Arterial stiffness assessment in coronary microvascular dysfunction and heart failure with preserved ejection fraction: An initial report from the WISE-CVD continuation study

Background

Heart failure with preserved ejection fraction (HFpEF) is the most common cardiac complication in patients with coronary microvascular dysfunction (CMD), yet its underlying pathways remain unclear. Aortic pulse-wave velocity (aPWV) is an indicator of large artery stiffness and a predictor for cardiovascular disease. However, aPWV in CMD and HFpEF is not well characterized and may provide understanding of disease progression.

Methods

Among participants without obstructive coronary artery disease, we evaluated 51 women with suspected CMD and 20 women and men with evidence of HFpEF. All participants underwent aPWV measurement (SphygmoCor, Atcor Medical) with higher aPWV indicating greater vascular stiffness. Cardiac magnetic resonance imaging (CMRI) assessed left ventricular (LV) ejection fraction, CMD via myocardial perfusion reserve index (MPRI), and ventricular remodeling via LV mass-volume ratio. . Statistical analysis was performed using Wilcoxon rank sum tests, Pearson correlations and linear regression analysis.

Results

Compared to the suspected CMD group, the HFpEF participants were older (65 ± 12 vs 56 ± 11 yrs., p = 0.002) had higher BMI (31.0 ± 4.3 vs 27.8 ± 6.7 kg/m2, p = 0.013), higher aPWV (10.5 ± 2.0 vs 8.0 ± 1.6 m/s, p = 0.05) and lower MPRI (1.5 ± 0.3 vs1.8 ± 0.3, p = 0.02), but not remodeling. In a model adjusted for cardiovascular risk factors, the HFpEF group had a lower LVEF (estimate -4.78, p = 0.0437) than the suspected CMD group.

Conclusions

HFpEF participants exhibit greater arterial stiffness and lower myocardial perfusion reserve, with lower LVEF albeit not remodeling, compared to suspected CMD participants. These findings suggest arterial stiffness may contribute to progression from CMD to HFpEF. Prospective work is needed and ongoing.

Association of Left Anterior Descending Coronary Artery Calcium Progression and Radiation Dose with Major Adverse Cardiac Events in Breast Cancer

PURPOSE/OBJECTIVE(S)

Coronary artery calcium (CAC) is associated with increased risk of major adverse cardiac events (MACE). Accelerated CAC progression has been observed in patients with breast cancer after radiotherapy (RT) and there is a relationship between left anterior descending (LAD) coronary artery RT dose and the risk of coronary events. However, there is lack of consensus on LAD dose constraints for breast RT and limited data on the extent and impact of CAC progression. Our objective was to evaluate the association of LAD dose exposure and CAC progression with the risk of MACE in patients with breast cancer following RT.

MATERIALS/METHODS

Retrospective analysis of 181 patients with breast cancer treated with RT between 2008 and 2019. CAC was manually measured on RT planning and follow-up CTs (with at least one-year interval) using the Agatston method. Coronary arteries were segmented using a deep learning-based automated algorithm and dosimetric parameters collected. MACE cumulative incidence was estimated, and Fine and Gray regressions performed, accounting for non-cardiac death as a competing risk.

RESULTS

The median follow-up following RT was 70 months (interquartile range [IQR], 53-86). The median age was 63 years (IQR, 53-72), 43% had hypertension, 40% hyperlipidemia, 8% coronary heart disease (CHD). Most had pathologic stage I-II disease (76%). RT was targeted to breast/chest wall only in 60% and included regional nodes in 40% (internal mammary chain in 4%). The most common dose/fractionation was 48-50.4 Gy/25-28 fractions (67%) and 42.6-42.7 Gy/16 fractions (30%). At the time of RT, 68 (38%) had at least moderate CAC burden (CAC >100; statin-therapy indicated), but only 29 (43%) were on statin therapy. At a median interval of 44 months (IQR, 26-63), 55% (n = 84) had CAC progression, with a median increase of 52%/year (IQR, 18-193). The median time to MACE was 68 months (IQR, 53-85), with a 5-year cumulative incidence of 7.3% (15 MACE overall). Accounting for age and CHD, there was an increased risk of MACE with LAD CAC progression (subdistribution hazard ratio [SHR] 1.02/10 CAC points; 95% confidence interval [CI] 1.01 = 1.03; p = .007) and the volume of LAD receiving 15 Gy (LAD V15 Gy; SHR 1.03/%; 95% CI, 1.01-1.06; p = .004). There was no association between mean heart dose, chemotherapy, or Her2 therapy exposure and MACE (p>.05).

CONCLUSION

LAD CAC progression and LAD V15 Gy dose exposure were associated with an increased risk of MACE following RT. Accelerated CAC progression was commonly observed, however most patients were under-optimized for cardiovascular (CV) risk, with less than half of statin-eligible patients with at least moderate CAC burden on statin therapy. Together, these data support more aggressive cardiac risk mitigation approaches, including guidelines-based CV risk factor modification and optimized sparing of LAD radiation dose.

P2715Diastolic dysfunction in women with ischemia and no obstructive coronary artery disease: novel insight from left atrial feature tracking

Background

Women with signs and symptoms of ischemia but no obstructive coronary artery disease (INOCA) are at increased risk of developing heart failure with preserved ejection fraction (HFpEF); however, the exact mechanism for HFpEF progression remains to be elucidated. Prior studies have focused specifically on impaired left ventricular diastolic function in INOCA. We hypothesized that extending our evaluation to include the left atrium (LA)– a key constituent of the transmitral pressure gradient and left ventricular filling– would provide additional, novel, pathophysiological insight.

Purpose

To evaluate LA function in women with INOCA using cardiac MRI (CMR).

Methods

We performed retrospective feature tracking analysis of cine images from CMR (Figure 1A), to evaluate LA strain, in 58 INOCA women with normal sinus rhythm (three were excluded due to suboptimal image quality). All strain measurements were performed in duplicate by an experienced investigator blinded to clinical status. We subdivided the cohort by an established threshold of resting left ventricular end diastolic pressure (LVEDP) <12 mmHg vs >12 mmHg, performed invasively within a median of 27 days of the CMR. As illustrated in Figure 1B, LA function was divided into three established phases: (1) reservoir strain, passive expansion of the left atrium from the pulmonary circulation while the mitral valve is closed; (2) conduit strain, passive emptying of the atrium into the ventricle; and (3) booster strain, active emptying of the left atrium following atrial depolarization.

Results

Reservoir strain was higher in the elevated LVEDP group (n=20, 26.1 + 1.3%) vs. not elevated group (n=35, 22.8 + 0.9%, p=0.03; Figure 1C). In contrast, we observed no group difference in conduit strain (16.5 + 1.0 and 16.5 + 0.7, p=0.78, respectively; Figure 1D), resulting in significantly higher atrial booster strain in the elevated LVEDP group (10.0 + 1.1% and 7.0 + 0.6, p<0.01, respectively; Figure 1E).

Conclusions

To our knowledge, this is the first report of LA function in women with INOCA. That reservoir strain was higher in subjects with elevated LVEDP provides important pathophysiologic insight regarding diastolic hemodynamics of the LA. The similar conduit function between groups– despite different LVEDP's– strongly suggests a ventricular contribution to the impaired transmitral pressure gradient. Together, these initial proof-of-concept data support the evaluation of LA function in our quest to better understand heart failure progression in INOCA.

30Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium and epicardial adipose tissue: a prospective study

Background/Introduction

Machine learning (ML) allows objective integration of clinical and imaging data for the prediction of events. ML prediction of cardiovascular events in asymptomatic subjects over long-term follow-up, utilizing quantitative CT measures of coronary artery calcium (CAC) and epicardial adipose tissue (EAT) have not yet been evaluated.

Purpose

To analyze the ability of machine learning to integrate clinical parameters with coronary calcium and EAT quantification in order to improve prediction of myocardial infarction (MI) and cardiac death in asymptomatic subjects.

Methods

We assessed 2071 consecutive subjects [1230 (59%) male, age: 56.049.03] from the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) trial with long-term follow-up after non-enhanced cardiac CT. CAC (Agatston) score, age-and-gender-adjusted CAC percentile, and aortic calcium scores were obtained. EAT volume and density were quantified using a fully automated deep learning method. Extreme gradient boosting, a ML algorithm, was trained using demographic variables, plasma lipid panel measurements, risk factors as well as CAC, aortic calcium and EAT measures from CAC CT scans. ML was validated using 10-fold cross validation; event prediction was evaluated using area-under-receiver operating characteristic curve (AUC) analysis and Cox proportional hazards regression. Optimal ML cut-point for risk of MI and cardiac death was determined by highest Youden's index (sensitivity + specificity – 1).

Results

At 152 years' follow-up, 76 events of MI and/or cardiac death had occurred. ML obtained a significantly higher AUC than the ASCVD risk and CAC score in predicting events (ML: 0.81; ASCVD: 0.76, p<0.05; CAC: 0.75, p<0.01, Figure A). ML performance was mostly driven by age, ASCVD risk and calcium as shown by the variable importance (Figure B); however, all variables with non-zero gain contributed to the ML performance. ML achieved a sensitivity and specificity of 77.6% and 73.5%, respectively. For an equal specificity, ASCVD and CAC scores obtained a sensitivity of 61.8% and 67.1%, respectively. High ML risk was associated with a high risk of suffering an event by Cox regression (HR: 9.25 [95% CI: 5.39–15.87], p<0.001; survival curves in Figure C). The relationships persisted when adjusted for age, gender, CAC, CAC percentile, aortic calcium score, and ASCVD risk score; with a hazard ratio of 3.42 for high ML risk (HR: 3.42 [95% CI: 1.54–7.57], p=0.002).

Conclusion(s)

Machine learning used to integrate clinical and quantitative imaging-based variables significantly improves prediction of MI and cardiac death in asymptomatic subjects undergoing CAC assessment, compared to standard risk assessment methods.

Acknowledgement/Funding

NHLBI 1R01HL13361, Bundesministerium für Bildung und Forschung (01EX1012B), Dr. Miriam and Sheldon G. Adelson Medical Research Foundation