Loss to Follow-Up in Patients With Proliferative Diabetic Retinopathy or Diabetic Macular Edema
Bibliographic record
Abstract
Importance: Effective management of proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME) requires reliable patient follow-up to prevent disease progression. Objective: To investigate the sociodemographic and clinical factors associated with being lost to follow-up (LTFU) among individuals with PDR or DME treated with anti-vascular endothelial growth factor (VEGF) intravitreal injections (IVIs) or panretinal photocoagulation (PRP). Design, Setting, and Participants: This cohort study included a multicenter, retrospective review of patients with PDR or DME treated in Toronto, Canada, from January 1, 2012, to December 31, 2021. Data were analyzed from February 1 to May 31, 2024. Exposures: All patients received at least 1 anti-VEGF IVI or PRP session. Main Outcomes and Measures: The primary outcome was the LTFU rate, defined as the absence of an ophthalmic visit or intervention in the 1-year period following an individual's last visit with the treating retinal specialist. Univariable and multivariable logistic regression models were conducted to evaluate associations between sociodemographic and clinical factors with the LTFU rate. Results: Overall, 2961 patients with PDR or DME (mean [SD] age, 71 [13] years; 1640 [55.4%] male) were included, of whom 507 (17.1%) were LTFU over a mean (SD) follow-up period of 61 (22) months. In the multivariable analysis, older patients (age ≥85 years vs age <65 years: odds ratio [OR], 0.58; 95% CI, 0.40-0.81; P = .002), those with worse baseline visual acuity (>20/200 Snellen vs 20/40 Snellen or better: OR, 0.68; 95% CI, 0.48-0.97; P = .04), those with DME (OR vs no DME, 0.60; 95% CI, 0.43-0.83; P = .003), those with frequent clinic visits (≥6 visits vs <6 visits: OR, 0.78; 95% CI, 0.62-0.98; P = .04), and those with a high anti-VEGF IVI burden in the first year (OR vs low anti-VEGF burden, 0.40; 95% CI, 0.21-0.76; P = .006) were less likely to be LTFU. In contrast, males (OR vs females, 1.23; 95% CI, 1.04-1.52; P = .04), patients living further from the point of care (>200 vs ≤20 km OR, 2.65; 95% CI, 1.85-3.76; P < .001), and those treated with PRP (OR vs anti-VEGF IVIs, 2.10; 95% CI, 1.24-3.55; P < .001) were more likely to be LTFU. Compared with White patients, Black patients (OR, 2.10; 95% CI, 1.50-2.95; P < .001) and Hispanic patients (OR, 1.54; 95% CI, 1.05-2.21; P = .03) were more likely to be LTFU. Conclusions and Relevance: This cohort study found multiple factors associated with LTFU rates. Identifying individuals at higher risk of LTFU and developing targeted strategies may reduce disease progression and vision loss in individuals with PDR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".