使用基于深度学习的视网膜生物标志物进行心血管疾病风险评估：与现有风险评分的比较
Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.
作者信息
Yi Joseph Keunhong, Rim Tyler Hyungtaek, Park Sungha, Kim Sung Soo, Kim Hyeon Chang, Lee Chan Joo, Kim Hyeonmin, Lee Geunyoung, Lim James Soo Ghim, Tan Yong Yu, Yu Marco, Tham Yih-Chung, Bakhai Ameet, Shantsila Eduard, Leeson Paul, Lip Gregory Y H, Chin Calvin W L, Cheng Ching-Yu
机构信息
Albert Einstein College of Medicine, 1300 Morris Park Ave, Bronx, NY 10461, USA.
Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore 169856, Singapore.
出版信息
Eur Heart J Digit Health. 2023 Mar 28;4(3):236-244. doi: 10.1093/ehjdh/ztad023. eCollection 2023 May.
AIMS
This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD.
METHODS AND RESULTS
We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively.
CONCLUSION
The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.
目的
本研究旨在评估基于深度学习的心血管疾病（CVD）视网膜生物标志物Reti-CVD识别CVD中高危个体的能力。
方法与结果
我们根据合并队列方程（PCE）、QRISK3和改良的弗雷明汉风险评分（FRS）定义中高危组。将Reti-CVD的预测结果与根据标准CVD风险评估工具识别出的中高危个体数量进行比较，并计算敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）以评估结果。在英国生物银行中，48260名参与者中，分别有20643名（42.8%）和7192名（14.9%）根据PCE和QRISK3被分类为中高危组。在新加坡眼病流行病学研究中，6810名参与者中，3799名（55.8%）根据改良FRS被分类为中高危组。Reti-CVD识别基于PCE的中高危组的敏感性、特异性、PPV和NPV分别为82.7%、87.6%、86.5%和84.0%。Reti-CVD识别基于QRISK3的中高危组的敏感性、特异性、PPV和NPV分别为82.6%、85.5%、49.9%和96.6%。Reti-CVD根据改良FRS识别中高危组的敏感性、特异性、PPV和NPV分别为82.1%、80.6%、76.4%和85.5%。
结论
视网膜照片生物标志物（Reti-CVD）能够根据现有风险评估工具识别CVD中高危个体。
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