Bhyravajosyula, Sri Charan

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

Background: Our team recently developed an Artificial Intelligence model that enables the identification of increased left ventricular filling pressures using a single-lead electrocardiogram (AI-ECG) with excellent performance (AUC 0.91). However, its ability to predict incident heart failure (HF) in the community is yet to be evaluated. Hypothesis: We tested the hypothesis that the output from the AI-ECG will predict the long-term incidence of heart failure (HF) and HF with preserved ejection fraction (HFpEF), and would refine the HF predictive capabilities of the AHA Predicting Risk of Cardiovascular Disease EVENTs (PREVENT TM ) Equations. Methods: Leveraging the resources of the Rochester Epidemiology Project (REP) we analyzed data from patients who sought primary care in Olmsted County, MN, between 1997-2003 with passive follow-up until April 2024. We included those with a digital ECG at baseline and excluded those who were part of the original algorithm development or history of heart failure. The diagnosis of heart failure was ascertained by using ICD codes (ICD9-428,ICD10-I.50) and a word search for the terms related to heart failure. HFpEF was ascertained using the Ejection Fraction (EF) data closest to the heart failure diagnosis date, using EF more than 40% as the criterion. We tested the association between AI-ECG predicted probabilities and new-onset HF and HFpEF using Cox proportional hazard models. These models were also adjusted for cardiovascular risk factors and were stratified to evaluate the effect of the AI-ECG output on PREVENT TM -predicted HF at 10 years. Results: We included 20,276 subjects, mean age 55±14.03, 52% women, 92% non-Hispanic white. After 18.2±8.5 years of follow-up, 4,008 (19.76%) were diagnosed with HF and 3,812 (18.8%) HFpEF. Risks of HF and HFpEF significantly increased in patients with increased left ventricular filling pressures by AI-ECG, independent of risk factors, all p for trend <0.001, see Fig A-B , respectively. AI-ECG enhanced the prediction capabilities of the PREVENT TM score across all risk groups, see Fig-C. Conclusions: AI-ECG-Derived increased left ventricular filling pressure is associated with new-onset HF and HFpEF in the community and was associated with incidental heart failure at any PREVENT TM risk category. Those results suggest a potential role of AI ECG in predicting heart failure.