract Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent nature. Deep learning-based electrocardiogram (DL-ECG) analysis can streamline AF risk stratification, providing a more reliable and accessible method to stratify risk using only a standard resting ECG. However, this tool has been validated only on AF detected clinically or with short ECG event monitors. Purpose This study aimed to validate the predictive accuracy of a DL-ECG model for identifying AF in normal sinus rhythm (NSR) ECGs among patients undergoing continuous cardiac monitoring with implantable loop recorders (ILRs). Methods We conducted a historical cohort study across three major U.S. academic medical centers, enrolling patients with an ILR and no prior diagnosis or documented ECG evidence of AF. Pre-implantation 10-second, 12-lead ECGs in NSR were analyzed using the DL-ECG model. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with the CHARGE-AF score. Kaplan-Meier survival analysis and Cox proportional hazards models assessed the association between DL-ECG risk categories and incident AF, defined as any episode lasting ≥6 minutes. Results We included 2,523 patients (mean age 66 ± 17 years, 52% male). AF was detected in 658 (26%) over a median follow-up of 19 months (IQR 8-37), Table 1. The DL-ECG model classified 1,634 patients (65%) as at low-risk and 889 (35%) as at high-risk. AF developed in 242 (14.8%) of the low-risk group and 416 (46.8%) of the high-risk group, yielding an AUC of 0.73 (95% CI 0.70-0.75), surpassing the CHARGE-AF AUC of 0.69 (95% CI 0.66-0.71). A combined DL-ECG and CHARGE-AF model yielded an AUC of 0.74 (95% CI 0.72-0.76), Figure 1 presents the AUC, Net Reclassification Index, and DeLong test comparisons among the three models. High-risk patients had a significantly higher incidence of AF, which remained significant after adjusting for CHARGE-AF variables, Figure 2. AF incidence increased as DL-ECG quartiles increased, with 61% of patients at the top quartile developing AF during follow-up, Figure 3. False negatives (patients initially classified as low-risk but in whom later AF was detected) had a longer median time to AF detection (127 days [IQR 28-425] vs. 101 days [IQR 20-318] for true positives, p = 0.01), and where found to have at least one positive ECG during follow-up in 36%, compared to 32% of true negatives, indicating eventual risk reclassification. Conclusions Among patients undergoing ILR monitoring, the DL-ECG model demonstrated superior predictive performance compared to CHARGE-AF, offering a promising and accessible alternative for early detection of silent AF. This approach could aid early AF detection, particularly in resource-limited settings where access to specialized rhythm monitoring services is limited.Table 1 and Figure 1 Figure 2 and 3

ABSTRACT Background Recent data show increased ventricular tachycardia (VT) related mortality. We aimed to investigate the trends and disparities of VT‐related mortality according to cardiomyopathy subtypes. Methods Mortality and demographic data were obtained from the CDC Wide‐ranging Online Data for Epidemiologic Research database between 1999 and 2020. VT‐related mortality was defined as the underlying cause of death and ischemic cardiomyopathy (ICM) or nonischemic cardiomyopathy (NICM) as the contributing cause of death. The direct method of standardization was utilized to estimate age‐adjusted mortality rates (AAMRs). Temporal trends were evaluated using log‐linear regression models. Results A total of 15 888 deaths were related to both VT and ICM, and 16 777 were due to both VT and NICM. There was a significant increase in VT and ICM‐related mortality between 2006 and 2020 with an APC of +1.38% ( p < 0.05). Similarly, VT and NICM‐related mortality increased between 2008 and 2020 with an APC of +0.60% ( p < 0.05). ICM had a higher AAMR in males [6.23 (6.12–6.34)], Whites [3.49 (3.43–3.54)], Hispanics [2.11 (1.95–2.26)], and the Midwest region [3.73 (3.61–3.85)] compared to NICM. In contrast, NICM had a higher AAMR in females [1.57 (1.52–1.61)], Black or African Americans [5.02 (4.84–5.20)], and the South region [3.10 (3.03–3.18)]. p for all trend < 0.05. Conclusions Real‐world data show significant differences in VT‐related mortality according to cardiomyopathy subtypes with prominent sex, race, and regional disparities. Clinical and public health strategies are needed to address inequities and improve outcomes.

Background: Sodium-glucose cotransporter-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA) have known cardiovascular benefits. We aimed to assess the efficacy of GLP1-RA or SGLT2i initiation in reducing mortality or cardiovascular events in a community-based cohort of adults with ischemic stroke. Methods: Patients ≥18 years old admitted for acute ischemic stroke between Jan 2000 and June 2022 were identified using the Rochester Epidemiology Project. Subjects were considered exposed to SGLT-2i or GLP1-RA if they were treated with either medication after the index stroke and before the primary (all-cause mortality) or secondary outcomes (MI, recurrent ischemic stroke or composite). Kaplan-Meier curves and Cox models are shown in the Figures . Results: We included 7044 subjects, mean age 72±14 yr, 52% male, 94% white, over a median follow-up of 3 [1-7] years, 416 subjects (6%) had a second stroke, 426 (6%) had an incident MI and 3738 (53%) died. Those on SGLT2i/GLP1-RA had a decreased risk for mortality, incident MI, and composite in the univariate analysis. These associations remained significant after multivariable adjustment and were not affected by minimum time of exposure Figure 1&2 . SGLT2i/GLP1-RA use was associated with recurrent ischemic stroke in the multivariate analysis Figure 2C . All associations remained significant in most sub-analyses of individual medications except for the association between GLP1-RA and recurrent stroke Figure 3 Conclusions: SGLT2i or GLP-1 RA use was associated with decreased mortality and risk of myocardial infarction and recurrent stroke in patients with ischemic stroke.

Introduction: Artificial Intelligence-enabled electrocardiogram (AI-ECG) can estimate the probability of having atrial fibrillation (AFib) while in sinus rhythm. Recurrent stroke in subjects without known AFib may be due to undiagnosed AFib. We evaluated the association between AI-ECG AFib and recurrent ischemic stroke and with incident AFib Methods: Consecutive adults admitted to a Mayo Clinic or Mayo Clinic Health System hospital or to Olmsted Community Hospital for ischemic stroke between January,1,2000 and June,30,2022 were identified using the Rochester Epidemiology Project. Recurrent ischemic strokes were identified after reviewing medical records and imaging studies. We excluded those with known AFib or without ECGs within 3 years prior to the first ischemic stroke. The AI-ECG network was previously developed using 649,931 ECGs from > 180,000 subjects. The primary endpoint was recurrent ischemic stroke; secondary endpoint was incident AFib. Cox model adjusted for age, sex, and CHA2DS2-VASc2 score variables. Results: We included 4,816 subjects, mean age 70±15 yr, 54% male, 92% white, median CHA2DS2-VASc2 score 4 [3-5], median follow-up 3 [1-7] yr. 280 subjects (6%) had a second stroke and 366 (8%) were diagnosed with incident AFib over follow-up. Those with a positive AI-ECG AFib had an increased risk for recurrent ischemic stroke(HR 1.54, 95% CI 1.21-1.96, P<0.001 Figure A) This association remained significant after adjustment for CHA2DS2-VASc2 score(HR 1.37 95% CI 1.07-1.76, P=0.01). These associations were not affected by anticoagulation use over follow-up. AI-ECG AFib was also associated with incident AFib(HR 2.42 95% CI 1.96-2.98, P<0.001 Figure B) Conclusions: AI-ECG AFib predicts recurrent ischemic stroke in the community, possibly reflecting undiagnosed AFib that can be detected by AI-ECG, as it also predicts incident AFib. These results underscore the potential utility of AI-ECG for risk stratification of patients with ischemic stroke.