Citations

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 Cardiopulmonary exercise testing (CPET) has emerged as an important tool for risk stratification and prognosis assessment in patients with cardiac diseases. The Weber and Ventilatory Functional Classification have been correlated with outcomes in patients with Heart Failure (HF). Weber classification is based on VO2: Weber class A, peak VO2 >20 mL/kg per minute; class B, peak VO2 16–20 mL/kg per minute; class C, peak VO2 10–16 mL/kg per minute; and class D, peak VO2 ≤10 mL/kg per minute and Ventilatory classification on VE/VCO2 slope I-IV (Figure A). Although VO2 and VE/VCO2 variables have found altered in the amyloidosis population, the applicability of this classification in a cohort of amyloidosis patients and its correlation with all-cause mortality have not been explored. Purpose To evaluate if the Weber and Ventilatory Classification of HF predicts mortality amyloidosis patients. Methods 194 with diagnosis of amyloidosis who underwent CPET from February 2010 to February 2024 were included in this retrospective cohort study. Patients with at least one CPET complete data were included in the analysis. Data on the type of amyloidosis, comorbidities, and echocardiogram were retrieved from medical records. Cohort was divided into two groups according to the Weber Classification: Weber Class A/B and Weber Class C/D. Individuals with CPET studies after heart transplant were excluded from the analysis. Statistical analyses including Fisher’s exact test, chi-square, Tukey-Kramer (TK), Cox regression model analysis, proportional hazards model for time to event, and Kaplan–Meier curves were performed. A p-value <0.05 was considered significant. Results Out of the 194 patients, 94 were classified as Weber A-B and 100 as Weber B-C. The age at stress test was similar between the groups: Weber A/B 62.9 ± 11.7 years and Weber C/D 62.3 ± 10.7 years (p=0.087). Ejection Fraction was similar between groups, 32% and 28.9% in A-B and C-D respectively (p =0.207). Higher levels of NT-proBNP were found in the Weber C/D group. The prevalence of comorbidities was balanced among the groups except chronic kidney disease, which was higher in Weber Class C/D (40%) vs (20.2%) in Weber Class A/B (Table 1). Cox analysis revealed a strong association between higher Weber class and the outcome HR 2.36 (1.18 – 4.71) p=0.014. For Higher Ventilatory Class (Group III-IV) similar results were found HR 2.95 (1.60 – 5.45) p=0.005(Table 2). Mortality was significantly higher during the follow-up in the Weber C/D group (25%) vs (10%) in the Weber A-B group p=0.007(Figure D). Conclusion Weber and ventilatory Classification based on CPET, besides predicting outcomes in patients with cardiac disease, are strong predictors of mortality in patients with amyloidosis. These findings suggest that this patient group benefits from earlier stratification to assess prognosis and the potential requirement for transplant independently from other factors.

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 Cardiopulmonary exercise testing (CPET) is a widely used tool for risk stratification and prognosis in patients with chronic heart failure. In amyloid patients, cardiac amyloid deposits are associated with poor prognosis, increased risk for arrhythmias, sudden death, and subsequent exercise limitation due to restrictive cardiomyopathy. The type of amyloidosis and its affectation of the heart are different, and little is known about the CPET parameters according to the type of cardiac amyloidosis. Purpose In this study, we aim to compare CPET data in different types of amyloidosis to determine if CPET variables evaluated could have a significant difference that could impact the follow-up or prognosis of the patients based on the type of amyloidosis. Methods We conducted a retrospective cohort study of 194 patients with a diagnosis of cardiac amyloidosis referred for CPET from February 2010 to February 2024. Patients with at least one CPET data were included in our analysis. The type of amyloidosis, comorbidities, and echocardiogram data were retrieved from medical records. Patients were divided into 3 different groups according to the type of amyloidosis: AL Amyloidosis(AL), Variant TTR Amyloidosis (vTRR), and Wild Type TTR Amyloidosis (wtTTR). Individuals with CPET studies after heart transplant were excluded from the analysis. Fisher’s exact test or chi-square was used to compare categorical variables. ANOVA and Tukey-Kramer (TK) were used to compare continuous variables. Adjustment using General Linear Models (GLM) procedure was used to identify any confounder that could affect the results. A p-value <0.05 was considered significant. Results Out of the 194 patients, 66 had AL Amyloidosis, 40 had vTTR Amyloidosis, and 88 had wtTTR Amyloidosis. Most of the patients were male, and the male predominance was higher in the wtTRR group. There were 41 (62.1%) males in the AL group, 35 (87.5%) in the vTRR group, and 83 (94.3%) in the wtTTR group (p<0.0001). Comorbidities were balanced among the groups. Additional demographic and comorbidities data are shown in Table 1. From the evaluated CPET variables, VE/VCO2 nadir and slope were significantly higher in the wtTTR group when compared to the other groups, as well as Peak VE and Peak TV(Figure A). However, after adjusting for sex, BMI, and age, VE/VCO2 nadir was still significantly higher in the wtTRR group (p=0.003) compared to the other groups, as well as Peak VE, which was higher again in vTTR patients (p=0.001), and Peak TV (Table 2). Conclusion Ventilation variables evaluated in CPET showed alterations in the wtTTR group. These findings suggest that wtTRR amyloid patients could have a severe problem in ventilation that could be of significant relevance in future prognosis.

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) <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 <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: <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.