Abstract
PURPOSE: 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.
DESIGN: 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.
CONCLUSION: 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.
| Original language | English |
|---|---|
| Journal | American Journal of Ophthalmology |
| Early online date | 19 Jul 2024 |
| DOIs | |
| Publication status | E-pub ahead of print - 19 Jul 2024 |
Bibliographical note
AcknowledgmentsFunding/ Support: This work was supported by Medical Research Scotland (MRS) (MRS Vacation Scholarship 2022), NHS Grampian Endowments Research and Development Endowment (19/030) and Chief Scientist office, Scotland. The sponsor or funding organization had no role in the design or conduct of this research. No other funding or support was received by any of the authors for the present manuscript.
Data Availability Statement
The data (template data collection forms; data extracted from included studies; data used for all analyses; analytic code) that support the findings of this study are available upon reasonable request from the corresponding author.Keywords
- Risk prediction model
- drug modelling
- diabetic retinopathy
- drug exposure
- network meta-analysis
