| 3 | 0 | 15 |
| 下载次数 | 被引频次 | 阅读次数 |
目的 系统评价威胁视力的糖尿病视网膜病变（VTDR）风险预测模型。方法 检索中国知网、维普、万方、中国生物医学文献数据库、CINAHL、PubMed、Embase、Web of Science、ProQuest、Ovid、Cochrane Library中英文数据库中与主题相关的文献，检索时限为建库至2025年12月。研究者根据纳入、排除标准筛选文献，2名研究员依据预测模型研究数据提取表和偏倚风险评估工具独立进行资料提取和质量评价。结果 研究纳入7篇文献，9个VTDR风险预测模型，总样本量为416～40 334例，结局事件数为50～6 187例，受试者工作特征曲线下面积为0.69～0.96，其中7个模型报告了校准，7个模型进行了外部验证。模型重复报告前5位的预测因子为年龄、糖尿病病程、糖化血红蛋白、血压、体质指数。现有模型的预测适用性普遍良好，但整体偏倚风险较高，主要源于研究设计缺陷、缺失数据处理不当与验证不充分。结论 当前VTDR风险预测模型尚处在发展阶段，不同研究预测因子差异明显，仍需进一步优化和完善。未来研究应需要更多前瞻性、多中心、大样本量的设计，并验证其在临床实践中的适用性和可行性。
Abstract：Objective To systematically evaluate risk prediction models for vision-threatening diabetic retinopathy(VTDR). Methods Search for relevant literature in Chinese and English databases, including CNKI, VIP, Wanfang, China Biomedical Literature Database, CINAHL, PubMed, Embase, Web of Science, ProQuest, Ovid, and Cochrane Library, from the inception of each database up to December 2025. Researchers will screen the literature according to inclusion and exclusion criteria. Two researchers will independently extract data and assess quality using a predesigned data extraction form and bias risk assessment tool. Results A total of seven studies involving nine VTDR risk prediction models were included, with sample sizes ranging from 416 to 40 334 participants and outcome events from 50 to 6 187. The area under the receiver operating characteristic curve ranged from 0. 69 to 0. 96. Seven models reported calibration performance, and seven underwent external validation. The top five most frequently reported predictors across models were age, duration of diabetes, glycated hemoglobin, blood pressure, and body mass index. The predictive applicability of the existing models was generally good, but the overall risk of bias was relatively high, mainly due to flaws in research design, improper handling of missing data, and insufficient validation. Conclusions Current VTDR risk prediction models are still in developmental stages, with significant differences in predictors across studies, and further optimization and improvement are still needed. Future studies should require more prospective, multicenter, and large-sample designs, and verify their applicability and feasibility in clinical practice.
[1]Li H, Jia W, Vujosevic S, et al. Current research and future strategies for the management of vision-threatening diabetic retinopathy[J]. Asia Pac J Ophthalmol(Phila), 2024,13(5):100109.
[2]Hou X, Wang L, Zhu D, et al. Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China[J]. Nat Commun, 2023,14(1):4296.
[3]Fong D S, Aiello L P, Ferris F L, et al. Diabetic retinopathy[J]. Diabetes Care, 2004,27(10):2540-2553.
[4]Cheung N, Mitchell P, Wong T Y. Diabetic retinopathy[J]. Lancet, 2010,376(9735):124-136.
[5]Cheung N, Wong T Y. Diabetic retinopathy and systemic vascular complications[J]. Prog Retin Eye Res, 2008,27(2):161-176.
[6]Teo Z L, Tham Y C, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045:systematic review and meta-analysis[J]. Ophthalmology, 2021,128(11):1580-1591.
[7]Usman T M, Saheed Y K, Nsang A, et al. A systematic literature review of machine learning based risk prediction models for diabetic retinopathy progression[J]. Artif Intell Med, 2023,143:102617.
[8]Van Der Heijden A A, Nijpels G, Badloe F, et al. Prediction models for development of retinopathy in people with type 2 diabetes:systematic review and external validation in a dutch primary care setting[J]. Diabetologia, 2020,63(6):1110-1119.
[9]Haider S, Sadiq S N, Moore D, et al. Prognostic prediction models for diabetic retinopathy progression:a systematic review[J].Eye(Lond), 2019,33(5):702-713.
[10]American Diabetes Association Professional Practice Committee.Classification and diagnosis of diabetes:standards of medical care in diabetes-2022[J]. Diabetes Care, 2022,45(S1):S17-S38.
[11]Moons K G M,de Groot J A,Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies:the CHARMS checklist[J]. PLoS Med, 2014,11(10):e1001744.
[12]Wolff R F, Moons K G M, Riley R D, et al. PROBAST:a tool to assess the risk of bias and applicability of prediction model studies[J]. Ann Intern Med, 2019,170(1):51-58.
[13]Moons K G M, Wolff R F, Riley R D, et al. PROBAST:a tool to assess risk of bias and applicability of prediction model studies:explanation and elaboration[J]. Ann Intern Med, 2019,170(1):W1-W33.
[14]Liang Y, Zhang X, Mei W, et al. Predicting vision-threatening diabetic retinopathy in patients with type 2 diabetes mellitus:systematic review, meta-analysis, and prospective validation study[J]. J Glob Health, 2024,14:04192.
[15]Gong D, Fang L, Cai Y, et al. Development and evaluation of a risk prediction model for diabetes mellitus type 2 patients with vision-threatening diabetic retinopathy[J]. Front Endocrinol(Lausanne), 2023,14:1244601.
[16]An Y, Cao B, Li K, et al. A prediction model for sight-threatening diabetic retinopathy based on plasma adipokines among patients with mild diabetic retinopathy[J]. J Diabetes Res, 2023,2023:8831609.
[17]Ke J, Li K, Cao B. A nomogram for predicting vision-threatening diabetic retinopathy among mild diabetic retinopathy patients:a case-control and prospective study of type 2 diabetes[J]. Diabetes Metab Syndr Obes, 2023,16:275-283.
[18]罗瑾，黄文勇，黎宇婷，等.威胁视力的2型糖尿病视网膜病变风险预测模型的建立与验证[J].中山大学学报（医学科学版），2023,44(6):999-1007.
[19]Jo K, Chang D J, Min J W, et al. Long-term prediction models for vision-threatening diabetic retinopathy using medical features from data warehouse[J]. Sci Rep, 2022,12(1):8476.
[20]Nugawela M D, Gurudas S, Prevost A T, et al. Development and validation of predictive risk models for sight threatening diabetic retinopathy in patients with type 2 diabetes to be applied as triage tools in resource limited settings[J]. EClinicalMedicine, 2022,51:101578.
[21]Wilkinson C P, Ferris F L, Klein R E, et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales[J]. Ophthalmology, 2003,110(9):1677-1682.
[22]Peng Y, Guo X, Liu J, et al. Incidence and risk factors for diabetic retinopathy in the communities of Shenzhen[J]. Ann Palliat Med, 2021,10(1):615-624.
[23]Yan J, Li B, Chen Y, et al. Prevalence and predictors of developing vision-threatening diabetic retinopathy within the first three years of type 2 diabetes[J]. Front Endocrinol(Lausanne), 2023,14:1305378.
[24]American Diabetes Association. Clinical practice recommendations 2000[J]. Diabetes Care, 2000,23(S1):S1-S116.
[25]Raman R, Vasconcelos J C, Rajalakshmi R, et al. Prevalence of diabetic retinopathy in India stratified by known and undiagnosed diabetes, urban-rural locations, and socioeconomic indices:results from the SMART India population-based cross-sectional screening study[J]. Lancet Glob Health, 2022,10(12):e1764-e1773.
[26]Rooney D, Lye W K, Tan G, et al. Body mass index and retinopathy in Asian populations with diabetes mellitus[J]. Acta Diabetol, 2015,52(1):73-80.
[27]Zhou Y, Zhang Y, Shi K, et al. Body mass index and risk of diabetic retinopathy:a meta-analysis and systematic review[J].Medicine(Baltimore), 2017,96(22):e6754.
[28]莫航沣，陈亚萍，韩慧，等.临床预测模型研究方法与步骤[J].中国循证医学杂志，2024,24(2):228-236.
[29]Ting D S W, Cheung C Y, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes[J]. JAMA, 2017,318(22):2211-2223.
[30]Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016,316(22):2402-2410.
基本信息:
中图分类号:R587.2;R774.1
引用信息:
[1]金贵央,裴瑶,方欣怡,等.威胁视力的糖尿病视网膜病变风险预测模型的系统评价[J].老年医学研究,2026,7(03):25-30.
2026-06-25
2026-06-25