Nugawela Manjula D, Gurudas Sarega, Prevost A Toby, Mathur Rohini, Robson John, Sathish Thirunavukkarasu, Rafferty J M, Rajalakshmi Ramachandran, Anjana Ranjit Mohan, Jebarani Saravanan, Mohan Viswanathan, Owens David R, Sivaprasad Sobha
UCL Institute of Ophthalmology, 11-43 Bath Street, London EC1V 9EL, United Kingdom.
King's College London, Nightingale-Saunders Clinical Trials and Epidemiology Unit, London SE5 9PJ, United Kingdom.
EClinicalMedicine. 2022 Jul 22;51:101578. doi: 10.1016/j.eclinm.2022.101578. eCollection 2022 Sep.
Delayed diagnosis and treatment of sight threatening diabetic retinopathy (STDR) is a common cause of visual impairment in people with Type 2 diabetes. Therefore, systematic regular retinal screening is recommended, but global coverage of such services is challenging. We aimed to develop and validate predictive models for STDR to identify 'at-risk' population for retinal screening.
Models were developed using datasets obtained from general practices in inner London, United Kingdom (UK) on adults with type 2 Diabetes during the period 2007-2017. Three models were developed using Cox regression and model performance was assessed using C statistic, calibration slope and observed to expected ratio measures. Models were externally validated in cohorts from Wales, UK and India.
A total of 40,334 people were included in the model development phase of which 1427 (3·54%) people developed STDR. Age, gender, diabetes duration, antidiabetic medication history, glycated haemoglobin (HbA1c), and history of retinopathy were included as predictors in the Model 1, Model 2 excluded retinopathy status, and Model 3 further excluded HbA1c. All three models attained strong discrimination performance in the model development dataset with C statistics ranging from 0·778 to 0·832, and in the external validation datasets (C statistic 0·685 - 0·823) with calibration slopes closer to 1 following re-calibration of the baseline survival.
We have developed new risk prediction equations to identify those at risk of STDR in people with type 2 diabetes in any resource-setting so that they can be screened and treated early. Future testing, and piloting is required before implementation.
This study was funded by the GCRF UKRI (MR/P207881/1) and supported by the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology.
2型糖尿病患者中,威胁视力的糖尿病视网膜病变(STDR)的延迟诊断和治疗是导致视力损害的常见原因。因此,建议进行系统的定期视网膜筛查,但此类服务的全球覆盖面临挑战。我们旨在开发并验证STDR预测模型,以识别视网膜筛查的“高危”人群。
使用从英国伦敦市中心的普通诊所获取的2007年至2017年期间2型糖尿病成年患者的数据集开发模型。使用Cox回归开发了三个模型,并使用C统计量、校准斜率和观察与预期比率指标评估模型性能。在来自英国威尔士和印度的队列中对模型进行外部验证。
模型开发阶段共纳入40334人,其中1427人(3.54%)发生了STDR。模型1纳入年龄、性别、糖尿病病程、抗糖尿病药物治疗史、糖化血红蛋白(HbA1c)和视网膜病变史作为预测因素,模型2排除了视网膜病变状态,模型3进一步排除了HbA1c。所有三个模型在模型开发数据集中均具有较强的区分性能,C统计量范围为0.778至0.832,在外部验证数据集中(C统计量0.685 - 0.823),重新校准基线生存后校准斜率更接近1。
我们开发了新的风险预测方程,以识别任何资源环境下2型糖尿病患者中STDR的高危人群,以便他们能够早期接受筛查和治疗。在实施之前需要进行进一步的测试和试点。
本研究由英国全球挑战研究基金(MR/P207881/1)资助,并得到了摩尔菲尔德眼科医院国民保健服务基金会信托基金和伦敦大学学院眼科研究所的NIHR生物医学研究中心的支持。