目的:进行系统评价,以评估糖尿病视网膜病变(DR)风险预测模型中的药物暴露处理,进行网络荟萃分析以确定与DR相关的药物,并进行荟萃分析以确定哪些药物有助于增强模型性能.
方法:系统评价和荟萃分析。
方法:我们纳入了以药物暴露为预测因子的DR模型研究。我们搜索了EMBASE,MEDLINE和SCOPUS从成立到2023年12月。我们使用预测模型偏差风险评估工具和使用GRADE的确定性评估研究质量。我们进行了网络荟萃分析和荟萃分析,以估计优势比(OR)和合并的C统计量,分别,和95%置信区间(CI)(PROSPERO:CRD42022349764)。
结果:在确定的5,653条记录中,我们纳入了678,837名1型或2型糖尿病参与者的28项研究,其中38,579(5.7%)有DR。共有19项、3项和7项研究处于高位,不清楚,低偏见风险,分别。模型中作为预测因子的药物包括:胰岛素(n=24),抗高血压药(n=5),口服抗糖尿病药(n=12),降脂药物(n=7),抗血小板(n=2)。药物暴露主要被建模为分类变量(n=23项研究)。两项研究将药物暴露作为时变协变量处理,和一个作为时间依赖的协变量。胰岛素与DR风险增加相关(OR=2.50;95%-CI:1.61-3.86)。包含胰岛素的模型(n=9)具有较高的合并C统计量(C统计量=0.84,CI:0.80-0.88),与将药物与胰岛素结合在一起的模型(n=9)相比(C统计量=0.79,CI:0.74-0.84),以及不包括胰岛素的模型(n=3)(C统计量=0.70,CI:0.64-0.75)。局限性包括在综述的研究中偏倚的高风险和显著的异质性。
结论:这是评估DR预测模型中药物暴露处理的第一篇综述。药物暴露主要被建模为分类变量,胰岛素与改善模型性能相关。然而,由于药物处理欠佳,其他药物与模型性能之间的关联可能被忽视了。这篇综述对未来的DR预测模型提出了以下几点:1)评估药物暴露作为变量,2)使用时变方法,3)考虑药物方案细节。改善药物暴露处理可能会揭示能够显着增强预测模型预测能力的新变量。
OBJECTIVE: To conduct a systematic review to assess drug exposure handling in diabetic retinopathy (DR) risk prediction models, a network-meta-analysis to identify drugs associated with DR and a meta-analysis to determine which drugs contributed to enhanced model performance.
METHODS: Systematic review and meta-analysis.
METHODS: We included studies presenting DR models incorporating drug exposure as a predictor. We searched EMBASE, MEDLINE, and SCOPUS from inception to December 2023. We evaluated the quality of studies using the Prediction model Risk of Bias Assessment Tool and certainty using GRADE. We conducted network meta-analysis and meta-analysis to estimate the odds ratio (OR) and pooled C-statistic, respectively, and 95% confidence intervals (CI) (PROSPERO: CRD42022349764).
RESULTS: Of 5,653 records identified, we included 28 studies of 678,837 type 1 or 2 diabetes participants, of which 38,579 (5.7%) had DR. A total of 19, 3, and 7 studies were at high, unclear, and low risk of bias, respectively. Drugs included in models as predictors were: insulin (n = 24), antihypertensives (n = 5), oral antidiabetics (n = 12), lipid-lowering drugs (n = 7), antiplatelets (n = 2). Drug exposure was modelled primarily as a categorical variable (n = 23 studies). Two studies handled drug exposure as time-varying covariates, and one as a time-dependent covariate. Insulin was associated with an increased risk of DR (OR = 2.50; 95% CI: 1.61-3.86). Models that included insulin (n = 9) had a higher pooled C-statistic (C-statistic = 0.84, CI: 0.80-0.88), compared to models (n = 9) that incorporated a combination of drugs alongside insulin (C-statistic = 0.79, CI: 0.74-0.84), as well as models (n = 3) not including insulin (C-statistic = 0.70, CI: 0.64-0.75). Limitations include the high risk of bias and significant heterogeneity in reviewed studies.
CONCLUSIONS: This is the first review assessing drug exposure handling in DR prediction models. Drug exposure was primarily modelled as a categorical variable, with insulin associated with improved model performance. However, due to suboptimal drug handling, associations between other drugs and model performance may have been overlooked. This review proposes the following for future DR prediction models: (1) evaluation of drug exposure as a variable, (2) use of time-varying methodologies, and (3) consideration of drug regimen details. Improving drug exposure handling could potentially unveil novel variables capable of significantly enhancing the predictive capability of prediction models.