A deep learning system for retinal vessel calibre improves cardiovascular risk prediction in Asians with chronic kidney disease

NDT Plus Pub Date : 2023-09-19 DOI:10.1093/ckj/sfad227
Cynthia Ciwei Lim, Crystal Chong, Gavin Tan, Chieh Suai Tan, Carol Y Cheung, Tien Y Wong, Ching Yu Cheng, Charumathi Sabanayagam
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Abstract

ABSTRACT Backgraund Cardiovascular disease (CVD) and mortality is elevated in chronic kidney disease (CKD). Retinal vessel calibre in retinal photographs is associated with cardiovascular risk and automated measurements may aid CVD risk prediction. Methods Retrospective cohort study of 860 Chinese, Malay and Indian participants aged 40–80 years with CKD [estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2] who attended the baseline visit (2004–2011) of the Singapore Epidemiology of Eye Diseases Study. Retinal vessel calibre measurements were obtained by a deep learning system (DLS). Incident CVD [non-fatal acute myocardial infarction (MI) and stroke, and death due to MI, stroke and other CVD] in those who were free of CVD at baseline was ascertained until 31 December 2019. Risk factors (established, kidney, and retinal features) were examined using Cox proportional hazards regression models. Model performance was assessed for discrimination, fit, and net reclassification improvement (NRI). Results Incident CVD occurred in 289 (33.6%) over mean follow-up of 9.3 (4.3) years. After adjusting for established cardiovascular risk factors, eGFR [adjusted HR 0.98 (95% CI: 0.97–0.99)] and retinal arteriolar narrowing [adjusted HR 1.40 (95% CI: 1.17–1.68)], but not venular dilation, were independent predictors for CVD in CKD. The addition of eGFR and retinal features to established cardiovascular risk factors improved model discrimination with significantly better fit and better risk prediction according to the low (<15%), intermediate (15–29.9%), and high (30% or more) risk categories (NRI 5.8%), and with higher risk thresholds (NRI 12.7%). Conclusions Retinal vessel calibre measurements by DLS were significantly associated with incident CVD independent of established CVD risk factors. Addition of kidney function and retinal vessel calibre parameters may improve CVD risk prediction among Asians with CKD.
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视网膜血管口径深度学习系统改善亚洲慢性肾病患者心血管风险预测
背景:慢性肾脏疾病(CKD)患者心血管疾病(CVD)和死亡率升高。视网膜照片中的视网膜血管口径与心血管风险相关,自动化测量可能有助于心血管疾病风险预测。方法回顾性队列研究参加新加坡眼病流行病学研究基线访视(2004-2011)的860名年龄在40-80岁的中国、马来和印度CKD患者[估计肾小球滤过率(eGFR) & lt;60 ml/min/1.73 m2]。通过深度学习系统(DLS)获得视网膜血管直径测量值。在2019年12月31日之前,确定了基线时无CVD的患者的CVD发生率[非致死性急性心肌梗死(MI)和卒中,以及因MI、卒中和其他CVD导致的死亡]。使用Cox比例风险回归模型检查危险因素(既定、肾脏和视网膜特征)。对模型性能进行判别、拟合和净重分类改进(NRI)评估。结果平均随访9.3年(4.3年),289例(33.6%)发生心血管疾病。在校正心血管危险因素后,eGFR[校正HR 0.98 (95% CI: 0.97-0.99)]和视网膜小动脉狭窄[校正HR 1.40 (95% CI: 1.17-1.68)]是CKD中CVD的独立预测因子,但不是静脉扩张。将eGFR和视网膜特征添加到已建立的心血管危险因素中,可以改善模型的识别,根据低(<15%)、中(15-29.9%)和高(30%或以上)风险类别(NRI 5.8%)和更高的风险阈值(NRI 12.7%),模型的拟合和风险预测效果明显更好。结论DLS测量的视网膜血管直径与CVD发生率显著相关,与CVD危险因素无关。肾脏功能和视网膜血管口径参数的增加可能改善亚洲CKD患者CVD风险预测。
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