Left ventricular area on non-contrast cardiac computed tomography as a predictor of incident heart failure - The Multi-Ethnic Study of Atherosclerosis

AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACTM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis

INTRODUCTION

Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF).

METHODS

We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.

RESULTS

Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​< ​0.0001), and comparable to clinical risk factors (0.85, p ​= ​0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p ​< ​0.0001).

CONCLUSION

AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis

Background

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths.

Methods

We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score.

Results

During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342).

Conclusion

In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.

Coronary artery calcium and sudden cardiac death: current evidence and future directions

PURPOSE OF REVIEW

To provide a summary of the current evidence and highlight future directions regarding coronary artery calcium (CAC) and risk of sudden cardiac death (SCD).

RECENT FINDINGS

Although up to 80% of all SCD is attributed to coronary heart disease (CHD), the subclinical atherosclerosis markers that help to improve SCD risk prediction are largely unknown. Recent observational data have demonstrated that, after adjustment for traditional risk factors, there is a stepwise higher risk for SCD across increasing CAC burden such that asymptomatic patients without overt atherosclerotic cardiovascular disease (ASCVD) experience a three-fold to five-fold higher SCD risk beginning at CAC at least 100 when compared with CAC = 0. Although the mechanisms underlying increasing CAC and SCD risk have yet to be fully elucidated, risk for myocardial infarction and scar, and/or exercise-induced ischemia may be potential mediators.

SUMMARY

High CAC burden is an important risk factor for SCD in asymptomatic middle-aged adults, suggesting that SCD risk stratification can begin in the early stages of CHD via measurement of calcific plaque on noncontrast computed tomography. Despite the clinical inertia for downstream functional cardiac testing after detecting high CAC, comprehensive ASCVD prevention strategies should be the primary focus for SCD risk reduction.

Small whole heart volume predicts cardiovascular events in patients with stable chest pain: insights from the PROMISE trial

OBJECTIVES

The size of the heart may predict major cardiovascular events (MACE) in patients with stable chest pain. We aimed to evaluate the prognostic value of 3D whole heart volume (WHV) derived from non-contrast cardiac computed tomography (CT).

METHODS

Among participants randomized to the CT arm of the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE), we used deep learning to extract WHV, defined as the volume of the pericardial sac. We compared the WHV across categories of cardiovascular risk factors and coronary artery disease (CAD) characteristics and determined the association of WHV with MACE (all-cause death, myocardial infarction, unstable angina; median follow-up: 26 months).

RESULTS

In the 3798 included patients (60.5 ± 8.2 years; 51.5% women), the WHV was 351.9 ± 57.6 cm3/m2. We found smaller WHV in no- or non-obstructive CAD, women, people with diabetes, sedentary lifestyle, and metabolic syndrome. Larger WHV was found in obstructive CAD, men, and increased atherosclerosis cardiovascular disease (ASCVD) risk score (p < 0.05). In a time-to-event analysis, small WHV was associated with over 4.4-fold risk of MACE (HR (per one standard deviation) = 0.221; 95% CI: 0.068-0.721; p = 0.012) independent of ASCVD risk score and CT-derived CAD characteristics. In patients with non-obstructive CAD, but not in those with no- or obstructive CAD, WHV increased the discriminatory capacity of ASCVD and CT-derived CAD characteristics significantly.

CONCLUSIONS

Small WHV may represent a novel imaging marker of MACE in stable chest pain. In particular, WHV may improve risk stratification in patients with non-obstructive CAD, a cohort with an unmet need for better risk stratification.

KEY POINTS

• Heart volume is easily assessable from non-contrast cardiac computed tomography. • Small heart volume may be an imaging marker of major adverse cardiac events independent and incremental to traditional cardiovascular risk factors and established CT measures of CAD. • Heart volume may improve cardiovascular risk stratification in patients with non-obstructive CAD.

Imaging predictors of incident heart failure: a systematic review and meta-analysis

BACKGROUND

Preventing the evolution of subclinical cardiac disease into overt heart failure is of paramount importance. Imaging techniques, particularly transthoracic echocardiography (TTE), are well suited to identify abnormalities in cardiac structure and function that precede the development of heart failure.

METHODS

This meta-analysis provides a comprehensive evaluation of 32 studies from 11 individual cohorts, which assessed cardiac indices from TTE (63%), cardiovascular magnetic resonance (CMR; 34%) or cardiac computed tomography (CCT; 16%). Eligible studies focused on measures of left ventricular geometry and function and were highly heterogeneous.

RESULTS

Among the variables that could be assessed through a meta-analytic approach, left ventricular systolic dysfunction, defined as left ventricular ejection fraction (LVEF) lower than 50%, and left ventricular dilation were associated with a five-fold [hazard ratio (HR) 4.76, 95% confidence interval (95% CI) 1.85-12.26] and three-fold (HR 3.14, 95% CI 1.37 -7.19) increased risk of heart failure development, respectively. Any degree of diastolic dysfunction conveyed an independent, albeit weaker, association with heart failure (HR 1.48, 95% CI 1.11-1.96), although there was only a trend for left ventricular hypertrophy in predicting incident heart failure (hazard ratio 2.85, 95% CI 0.82-9.85).

CONCLUSION

LVEF less than 50%, left ventricular dilation and diastolic dysfunction are independent predictors of incident heart failure among asymptomatic individuals, while left ventricular hypertrophy seems less predictive. These findings may serve as a framework for implementing imaging-based screening strategies in patients at risk of heart failure and inform future studies testing preventive or therapeutic approaches aiming at thwarting or halting the progression from asymptomatic (preclinical) to overt heart failure.

Identification of High-Risk Left Ventricular Hypertrophy on Calcium Scoring Cardiac Computed Tomography Scans: Validation in the DHS

BACKGROUND

Coronary artery calcium scoring only represents a small fraction of all information available in noncontrast cardiac computed tomography (CAC-CT). We hypothesized that an automated pipeline using radiomics and machine learning could identify phenotypic information about high-risk left ventricular hypertrophy (LVH) embedded in CAC-CT.

METHODS

This was a retrospective analysis of 1982 participants from the DHS (Dallas Heart Study) who underwent CAC-CT and cardiac magnetic resonance. Two hundred twenty-four participants with high-risk LVH were identified by cardiac magnetic resonance. We developed an automated adaptive atlas algorithm to segment the left ventricle on CAC-CT, extracting 107 radiomics features from the volume of interest. Four logistic regression models using different feature selection methods were built to predict high-risk LVH based on CAC-CT radiomics, sex, height, and body surface area in a random training subset of 1587 participants.

RESULTS

The respective areas under the receiver operating characteristics curves for the cluster-based model, the logistic regression model after exclusion of highly correlated features, and the penalized logistic regression models using least absolute shrinkage and selection operators with minimum or one SE λ values were 0.74 (95% CI, 0.67-0.82), 0.74 (95% CI, 0.67-0.81), 0.76 (95% CI, 0.69-0.83), and 0.73 (95% CI, 0.66-0.80) for detecting high-risk LVH in a distinct validation subset of 395 participants.

CONCLUSIONS

Ventricular segmentation, radiomics features extraction, and machine learning can be used in a pipeline to automatically detect high-risk phenotypes of LVH in participants undergoing CAC-CT, without the need for additional imaging or radiation exposure. Registration: URL http://www.clinicaltrials.gov. Unique identifier: NCT00344903.

The Implication of Coronary Artery Calcium Testing for Cardiovascular Disease Prevention and Diabetes

Over the last two decades coronary artery calcium (CAC) scanning has emerged as a quick, safe, and inexpensive method to detect the presence of coronary atherosclerosis. Data from multiple studies has shown that compared to individuals who do not have any coronary calcifications, those with severe calcifications (i.e., CAC score >300) have a 10-fold increase in their risk of coronary heart disease events and cardiovascular disease. Conversely, those that have a CAC of 0 have a very low event rate (~0.1%/year), with data that now extends to 15 years in some studies. Thus, the most notable implication of identifying CAC in individuals who do not have known cardiovascular disease is that it allows targeting of more aggressive therapies to those who have the highest risk of having future events. Such identification of risk is especially important for individuals who are not on any therapies for coronary heart disease, or when intensification of treatment is being considered but has an uncertain role. This review will highlight some of the recent data on CAC testing, while focusing on the implications of those findings on patient management. The evolving role of CAC in patients with diabetes will also be highlighted.