Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores
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 Equatio...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2023
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| _version_ | 1811140047547662336 |
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| author | Yi, JK Rim, TH Park, S Kim, SS Kim, HC Lee, CJ Kim, H Lee, G Lim, JSG Tan, YY Yu, M Tham, Y Bakhai, A Shantsila, E Leeson, P Lip, GYH Chin, CWL Cheng, C |
| author_facet | Yi, JK Rim, TH Park, S Kim, SS Kim, HC Lee, CJ Kim, H Lee, G Lim, JSG Tan, YY Yu, M Tham, Y Bakhai, A Shantsila, E Leeson, P Lip, GYH Chin, CWL Cheng, C |
| author_sort | Yi, JK |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-09-25T04:15:46Z |
| format | Journal article |
| id | oxford-uuid:9a36d3d4-c596-44fa-96ed-11bc287c32b8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:15:46Z |
| publishDate | 2023 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:9a36d3d4-c596-44fa-96ed-11bc287c32b82024-07-20T13:58:55ZCardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scoresJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:9a36d3d4-c596-44fa-96ed-11bc287c32b8EnglishJisc Publications RouterOxford University Press2023Yi, JKRim, THPark, SKim, SSKim, HCLee, CJKim, HLee, GLim, JSGTan, YYYu, MTham, YBakhai, AShantsila, ELeeson, PLip, GYHChin, CWLCheng, CAims: 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. |
| spellingShingle | Yi, JK Rim, TH Park, S Kim, SS Kim, HC Lee, CJ Kim, H Lee, G Lim, JSG Tan, YY Yu, M Tham, Y Bakhai, A Shantsila, E Leeson, P Lip, GYH Chin, CWL Cheng, C Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores |
| title | Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores |
| title_full | Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores |
| title_fullStr | Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores |
| title_full_unstemmed | Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores |
| title_short | Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores |
| title_sort | cardiovascular disease risk assessment using a deep learning based retinal biomarker a comparison with existing risk scores |
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