Agreement of ejection fraction measured by coronary computed tomography (CT) and cardiac ultrasound in evaluating patients with chronic heart failure: an observational comparative study

Heyang Wang; Qijing Zhou; Wei Lu; Liang Dong; Yong Sun; Jun Jiang; Xiaochang Leng; Yabin Liu; Jianping Xiang; Changling Li

doi:10.21037/qims-23-1864

Agreement of ejection fraction measured by coronary computed tomography (CT) and cardiac ultrasound in evaluating patients with chronic heart failure: an observational comparative study

Quant Imaging Med Surg. 2024 May 1;14(5):3619-3627. doi: 10.21037/qims-23-1864. Epub 2024 Apr 26.

Authors

Heyang Wang^#¹, Qijing Zhou^#², Wei Lu³, Liang Dong¹, Yong Sun¹, Jun Jiang¹, Xiaochang Leng³, Yabin Liu¹, Jianping Xiang³, Changling Li¹

Affiliations

¹ Department of Cardiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
² Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
³ ArteryFlow Technology Co., Ltd., Hangzhou, China.

^# Contributed equally.

Abstract

Background: Cardiac ultrasound is one of the most important examinations in cardiovascular medicine, but the technical requirements for the operator are relatively high, which to some extent affects the scope of its use. This study was dedicated to investigating the agreement of ejection fraction between coronary computed tomography (CT) and cardiac ultrasound and diagnostic performance in evaluating the clinical diagnosis of patients with chronic heart failure.

Methods: We conducted a single-center-based retrospective study including 343 consecutive patients enrolled between January 2019 to April 2020, all of whom presented with suspected symptoms of heart failure within one month. All enrolled cases performed cardiac ultrasound and coronary CT scans. The CT images were analyzed using accurate left ventricle (AccuLV) artificial intelligence (AI) software to calculate the ejection fraction-computed tomography (EF-CT) and it was compared with the ejection fraction (EF) obtained based on ultrasound. Cardiac insufficiency was determined if the EF measured by ultrasound was below 50%. Diagnostic performance analysis, correlation analysis and Bland-Altman plot were used to compare agreement between EF-CT and CT.

Results: Of the 319 successfully performed patients, 220 (69%) were identified as cardiac insufficiency. Quantitative consistency analysis showed a good correlation between EF-CT and EF values in all cases (R square =0.704, r=0.837). Bland-Altman analysis showed mean bias of 6.6%, mean percentage error of 27.5% and 95% limit of agreement of -17% to 30% between EF and EF-CT. The results of the qualitative diagnostic study showed that the sensitivity and specificity of EF measured by coronary CT reached a high level of 91% [95% confidence interval (CI): 86-94%], and the positive diagnostic value was up to 96% (95% CI: 92-98%).

Conclusions: The EF-CT and EF have excellent agreement, and AccuLV-based AI left ventricular function analysis software perhaps can be used as a clinical diagnostic reference.

Keywords: Left ventricle (LV); deep learning; ejection fraction (EF); heart failure.