Determination and distribution of left ventricular size as measured by noncontrast CT in 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.

Left atrial appendage size in patients with atrial fibrillation in Japan and the United States

Left atrial appendage (LAA) evaluation is important to select the optimal LAA closure device for patients with atrial fibrillation (AF). The LAA characteristics of Japanese patients remain uninvestigated. We compared the LAA size and morphology between 212 Japanese AF patients before catheter ablation and 119 AF patients undergoing LAA closure in the United States (US). We measured the LAA ostial dimension and depth by transesophageal echocardiography in all patients and determined the LAA morphology types of Japanese patients by multidetector cardiac computed tomography and those of US patients by LAA angiography. The maximum LAA ostial dimension was significantly larger in Japanese patients than in US patients (22.6 ± 4.1 mm vs. 21.5 ± 3.5 mm, P = 0.02). Also, Japanese patients had larger maximum dimension and depth corrected by body surface area than US patients in both paroxysmal and nonparoxysmal AF groups. The angle showing the maximum dimension was 0° or 135° in approximately 75% of patients in both groups. The common LAA morphology types were "cauliflower" and "chicken wing" in Japanese patients and "cactus" and "windsock" in US patients. In this study, Japanese patients had a larger LAA size than US patients. Because the maximum LAA dimension was obtained at the same angles, the LAA measurement method for US patients can be applicable to Japanese patients.

Going beyond Cardiomegaly: Evaluation of Cardiac Chamber Enlargement at Non-Electrocardiographically Gated Multidetector CT: Current Techniques, Limitations, and Clinical Implications

Cardiac chamber enlargement is important in the prediction of morbidity and mortality for a multitude of cardiovascular processes. Although non-electrocardiographically (ECG) gated multidetector CT is a commonly used cross-sectional imaging modality to evaluate a litany of cardiothoracic processes, a standardized method for evaluating and reporting cardiac chamber size does not exist. This has led to heterogeneity in the reporting of cardiac enlargement at routine multidetector CT with most readers often using gestalt assessment and the term cardiomegaly, which does not implicate the chamber or chambers that are enlarged. The purpose of this review article is to highlight advantages and limitations of several techniques used to assess cardiac chamber size at non-ECG-gated multidetector CT and to provide readers with reproducible and rapid measurements to determine if cardiac chamber size is present. The long-term aim would be to promote discussions between radiologists and institutions that would result in improved accuracy and decreased variability when commenting on cardiac chamber size. © RSNA, 2019.