Risk prediction models for diabetic retinopathy: a systematic review - Hui Huang; Yingmin Wu; Hejiang Ye; Jiaoyang Li; Ling Chen; Xuan Huang (Frontiers in Endocrinology)
BackgroundDiabetic retinopathy, a prevalent complication of diabetes mellitus, is a growing public health concern. The use of robust predictive models can aid healthcare professionals in identifying high-risk patients, enabling them to implement early intervention and treatment strategies.ObjectiveTo systematically evaluate published prediction models for diabetic retinopathy, select better prediction models for healthcare professionals, and provide a valuable reference for model optimization.MethodsA comprehensive search was conducted across the PubMed, Web of Science, Embase, and the Cochrane Library databases for relevant literature on predictive models for diabetic retinopathy. The search period was set from the time of library construction to November 14, 2023. Furthermore, risk of bias and applicability assessment of the included study models were performed using the PROBAST risk assessment tool.ResultsA total of 2030 studies were retrieved, including 15 studies. The range of the working characteristic curve of the subjects for the 15 models varied from 0.700 to 0.960. All 15 included studies were recognized as high risk of bias. However, five studies had better applicability. The 15 models had Common risk factors for the 15 models included diabetes duration, age, glycosylated hemoglobin, serum creatinine and urinary albumin creatinine ratio.ConclusionsWhile the performance of the 15 models had certain predictive performance, the high risk of bias is a concern. Hopefully, future studies will ensure transparency and science in the model-building process by conducting large-sample integrated machine learning, reinforcing multicenter external validation. This study was registered with PROSPERO, an international prospective systematic evaluation registry platform, and the title was approved with registration number CRD42023483749.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024559392.