Left ventricular myocardial tissue characteristics and function among healthy subjects with varying atherosclerotic cardiovascular disease risk

 

The Atherosclerotic Cardiovascular Disease Risk (ASCVD) score by pooled cohort equation is a reliable predictor for future ASCVD events and is used to guide primary prevention in asymptomatic aging subjects. ASCVD risk is associated with burden of coronary artery disease as measured from computed tomography angiography.

Purpose

We aim to investigate the association between ASCVD risk and cardiac magnetic resonance (CMR) derived left ventricular (LV) myocardial tissue characteristics (T1 values) and LV systolic and diastolic ventricular function in a large cohort of healthy subjects.

Methods

We selected all healthy subjects who underwent CMR from the UK-Biobank cohort study. We collected patient characteristics, cardiovascular risk factors, blood pressure at CMR, medication use and cholesterol levels. We used AI-CMRQC, our quality-controlled tool for analysis of cardiovascular function metrics from CMR using artificial intelligence [1,2], to automatically extract septal T1 values from native T1 maps and LV ejection fraction (EF), peak ejection and early filling rates (PER, PEFR), peak systolic longitudinal strain and diastolic strain-rate, mitral valve annular plane systolic excursion and diastolic peak velocity from cine long and short axis acquisitions. Subjects were stratified for low (<7.5%), intermediate (7.5–21%) and high (>21%) ASCVD risk. One-way repeated measures ANOVA was used to examine the association between cardiovascular metrics and ASCVD risk groups.

Results

12,493 healthy subjects were included (females n=6,000). Mean age was 62.7±7.5 years, 3.2% had diabetes, 12.5% received treatment for hypertension and 23% smoked. ASCVD risk score could be calculated in 9,487 subjects. Mean ASCVD risk was 12.7±9%. 38% of subjects had low, 43% intermediate and 19% high ASCVD risk.

T1 values fell across the incremental ASCVD groups (low: 943±51 ms, intermediate: 921±47 ms, high: 918±50 ms, P<0.001). Indexed LV PEFR (low: 188±45 ml/ms·m2, intermediate: 170±48 ml/ms·m2, high: 147±45 ml/ms·m2, P<0.001), diastolic longitudinal strain rate (low: 1.25±0.36, intermediate: 1.20±0.37, high: 1.17±0.36, P<0.001) also fell consistently with incremental risk. A statistically significant, but clinically less relevant decrease was seen for LVEF (low: 59±6%, intermediate 58±6%, high: 57±6% p<0.001) and longitudinal systolic strain (low: 21±3.5%, intermediate: 21±3.4%, high: 21±3.5%).

Conclusion

Increasing ASCVD risk was associated with lower native T1 values and decreasing metrics of diastolic and systolic LV function. The fall in T1 might suggest fibrofatty replacement in the LV myocardium in patients with incremental ASCVD risk that could contribute to the observed deterioration of LV function.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIHR MedTech cooperation awarded to Guy's and ST Thomas NHS Foundation Trust

Automated non-invasive pressure-volume loop analysis of cardiac aging in a large cohort of healthy community dwellers

Background/Introduction

Pressure-volume loops (PVloops) provide a wealth of information on cardiac function that is not readily available from cardiac imaging alone.

Methods

To estimate left ventricular (LV) PVloops non-invasively have been available, but have so far not been used to interrogate ventricular function in large patient cohorts, due to the complexity of estimating PVloops. A new method was recently validated that construct PVloops non-invasively from cine cardiac magnetic resonance (CMR), based on the time-varying elastance model [1]. At the same time, we have validated a framework for automated, quality controlled analysis of cine CMR in large cohorts of patients/subjects [2]. Combining these two methods could automated PVloop estimation, enabling analysis of ventricular pressure-volume relationships in large study populations.

Purpose

Evaluate if CMR-based non-invasive PVloops can be used to interrogate the impact of cardiac ageing on LV function occurring in a large population of healthy community dwellers.

Methods

Non-invasive PVloops were calculated from a full cardiac cycle LV volume curve and brachial blood pressure data using a recently validated method based on the time-varying elastance model [1], in 7,650 healthy community dwellers from the UKBiobank population study. The LV volume curve was automatically obtained using our state-of-the-art, quality controlled deep learning (DL) based cine CMR analysis framework [2]. External Work, pressure-volume-area (PVA), end-systolic pressure (Pes), ventricular elastance (Ees, an estimate of contractility) and arterial elastance (Ea) and energy per ejected volume (EEV: PVA/ stroke volume) were calculated from the PVloops. We performed univariate regression between PVloop parameters and age. We also calculated the additional impact of cardiovascular risk-factors in a multivariate analysis.

Results

See results in table 1. With age, LV volumes fall (p<0.001) in healthy subjects, while systolic blood pressure and Pes increases (both p<0.001). As a result of the higher afterload, PVA (p=0.894) and EW (p=0.499) do not significantly change with age despite a lower SV. Arterial elastance (Ea) increased, and so did contractility, as measured by Ees (p<0.001). Due to all these changes, EEV increased with age (p<0.001). In multivariate analysis, cardiovascular risk factors hypercholesterolemia and hypertension negatively impacted Pes, PVA, Ees and EEV. Diabetes and smoking habits did not.

Conclusion

Non-invasive CMR-based PVloop analyses capture the impact of known changes occurring during cardiac ageing on cardiac work, contractility and energetic expenditure. Obtaining PVloops automatically using our AI analysis system in this large cohort of healthy subjects allows to formulate reference for assessment of cardiac disease.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): The authors acknowledge financial support (support) the National Institute for Health Research (NIHR) Cardiovascular MedTech Co-operative (previously existing as the Cardiovascular Healthcare Technology Co-operative 2012 - 2017) award to the Guy's and St Thomas' NHS Foundation Trust, in partnership with King's College London and the NIHR comprehensive Biomedical Research Centre of the Guy's & St Thomas' NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health Univariate regression analysisExample of estimated PV loop