Artificial Intelligence Identification of Heart Failure With Preserved Ejection Fraction Substrate in Cardiac Surgery Patients

人工智能识别心脏手术患者射血分数保留型心力衰竭的基质

阅读:1

Abstract

BACKGROUND: Outcomes for patients with heart failure with preserved ejection fraction (HFpEF) who are undergoing cardiac surgery are not clear. We sought to examine the impact of artificial intelligence (AI) -diagnosed HFpEF on cardiac surgical outcomes in low- to moderate-surgical risk patients. METHODS: Adult patients undergoing isolated aortic valve replacement (AVR), mitral valve repair (MVr), or coronary artery bypass grafting (CABG) between 2004 and 2022 were retrospectively scored for the likelihood of HFpEF by using a commercially available echocardiogram-based AI algorithm. Patients with a left ventricular ejection fraction (LVEF)<50% were classified as having a reduced ejection fraction (HFrEF). On the basis of AI probabilities ranging from 0 to 1, patients with an LVEF ≥50% were stratified to normal function (0-0.49), moderate-probability HFpEF (0.5-0.74), and high-probability HFpEF (≥0.75). RESULTS: Among 1882 patients, 86.6% (n = 1629) had an LVEF ≥50%; of those patients, 36.8% (n = 599) were in the high-probability HFpEF group (median LVEF, 60% [interquartile range {IQR}, 56%-65%]), 6.7% (n = 109) were in the moderate-probability HFpEF group (LVEF, 61% [IQR, 59%-65%]), and 56.5% (n = 921) had normal function (LVEF, 63% [IQR, 59%-66%]). The remaining 13.4% (n = 253) were in the HFrEF group (LVEF, 40% [IQR, 31%-45%]). Compared with normal function, high-probability HFpEF was associated with increased operative mortality (1.3% vs 0.3%; P = .002) and 30-day readmission (12.8% vs 6.7%; P < .001). Over a median of 5.8 years (range, 0-20.0 years) of follow-up, high-probability HFpEF had increased mortality, HF admission, and atrial fibrillation (P < .01 for all) compared with normal function. Risk-adjusted all-cause mortality was greater in high-probability HFpEF (hazard ratio, 1.84; 95% CI: 1.35-2.52) vs normal function. CONCLUSIONS: Using AI, we can identify patients with high-probability HFpEF at increased risk of adverse events. Identifying this subgroup may enable surgical teams to improve short- and long-term outcomes through guideline-directed medical therapy and warrants further study.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。