Sheffeh, Mohammad Ali

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.

Obesity affects approximately 72 million Americans and is a significant contributor to ischemic heart disease (IHD). Given the scarcity of data, this observational study examines trends and disparities in IHD-related mortality among obese individuals in the United States from 2003 to 2019 using Centers for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research data. Age-adjusted mortality rates (AAMRs) were calculated for IHD as the underlying cause of death and obesity as a contributing cause of death, revealing an increase in IHD-related mortality among obese adults. AAMR rose from 2.1 in 2003 to 3.9 in 2019, with higher rates in men, non-Hispanic Black individuals, the elderly, and those in nonmetropolitan and Midwest regions. These findings underscore significant sex, racial, and regional disparities in mortality, suggesting a need for targeted health policies and resource allocation, improving overall cardiovascular health outcomes.

Background: Non-ischemic cardiomyopathies can be primary (p-NICM) which involves mainly the heart muscle or caused by systemic disease with heart muscle involvement as it is seen in systemic infiltrative diseases (i-NICM). There is limited data on the safety and in-hospital outcomes of ventricular tachycardia (VT) ablation in i-NICM. Methods: We identified patients who underwent VT ablation between 2018-2021 using the National Inpatient Sample Database. i-NICM include amyloidosis, hemochromatosis, or sarcoidosis. In-hospital complications include mortality, acute kidney injury (AKI), acute heart failure (AHF), bleeding, vasopressor use, blood transfusion and cardiac tamponade. Multivariate logistic regression analyses were performed. Results : There were 7,420 VT ablations, 7,235 had P-NICM and 185 had i-NICM. A multivariable analysis did not reveal any difference in mortality (adjusted odds ratio [aOR]: 0.81; 95% confidence interval 0.32-1.98; p=0.62), AHF aOR: 0.88 (0.41-1.81; p=0.69, AKI aOR:1.17 (0.83-1.65); p=0.36, blood transfusion aOR: 0.63 (0.25-1.59); p=0.33, vasopressor aOR: 0.86( 0.51-1.49); p=0.61 or pericardial effusion/ tamponade aOR: 1.16 (0.67-2.1); p=0.58 Figure Conclusion: Patients with i-NICM had similar in-hospital complications compared to P-NICM further supporting the safety of VT ablation in managing recurrent VT in patients with i-NICM.

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.

Background: There is limited data on the safety and efficacy of ventricular tachycardia (VT) ablation in patients with chronic kidney disease (CKD). We examined the outcomes of patients with CKD undergoing VT ablation in a nationally representative cohort of patients. Methods: The National Inpatient Sample Database (NIS) was analyzed from 2018 to 2021 to identify patients ≥18 years old with VT undergoing ablation. Patients with atrial fibrillation, atrial flutter, supraventricular tachycardia, or pre-excitation syndrome were excluded. Patients were divided into those with CKD and without CKD. A multivariable logistic regression model was utilized to assess the association of CKD with in-hospital mortality and outcomes after adjusting for confounders. Results: Our cohort included 1608 VT ablation procedures, of which 428 (27%) were performed on CKD patients. Mean age was 63 (±13) years, 318 (19%) were female, and 1194 (74%) were White. 1475 (92%) of the procedures were done at an urban teaching hospital, and 1240 (77%) at a private non-profit hospital. On multivariable analysis, CKD was associated with significantly higher odds of death (adjusted odds ration [aOR]: 3.43; 95% confidence interval [CI]: 1.79-6.5; p=0.0002), acute decompensated heart failure (aOR: 3.1; 95% CI 2.24-4.56; p<.0001), bleeding (aOR: 1.53; 95% CI1.02-2.28; p<.03), and acute kidney injury (aOR: 4.49; 95% CI3.42-5.9; p<.0001). There was no difference in the odds of acute myocardial infarction, stroke, cardiogenic shock, vasopressor use, mechanical ventilation, and pericardial tamponade or pericardial procedures Figure . Conclusion: Patients with CKD undergoing VT ablation have an increased risk of mortality and adverse in-hospital outcomes when compared to patients without CKD. These hypothesis-generating findings need validation in future prospective studies for optimal pre-procedural risk stratification.

Abstract Background Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery. Objective We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries. Methods We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values. Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance. Conclusion AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.