基于生物标志物估算肾小球滤过率的整合及其在基于影像的心血管疾病/中风风险计算器中的应用：一个南亚-印度的糖尿病队列研究，该队列研究人群患有中度慢性肾脏病。
Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease.
机构信息
MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India.
Department of Electronics and Communications, Visvesvaraya National Institute of Technology, Nagpur, India.
出版信息
Int Angiol. 2020 Aug;39(4):290-306. doi: 10.23736/S0392-9590.20.04338-2. Epub 2020 Mar 26.
BACKGROUND
Recently, a 10-year image-based integrated calculator (called AtheroEdge Composite Risk Score-AECRS1.0) was developed which combines conventional cardiovascular risk factors (CCVRF) with image phenotypes derived from carotid ultrasound (CUS). Such calculators did not include chronic kidney disease (CKD)-based biomarker called estimated glomerular filtration rate (eGFR). The novelty of this study is to design and develop an advanced integrated version called-AECRS2.0 that combines eGFR with image phenotypes to compute the composite risk score. Furthermore, AECRS2.0 was benchmarked against QRISK3 which considers eGFR for risk assessment.
METHODS
The method consists of three major steps: 1) five, current CUS image phenotypes (CUSIP) measurements using AtheroEdge system (AtheroPoint, CA, USA) consisting of: average carotid intima-media thickness (cIMTave), maximum cIMT (cIMTmax), minimum cIMT (cIMTmin), variability in cIMT (cIMTV), and total plaque area (TPA); 2) five, 10-year CUSIP measurements by combining these current five CUSIP with 11 CCVRF (age, ethnicity, gender, body mass index, systolic blood pressure, smoking, carotid artery type, hemoglobin, low-density lipoprotein cholesterol, total cholesterol, and eGFR); 3) AECRS2.0 risk score computation and its comparison to QRISK3 using area-under-the-curve (AUC).
RESULTS
South Asian-Indian 339 patients were retrospectively analyzed by acquiring their left/right common carotid arteries (678 CUS, mean age: 54.25±9.84 years; 75.22% males; 93.51% diabetic with HbA1c ≥6.5%; and mean eGFR 73.84±20.91 mL/min/1.73m). The proposed AECRS2.0 reported higher AUC (AUC=0.89, P<0.001) compared to QRISK3 (AUC=0.51, P<0.001) by ~74% in CKD patients.
CONCLUSIONS
An integrated calculator AECRS2.0 can be used to assess the 10-year CVD/stroke risk in patients suffering from CKD. AECRS2.0 was much superior to QRISK3.
背景
最近，开发了一种基于 10 年图像的综合计算器（称为 AtheroEdge 综合风险评分-AECRS1.0），该计算器将常规心血管风险因素（CCVRF）与颈动脉超声（CUS）衍生的图像表型相结合。这样的计算器没有包括基于慢性肾脏病（CKD）的生物标志物估算肾小球滤过率（eGFR）。本研究的新颖之处在于设计和开发一种称为-AECRS2.0 的高级综合版本，该版本将 eGFR 与图像表型相结合以计算复合风险评分。此外，AECRS2.0 与考虑 eGFR 进行风险评估的 QRISK3 进行了基准测试。
方法
该方法包括三个主要步骤：1）使用 AtheroEdge 系统（AtheroPoint，加利福尼亚州）进行五次当前 CUS 图像表型（CUSIP）测量，包括：平均颈动脉内膜-中层厚度（cIMTave）、最大 cIMT（cIMTmax）、最小 cIMT（cIMTmin）、cIMT 变异性（cIMTV）和总斑块面积（TPA）；2）通过将这五个当前 CUSIP 与 11 个 CCVRF（年龄、种族、性别、体重指数、收缩压、吸烟、颈动脉类型、血红蛋白、低密度脂蛋白胆固醇、总胆固醇和 eGFR）相结合，获得五次 10 年 CUSIP 测量值；3）计算 AECRS2.0 风险评分，并使用曲线下面积（AUC）将其与 QRISK3 进行比较。
结果
对 339 名南亚裔印度患者进行了回顾性分析，通过获取其左右颈总动脉（678 个 CUS，平均年龄：54.25±9.84 岁；75.22%为男性；93.51%患有 HbA1c≥6.5%的糖尿病；平均 eGFR 为 73.84±20.91mL/min/1.73m）。与 QRISK3（AUC=0.51，P<0.001）相比，所提出的 AECRS2.0 报告的 AUC（AUC=0.89，P<0.001）更高，在 CKD 患者中约高 74%。
结论
综合计算器 AECRS2.0 可用于评估患有 CKD 的患者的 10 年 CVD/中风风险。AECRS2.0 明显优于 QRISK3。
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