To validate a regression-based keratometric prediction model in eyes with pterygium, emphasizing its importance for improving postoperative refractive outcomes after cataract surgery.
Key Findings:
Significant differences among the four keratometric methods for mean K, spherical equivalent, refractive error, and absolute refractive error, with specific statistical values.
No statistically significant differences between postoperative K and predicted K.
Preoperative corneal characteristics may influence refractive predictability.
Interpretation:
Regression-based K estimation showed refractive performance comparable to postoperative keratometry, suggesting its clinical utility when postoperative K values are unavailable, with implications for improving patient outcomes.
Limitations:
Small sample size of 20 eyes.
Retrospective design may introduce bias.
Exclusion of bilateral pterygium cases limits generalizability, with suggestions for future research directions.
Conclusion:
Regression-based keratometry can serve as a practical alternative for predicting refractive outcomes in pterygium-affected eyes post-cataract surgery, pending validation in larger studies to confirm its efficacy.
