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Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank

BACKGROUND: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk s...

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Autores principales: Tseng, Rachel Marjorie Wei Wen, Rim, Tyler Hyungtaek, Shantsila, Eduard, Yi, Joseph K., Park, Sungha, Kim, Sung Soo, Lee, Chan Joo, Thakur, Sahil, Nusinovici, Simon, Peng, Qingsheng, Kim, Hyeonmin, Lee, Geunyoung, Yu, Marco, Tham, Yih-Chung, Bakhai, Ameet, Leeson, Paul, Lip, Gregory Y.H., Wong, Tien Yin, Cheng, Ching-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872417/
https://www.ncbi.nlm.nih.gov/pubmed/36691041
http://dx.doi.org/10.1186/s12916-022-02684-8
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author Tseng, Rachel Marjorie Wei Wen
Rim, Tyler Hyungtaek
Shantsila, Eduard
Yi, Joseph K.
Park, Sungha
Kim, Sung Soo
Lee, Chan Joo
Thakur, Sahil
Nusinovici, Simon
Peng, Qingsheng
Kim, Hyeonmin
Lee, Geunyoung
Yu, Marco
Tham, Yih-Chung
Bakhai, Ameet
Leeson, Paul
Lip, Gregory Y.H.
Wong, Tien Yin
Cheng, Ching-Yu
author_facet Tseng, Rachel Marjorie Wei Wen
Rim, Tyler Hyungtaek
Shantsila, Eduard
Yi, Joseph K.
Park, Sungha
Kim, Sung Soo
Lee, Chan Joo
Thakur, Sahil
Nusinovici, Simon
Peng, Qingsheng
Kim, Hyeonmin
Lee, Geunyoung
Yu, Marco
Tham, Yih-Chung
Bakhai, Ameet
Leeson, Paul
Lip, Gregory Y.H.
Wong, Tien Yin
Cheng, Ching-Yu
author_sort Tseng, Rachel Marjorie Wei Wen
collection PubMed
description BACKGROUND: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5–10% (termed as borderline-QRISK3 group) using the UK Biobank. METHODS: Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups. RESULTS: Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30–1.52) with a 13.1% (95% CI, 11.7–14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010–0.017) in non-statin cohort, 0.013 (0.007–0.019) in stage 1 hypertension cohort, and 0.023 (0.018–0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3. CONCLUSIONS: Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02684-8.
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spelling pubmed-98724172023-01-25 Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank Tseng, Rachel Marjorie Wei Wen Rim, Tyler Hyungtaek Shantsila, Eduard Yi, Joseph K. Park, Sungha Kim, Sung Soo Lee, Chan Joo Thakur, Sahil Nusinovici, Simon Peng, Qingsheng Kim, Hyeonmin Lee, Geunyoung Yu, Marco Tham, Yih-Chung Bakhai, Ameet Leeson, Paul Lip, Gregory Y.H. Wong, Tien Yin Cheng, Ching-Yu BMC Med Research Article BACKGROUND: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5–10% (termed as borderline-QRISK3 group) using the UK Biobank. METHODS: Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups. RESULTS: Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30–1.52) with a 13.1% (95% CI, 11.7–14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010–0.017) in non-statin cohort, 0.013 (0.007–0.019) in stage 1 hypertension cohort, and 0.023 (0.018–0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3. CONCLUSIONS: Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02684-8. BioMed Central 2023-01-24 /pmc/articles/PMC9872417/ /pubmed/36691041 http://dx.doi.org/10.1186/s12916-022-02684-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Tseng, Rachel Marjorie Wei Wen
Rim, Tyler Hyungtaek
Shantsila, Eduard
Yi, Joseph K.
Park, Sungha
Kim, Sung Soo
Lee, Chan Joo
Thakur, Sahil
Nusinovici, Simon
Peng, Qingsheng
Kim, Hyeonmin
Lee, Geunyoung
Yu, Marco
Tham, Yih-Chung
Bakhai, Ameet
Leeson, Paul
Lip, Gregory Y.H.
Wong, Tien Yin
Cheng, Ching-Yu
Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank
title Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank
title_full Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank
title_fullStr Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank
title_full_unstemmed Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank
title_short Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank
title_sort validation of a deep-learning-based retinal biomarker (reti-cvd) in the prediction of cardiovascular disease: data from uk biobank
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872417/
https://www.ncbi.nlm.nih.gov/pubmed/36691041
http://dx.doi.org/10.1186/s12916-022-02684-8
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