Features of pericoronary adipose tissue (PCAT) assessed from coronary
computed tomography angiography (CCTA) are associated with inflammation and
cardiovascular risk. As PCAT is vascularly connected with coronary vasculature,
the presence of iodine is a potential confounding factor on PCAT HU and
textures that has not been adequately investigated. Use dynamic cardiac CT
perfusion (CCTP) to inform contrast determinants of PCAT assessment. From CCTP,
we analyzed HU dynamics of territory-specific PCAT, myocardium, and other
adipose depots in patients with coronary artery disease. HU, blood flow, and
radiomics were assessed over time. Changes from peak aorta time, Pa, chosen to
model the time of CCTA, were obtained. HU in PCAT increased more than in other
adipose depots. The estimated blood flow in PCAT was ~23% of that in the
contiguous myocardium. Comparing PCAT distal and proximal to a significant
stenosis, we found less enhancement and longer time-to-peak distally.
Two-second offsets [before, after] Pa resulted in [ 4-HU, 3-HU] differences in
PCAT. Due to changes in HU, the apparent PCAT volume reduced ~15% from the
first scan (P1) to Pa using a conventional fat window. Comparing radiomic
features over time, 78% of features changed >10% relative to P1. CCTP
elucidates blood flow in PCAT and enables analysis of PCAT features over time.
PCAT assessments (HU, apparent volume, and radiomics) are sensitive to
acquisition timing and the presence of obstructive stenosis, which may confound
the interpretation of PCAT in CCTA images. Data normalization may be in order.

使用心动脉 CT 肿瘤灌注（CCTP）技术研究了心脏病患者中冠心病相关脂肪组织 (PCAT) 的影像特征和血流动力学性质，发现 PCAT 的血流量约为相应心肌的 23％，PCAT 在影像特征、表观体积和放射图像学方面的评估受取像时间和梗阻性狭窄存在的干扰因素的影响敏感。

冠脂肪周围组织心脏 CT 灌注成像揭示冠脉 CTA 潜在干扰因素

Cardiac CT perfusion imaging of pericoronary adipose tissue (PCAT)  highlights potential confounds in coronary CTA

Purpose: To create and evaluate the accuracy of an artificial intelligence
Deep learning platform (ORAiCLE) capable of using only retinal fundus images to
predict both an individuals overall 5 year cardiovascular risk (CVD) and the
relative contribution of the component risk factors that comprise this risk.
Methods: We used 165,907 retinal images from a database of 47,236 patient
visits. Initially, each image was paired with biometric data age, ethnicity,
sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD
event wtihin 5 years of the retinal image acquisition. A risk score based on
Framingham equations was calculated. The real CVD event rate was also
determined for the individuals and overall population. Finally, ORAiCLE was
trained using only age, ethnicity, sex plus retinal images. Results: Compared
to Framingham-based score, ORAiCLE was up to 12% more accurate in prediciting
cardiovascular event in he next 5-years, especially for the highest risk group
of people. The reliability and accuracy of each of the restrictive models was
suboptimal to ORAiCLE performance ,indicating that it was using data from both
sets of data to derive its final results. Conclusion: Retinal photography is
inexpensive and only minimal training is required to acquire them as fully
automated, inexpensive camera systems are now widely available. As such,
AI-based CVD risk algorithms such as ORAiCLE promise to make CV health
screening more accurate, more afforadable and more accessible for all.
Furthermore, ORAiCLE unique ability to assess the relative contribution of the
components that comprise an individuals overall risk would inform treatment
decisions based on the specific needs of an individual, thereby increasing the
likelihood of positive health outcomes.

本研究旨在创建和评估一个人工智能深度学习平台 (ORAiCLE)，该平台仅使用视网膜底部图像即可预测个体在 5 年内的心血管疾病 (CVD) 风险以及构成该风险的组成部分的相关贡献。使用 Framingham 公式计算风险，与 ORAiCLE 相比，其在高风险人群中预测心血管事件的准确性最高可提高 12％。因此，学习者认为这种基于人工智能的 CVD 风险算法有望使患者更精确，更实惠，更易于得到诊断。

利用人工智能和视网膜成像替代基于 Framingham 的心血管疾病风险和不良结果预测方程

Replacing the Framingham-based equation for prediction of cardiovascular disease risk and adverse outcome by using artificial intelligence and retinal imaging

Estimation of treatment efficacy of real-world clinical interventions
involves working with continuous outcomes such as time-to-death,
re-hospitalization, or a composite event that may be subject to censoring.
Counterfactual reasoning in such scenarios requires decoupling the effects of
confounding physiological characteristics that affect baseline survival rates
from the effects of the interventions being assessed. In this paper, we present
a latent variable approach to model heterogeneous treatment effects by
proposing that an individual can belong to one of latent clusters with distinct
response characteristics. We show that this latent structure can mediate the
base survival rates and helps determine the effects of an intervention. We
demonstrate the ability of our approach to discover actionable phenotypes of
individuals based on their treatment response on multiple large randomized
clinical trials originally conducted to assess appropriate treatments to reduce
cardiovascular risk.

本文提出了一种潜变量方法，用于模拟异质性治疗效果，并演示其在多个大型随机对照试验中发现个体可操作表型的能力。