Aviles, Laura Ortega

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: Many children born with single ventricle physiology are palliated with Fontan operation, which may include a fenestration creation. This has been demonstrated to improve short-term outcomes after surgery. Reduced exercise capacity has been shown to be a predictor of prognosis in Fontan patients. Aim: We sought to compare cardiopulmonary exercise test (CPET) data between fenestrated and non-fenestrated Fontan patients. Methods: We conducted a retrospective cohort study of 185 patients referred for CPET from 2011 to 2024. Patients with complete CPET data were included. Type of Fontan, comorbidities, and echocardiogram data were ascertained electronically. Patients were divided into 2 different groups according to the fenestration patency. Fisher’s exact test or chi-square was used to compare categorical variables. T-test and Turkey-Kramer were used to compare continuous variables. Adjustment using General Lineal Models procedure were used to identify any confounder that could affect the results. P value <0.05 was considered significant. Results: 142 patients were classified in the non-fenestrated (NF) group, and 43 in the fenestrated group (FF). Baseline characteristics of both groups are shown in Table 1. From the evaluated CPET variables, the minute ventilation-carbon dioxide production (VE/VCO2) was higher in the FF (p=<0.001), and oxygen saturation at peak exercise (SatO2) was lower in this group (p= <0.001) (Figure 1). There was no significant difference between peak oxygen consumption (VO2) between groups. When adjusted by comorbidities only VE/VCO2 nadir, slope and peak SatO2 were statistically significant different between groups (Table 2). Conclusion: Ventilatory variables showed significant differences when fenestration was presented, secondary to V-Q mismatch from increased right to left shunt. Despite the theoretical advantage of increased cardiac output from Fontan fenestration, exercise performance was similar between both groups.

Background: Cardiac amyloidosis is a rapidly progressing and severe condition leading to heart failure and reduced quality of life. Amyloid deposits impair both chronotropic and pumping functions of the heart. The Cardiopulmonary Exercise Test (CPET) is a vital tool in assessing the severity and progression of this disease. Aims: This study aims to investigate the differences in chronotropic function associated with the stages of heart failure in patients with cardiac amyloidosis and outcomes. Methods: A retrospective cohort study was conducted involving 194 patients diagnosed with cardiac amyloidosis who underwent at least one CPET from February 2010 to February 2024. Medical records were reviewed for data on the type of amyloidosis, comorbidities, and CPET results. The cohort was divided into two groups according to the Weber Classification: Weber Class A/B and Weber Class C/D. Patients who had CPET studies post-heart transplant were excluded. Statistical analyses, including Fisher’s exact test and chi-square test, were used. Cox regression model analysis, proportional hazards model for time to event. Adjustment for beta-blocker use was made to assess chronotropic incompetence. A p-value <0.05 was considered significant. Results: Among the 194 patients, 94 were classified as Weber A/B and 100 as Weber C/D. The average age at the time of the stress test was similar between the groups: 62.9 ± 11.7 years for Weber A/B and 62.3 ± 10.7 years for Weber C/D (p=0.087) Table 1 . Chronotropic function, evaluated through CPET, showed significant differences. Heart rate reserve was significantly lower in the Weber C-D group compared to the A-B group (p<0.001). The chronotropic index was also significantly lower in the Weber C-D group Figure 2. Chronotropic incompetence was observed in 43.9% of the Weber A and increased progressively, being significantly more prevalent in the Weber D group (100%) (p<0.001). Additionally, Chronotropic incompetence was strongly associated with the composite outcome of (hospitalization for heart failure and death) HR 1.932 (1.126 -3.313) p =0.0167 (Table 2). Conclusion: CPET assessment of chronotropic function correlates with VO2 parameter evaluated in Weber classification. Patients with higher Weber stages of heart failure in amyloidosis exhibited a higher prevalence of chronotropic incompetence, indicating it could serve as a marker of cardiac function.

Introduction: Sleep duration is now recognized as an important determinant of cardiovascular (CV) health. Recently, the CV implications of day-to-day variability in sleep duration have become apparent, with irregular sleep duration associated with increased risk of CV disease, including hypertension. However, the relationship between sleep duration variability and ambulatory blood pressure (BP) patterns in the general population or in patients with CV disease is unknown. Research Questions: We sought to investigate whether irregular sleep duration is associated with aberrant ambulatory BP or heart rate (HR) profiles in patients undergoing cardiac rehabilitation (CR). Methods: We recruited a sample of 42 patients with coronary artery disease enrolled in CR at Mayo Clinic Rochester. Participants underwent 24-hour ambulatory BP monitoring and sleep assessment including one week of wrist actigraphy and sleep diary prior to entering CR. Twenty-four hour, awake, and asleep averages were calculated for systolic BP, diastolic BP, and HR. Nocturnal dipping and morning surge were also computed for BP and HR. Sleep regularity was quantified by SD of actigraphy-derived sleep duration and irregular sleep duration was defined as SD >1 hour. Chi-square tests and Mann-Whitney U tests were used to compare groups. Results: Thirty-six participants (22% females, median age [25 th ,75 th IQR] 66.5 [61,75.8] years, body mass index 31.7 [26.9,35.1] kg/m 2 ) had valid actigraphy and ambulatory BP recordings. We found sleep duration SD >1 hour in 36% of them. Demographics and average sleep duration were similar between those with irregular and regular sleep duration (all Ps>0.11). Twenty-four-hour, awake and asleep HR values were comparable between groups (all Ps>0.26). Participants with irregular sleep duration exhibited lower nocturnal HR dipping (9.2 [3.1,11.6] % vs 12.9 [8.9,18.2] %, P=0.019) than their counterparts with regular sleep. Morning HR surge was also attenuated in patients with irregular sleep duration (3 [-1,9] bpm vs 10 [7,14] bpm, P=0.006). Ambulatory SBP and DBP measures did not differ significantly between groups (all Ps>0.17). Conclusions: High day-to-day variability in sleep duration is linked to blunted nocturnal dipping and morning surge in HR in patients with coronary artery disease enrolled in CR. Because altered ambulatory HR profile predicts unfavorable CV outcomes, evaluation of sleep patterns in the CR setting may yield important prognostic information.

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.

Abstract Background The concept of ideal cardiovascular health (CVH) was developed by the American Heart Association (AHA) as a composite of seven health factors and behaviors labeled as the Life’s Simple 7 (LS7). Ideal CVH has been associated with complex measures of cardiovascular aging. We hypothesized that physiologic aging, as determined by a simple, routinely used tool such as the electrocardiogram (ECG) enabled by an artificial intelligence algorithm to predict an individual’s physiologic age (ECG age), will be associated with poor CVH as defined by an adapted version of the AHA LS7. Methods We conducted an observational retrospective study of consecutive patients aged ≥18 years who sought care between June 2019 and March 2022, self-reported behavioral history through a validated intake questionnaire in clinical outpatient visits, had a clinically indicated ECG within one year, and provided research authorization. We applied a previously developed AI-ECG convolutional neural network to derive the ECG-Age. We excluded patients with missing data, those with body mass index (BMI) &lt;18.5 kg/m2, previous history of left-bundle branch block, atherosclerotic cardiovascular disease events, atrial fibrillation, heart failure, and non-skin cancer. An adapted version of the LS7 was used to define CVH, which includes anthropometrics (e.g., BMI), behavioral history (e.g., smoking, physical activity, and diet), vital signs (e.g., blood pressure), and laboratory values (e.g., total cholesterol, and fasting glucose). CVH was stratified as ideal, intermediate, and poor if the LS7 score had values of 9-14, 7-8, and 0-6, respectively, constructed by adding 2,1 or 0 points to each metric that met each category criteria, respectively. Accelerated aging was defined as having a Delta-Age ≥1SD above the mean, whereas Delta-age was defined as ECG minus chronological age. Logistic regression models tested the association between CVH categories and positive Delta-Age while adjusting for chronological age and sex. Results We included 114,214 individuals (53.3% females, 86.9% Caucasian). Mean chronological age was 57.7±14 years and mean AI-ECG- Age was 58 ± 12.44 years (R2 0.80, p-value &lt;0.001). Mean Delta-Age was 0.28 ±8.8 years. LS7 average score was 8.3 ± 2.23; 46.8% had ideal, 31.5% intermediate, and 21.7% had poor CVH (Figure A). Compared to those with ideal CVH, those with poor CVH were more than 50% more likely to have Delta-Age ≥1SD (OR 1.61, CI 95% 1.54-1.69, p-value: &lt;0.001), independent of chronological age and sex. (Figure B). Conclusion Poor CVH, defined by the AHA LS7, is linked with biological aging as determined by AI-enabled ECG, highlighting the importance of promoting CVH in adults as a strategy to decelerate biological aging and reduce its associated mortality.