Evidence map›Paper›PMID 36691041›Full record

ArticleBMC medicine2023

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

Rachel Marjorie Wei Wen Tseng, Tyler Hyungtaek Rim, Eduard Shantsila, Joseph K Yi, Sungha Park, Sung Soo Kim, Chan Joo Lee, Sahil Thakur, Simon Nusinovici, Qingsheng Peng and 9 more

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
12.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 7 institutions in 6 countries.

Rachel Marjorie Wei Wen Tseng *Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Tyler Hyungtaek Rim *Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore. tyler.rim.academia@gmail.com.ORCID 0000-0001-6465-2620
Eduard ShantsilaDepartment of Primary Care and Mental Health, University of Liverpool, Liverpool, UK.
Joseph K YiAlbert Einstein College of Medicine, New York, NY, USA.
Sungha ParkDivision of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Sung Soo KimDivision of Retina, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Chan Joo LeeDivision of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Sahil ThakurSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Simon NusinoviciSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Qingsheng PengSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Hyeonmin KimMediwhale Inc., Seoul, South Korea.
Geunyoung LeeMediwhale Inc., Seoul, South Korea.
Marco YuSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Yih-Chung ThamSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Ameet BakhaiRoyal Free Hospital London NHS Foundation Trust, London, UK.
Paul LeesonCardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, Oxford, UK.
Gregory Y H LipLiverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; and Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
Tien Yin WongSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Ching-Yu ChengSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
Singapore National Eye Center · SGYonsei University · KRNational University of Singapore · SGUniversity of Liverpool · GBAlbert Einstein College of Medicine · USRoyal Free London NHS Foundation Trust · GBUniversity of Oxford · GB

Funding

Medical Research Council MC_PC_17228Medical Research Council MR/W003686/1
6 · The paper itself

Abstract

backgroundCurrently 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.

methodsReti-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.

resultsAmong 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.

conclusionsReti-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.

Indexed as

Cardiovascular DiseasesDeep LearningHypertensionAdultBiological Specimen BanksBiomarkersHumansMiddle AgedRisk FactorsUnited KingdomBiomarkersArtificial intelligenceCardiovascular diseaseDeep learningRetinal imagingRetinal photographRisk stratificationRisk stratification systemUK Biobank

Identifiers

PMID36691041
PMCPMC9872417
OpenAlexW4317788441

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.