Developing and Validating Models to Predict Progression to Proliferative Diabetic Retinopathy.

Purpose: To develop models for progression of nonproliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR) and determine if incorporating updated information improves model performance. Design: Retrospective cohort study. Participants: Electronic health record (EHR) data from a tertiary academic center, University of California San Francisco (UCSF), and a safety-net hospital, Zuckerberg San Francisco General (ZSFG) Hospital were used to identify patients with a diagnosis of NPDR, age ≥ 18 years, a diagnosis of type 1 or 2 diabetes mellitus, ≥ 6 months of ophthalmology follow-up, and no prior diagnosis of PDR before the index date (date of first NPDR diagnosis in the EHR). Methods: Four survival models were developed: Cox proportional hazards, Cox with backward selection, Cox with LASSO regression and Random Survival Forest. For each model, three variable sets were compared to determine the impact of including updated clinical information: Static Main Outcome Measures: Time to PDR. Results: The UCSF cohort included 1130 patients and 92 (8.1%) patients progressed to PDR. The ZSFG cohort included 687 patients and 30 (4.4%) patients progressed to PDR. All models performed similarly (C-indices ∼ 0.70) in internal validation. The random survival forest with Static Conclusions: Our models for progression of NPDR to PDR achieved acceptable predictive performance and validated well in an external setting. Updating the baseline variables with new clinical information did not consistently improve the predictive performance. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.