Impact of Patient Characteristics and Surgery-Related Risk Factors on Endophthalmitis after Cataract surgery: A Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Cataracts are the leading cause of blindness worldwide, with a notably high incidence rate. Endophthalmitis is the most severe complication following cataract surgery, often resulting in profound vision loss. This study evaluates the impact of risk factors such as age, sex, diabetes mellitus (DM), hypertension, posterior capsule rupture (PCR), type of surgery, and use of intraocular lens (IOL) material on the risk of endophthalmitis after cataract surgery. METHODS: English and Chinese public databases were searched from inception to March 1, 2024. We included studies reporting the number of occurrences of endophthalmitis after cataract surgery on potential risk factors, including age, sex, DM status, hypertension status, intraoperative PCR, type of surgery and use of IOL material. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. RESULTS: A total of 57 studies were included and critically evaluated in the meta-analysis. The results showed that female sex was associated with a lower risk of endophthalmitis (odds ratio [OR]: 0.81; 95% CI: 0.75-0.87; p < 0.001). Individuals with diabetes who underwent cataract surgery were found to have a greater risk of endophthalmitis (I2 = 95%; OR: 4.90; 95% CI: 2.41, 9.95; p < 0.001), but the result may be influenced by publication bias. Individuals with hypertension (OR: 2.88; 95% CI: 1.53, 5.45; p = 0.001) and intraoperative PCR (OR: 9.18; 95% CI: 3.31, 25.43; p < 0.001) were found to have a greater risk of endophthalmitis. Phacoemulsification significantly reduced the risk of endophthalmitis compared with extracapsular cataract extraction (ECCE) (OR: 0.62; 95% CI: 0.45, 0.85) based on network meta-analysis. CONCLUSION: Male sex, hypertension, intraoperative PCR, and the use of the ECCE surgical approach are associated with a greater risk of postoperative endophthalmitis. Although an age-related trend in elevated risk was observed, this finding should be interpreted cautiously.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.061 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".