Drivers of and Barriers to Adherence to Neovascular Age-Related Macular Degeneration and Diabetic Macular Edema Treatment Management Plans: A Multi-National Qualitative Study
Bibliographic record
Abstract
Purpose: Neovascular age-related macular degeneration (nAMD) and diabetic macular edema (DME) patients treated with intravitreally injected anti-vascular endothelial growth factor (anti-VEGF) monotherapies achieve lower vision improvements compared with patients in clinical trials. This qualitative research study aimed to better understand the real-world anti-VEGF treatment experience from nAMD and DME patients', caregivers', and retina specialists' perspectives. Methods: One-time, semi-structured, individual interviews were conducted with adult patients with nAMD or DME treated with anti-VEGF injections for ≥12 months, their caregivers, and experienced retina specialists. Interview transcripts were analyzed qualitatively using a thematic analysis approach. Results: A total of 49 nAMD and 46 DME patients, 47 nAMD and 33 DME caregivers, and 62 retina specialists were interviewed in the USA, Canada, France, Germany, Italy and Spain. Most (79%) patients and caregivers reported disruptions to their routine on the day before, the day of, or the day after anti-VEGF injection. Seven nAMD patients (14%) and 14 DME patients (30%) reported having missed an injection visit. The most frequently reported driver for adherence for patients was the doctor-patient relationship (n=66, 70%), whereas for caregivers, it was the ease of booking an appointment (n=25, 32%). Retina specialists reported patient education on the treatment (n=28, 45%) as the most important driver. Treatment barriers could be grouped into four categories: tolerability, clinical factors, logistical parameters and human factors. The most frequently reported barrier to adherence for patients and caregivers was related to side effects (pain/discomfort/irritation: n=63, 67% of patients; n=52, 66% of caregivers), whereas for retina specialists it was logistical parameters (travel logistics: n=44, 71%). Conclusion: This study highlights the importance of the doctor-patient relationship and patient education as key drivers, and treatment tolerability and logistics as key barriers to treatment adherence. Improved doctor-patient relationship/communication and patient education together with new therapies offering convenience, long-acting effectiveness, and better tolerability may improve treatment adherence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".