Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS European heart journal. Digital health Pub Date : 2023-03-28 eCollection Date: 2023-05-01 DOI:10.1093/ehjdh/ztad023
Joseph Keunhong Yi, Tyler Hyungtaek Rim, Sungha Park, Sung Soo Kim, Hyeon Chang Kim, Chan Joo Lee, Hyeonmin Kim, Geunyoung Lee, James Soo Ghim Lim, Yong Yu Tan, Marco Yu, Yih-Chung Tham, Ameet Bakhai, Eduard Shantsila, Paul Leeson, Gregory Y H Lip, Calvin W L Chin, Ching-Yu Cheng
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Abstract

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.

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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。
目的:本研究旨在评估基于深度学习的心血管疾病(CVD)视网膜生物标记物 Reti-CVD 识别心血管疾病中高危人群的能力:我们根据集合队列方程(PCE)、QRISK3和修正的弗雷明汉风险评分(FRS)定义了中高风险组。将 Reti-CVD 的预测结果与根据标准心血管疾病风险评估工具确定为中危和高危的人数进行比较,并计算灵敏度、特异性、阳性预测值 (PPV) 和阴性预测值 (NPV) 以评估结果。在英国生物库的 48 260 名参与者中,根据 PCE 和 QRISK3,分别有 20 643 人(42.8%)和 7192 人(14.9%)被归入中危和高危组。在新加坡眼病流行病学研究中,6810 名参与者中有 3799 人(55.8%)根据修改后的 FRS 被划分为中高危组。Reti-CVD 可识别基于 PCE 的中高危人群,灵敏度、特异性、PPV 和 NPV 分别为 82.7%、87.6%、86.5% 和 84.0%。Reti-CVD 可识别基于 QRISK3 的中危和高危人群,灵敏度、特异性、PPV 和 NPV 分别为 82.6%、85.5%、49.9% 和 96.6%。Reti-CVD根据改良的FRS确定中高危人群,其灵敏度、特异性、PPV和NPV分别为82.1%、80.6%、76.4%和85.5%:视网膜照片生物标志物(Reti-CVD)能够根据现有的风险评估工具识别心血管疾病的中高危人群。
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