Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA.
Sci Rep. 2024 Nov 6;14(1):26898. doi: 10.1038/s41598-024-77269-x.
Studies have used extensive clinical information to predict time-to-heart failure (HF) in patients with and without diabetes mellitus (DM). We aimed to determine a screening method using only computed tomography calcium scoring (CTCS) to assess HF risk. We analyzed CTCS scans from 1,998 patients (336 with type 2 diabetes) from a no-charge coronary artery calcium score registry (CLARIFY Study, Clinicaltrials.gov NCT04075162). We used deep learning to segment epicardial adipose tissue (EAT) and engineered radiomic features of calcifications ("calcium-omics") and EAT ("fat-omics"). We developed models incorporating radiomics to predict risk of incident HF in patients with and without type 2 diabetes. At a median follow-up of 1.7 years, 5% had incident HF. In the overall cohort, fat-omics (C-index: 77.3) outperformed models using clinical factors, EAT volume, Agatston score, calcium-omics, and calcium-and-fat-omics to predict HF. For DM patients, the calcium-omics model (C-index: 81.8) outperformed other models. In conclusion, CTCS-based models combining calcium and fat-omics can predict incident HF, outperforming prediction scores based on clinical factors.Please check article title if captured correctly.YesPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.Yes.
研究已经利用广泛的临床信息来预测有或没有糖尿病的患者发生心力衰竭（HF）的时间。我们旨在确定一种仅使用计算机断层扫描钙评分（CTCS）评估 HF 风险的筛选方法。我们分析了来自一项免费冠状动脉钙评分登记研究（CLARIFY 研究，Clinicaltrials.gov NCT04075162）的 1998 名患者（336 名 2 型糖尿病患者）的 CTCS 扫描。我们使用深度学习技术对心外膜脂肪组织（EAT）进行分割，并对钙化的工程放射组学特征（“钙组学”）和 EAT（“脂肪组学”）进行了分析。我们开发了纳入放射组学的模型，以预测有或没有 2 型糖尿病的患者发生 HF 的风险。在中位随访 1.7 年期间，有 5%的患者发生了 HF。在整个队列中，脂肪组学（C 指数：77.3）优于使用临床因素、EAT 体积、Agatston 评分、钙组学以及钙和脂肪组学来预测 HF 的模型。对于 DM 患者，钙组学模型（C 指数：81.8）优于其他模型。总之，基于 CTCS 的模型结合钙和脂肪组学可以预测 HF 的发生，其预测性能优于基于临床因素的评分。
