Optical Coherence Tomography Predictive Biomarkers for Visual Outcomes after Macula-Off Rhegmatogenous Retinal Detachment: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
TOPIC: Systematic review and meta-analysis of preoperative optical coherence tomography (OCT) features as predictors of visual outcomes following macula-off rhegmatogenous retinal detachment (RRD) surgery. CLINICAL RELEVANCE: To assess the impact of preoperatively assessed OCT parameters including photoreceptor ellipsoid zone (EZ) integrity, external limiting membrane (ELM) preservation, outer retinal corrugations (ORCs), subretinal fluid (SRF) height, intraretinal cysts (IRC), and epiretinal membrane (ERM) on post-operative best-corrected visual acuity (BCVA). METHODS: We systematically searched PubMed, Embase, and Cochrane Library databases through March 2025. Eligible studies evaluated preoperative OCT parameters as predictors of postoperative visual outcomes. Meta-analysis was performed using random-effects models with DerSimonian-Laird method to calculate pooled odds ratios (ORs) and standardized mean differences (SMDs) with 95% confidence intervals (CIs). Risk of bias for individual studies was assessed using a Modified Newcastle-Ottawa Quality Assessment Scale. The certainty of the body of evidence for each outcome was evaluated using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach. RESULTS: Twenty studies with 1841 eyes were included. Lower SRF height was associated with better outcomes (SMD: -0.72, 95% CI: -1.08 to -0.36; I² = 71%). Intact EZ was significantly associated with better visual outcomes (pooled OR: 3.76, 95% CI: 2.61-5.42; I² = 54%). Preserved ELM integrity was associated with better outcomes (pooled OR: 3.24, 95% CI: 2.13-4.92; I² = 49%). Absence of ORCs was associated with better outcomes (pooled OR: 2.83, 95% CI: 1.94-4.13; I² = 47%). Absence of IRC and ERM improved outcomes (OR: 2.26 and 2.64 respectively). SRF height predictive value was stronger for detachments ≤7 days. Morphologic staging systems showed strong correlation with visual outcomes (pooled r = 0.64, 95% CI: 0.52-0.74). CONCLUSIONS: Based on low to very low-quality evidence according to GRADE assessment, preoperative OCT biomarkers may predict visual outcomes after macula-off RRD surgery, with photoreceptor layer integrity showing the strongest associations. However, the high heterogeneity, predominance of retrospective studies, and methodological limitations require that these findings be interpreted with significant caution and validated in high-quality prospective studies before clinical implementation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".