Artificial intelligence enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke
Artificial intelligence enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke
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人工智能使视网膜血管测量能够预测循环死亡率、心肌梗死和中风
DOI：
10.1101/2022.05.16.22275133
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Rudnicka A
中科院分区：
文献类型：
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作者：
Rudnicka A
目的我们研究是否包括人工智能（AI）启用视网膜血管测量（RV）改善现有的中风事件的风险算法，心肌梗死（MI）和循环系统死亡率。方法人工智能启用视网膜血管图像分析处理图像从88 052英国生物银行（UKB）参与者（在图像捕获时年龄为40-69岁）和7411名欧洲癌症前瞻性研究（EPIC）-诺福克参与者（年龄为48-92岁）。提取视网膜微动脉和微静脉的宽度、迂曲度和面积。使用循环系统死亡率、卒中事件和MI的多变量考克斯比例风险回归，在UKB开发预测模型，并在EPIC-Norfolk进行外部验证。使用乐观调整校准、C-统计量和R2统计量评估模型性能。在FRS中加入RV，将卒中和心肌梗死事件的FRS表现与基于RV、年龄、吸烟状况和病史的简单模型进行比较（抗高血压/降胆固醇药物、糖尿病、流行性卒中/心肌梗死）。（平均年龄56.8岁;中位随访时间7.7年），并在5862名EPIC-Norfolk参与者中进行了验证（分别为67.6，9.1年）。男性和女性循环系统死亡率预测模型的乐观调整C统计量和R2统计量分别在0.75-0.77和0.33-0.44之间。对于卒中和MI事件，在FRS中添加RV并未改善任一队列的模型性能。然而，更简单的RV模型执行相同或更好比FRS.ConclusionRV提供了一种替代的预测生物标志物，传统的风险评分血管健康，而不需要血液采样或血压测量。需要进一步的工作来检查RV在人群筛查中对高风险个体进行分流。
AimsWe examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality.MethodsAI-enabled retinal vessel image analysis processed images from 88 052 UK Biobank (UKB) participants (aged 40–69 years at image capture) and 7411 European Prospective Investigation into Cancer (EPIC)-Norfolk participants (aged 48–92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C-statistics and R2statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI).ResultsUKB prognostic models were developed on 65 144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5862 EPIC-Norfolk participants (67.6, 9.1 years, respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C-statistics and R2statistics between 0.75–0.77 and 0.33–0.44, respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS.ConclusionRV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk.