Preliminary Nomogram Established For Predicting Myopic Regression After Femtosecond Laser-Assisted In Situ Keratomileusis And Small Incision Lenticular Extraction
Published 2024 - 42nd Congress of the ESCRS
Reference: PP15.15 | Type: Free paper | DOI: 10.82333/fnrv-ah86
Authors: Jihong Zhou* 1 , Fengju Zhang 2 , Wenjuan Wang 3 , Guoli He 3 , Yan Gao 3 , Panzi Qiu 3
1Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Ophthalmology and Science Key Lab, Capital Medical University,Beijing,China;Beijing AierIntech Eye Hospital,Beijing,China, 2Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Ophthalmology and Science Key Lab, Capital Medical University,Beijing,China, 3Beijing AierIntech Eye Hospital,Beijing,China
Purpose
To explore the effectiveness of the Cox proportional hazards model in predicting myopic regression of patients undergoing corneal refractive surgery based on preoperative and operative data.
Setting
This is a retrospective cohort study conducted at Beijing AierIntech Eye Hospital. The study involved patients with myopia and astigmatism who underwent FS-LASIK or SMILE procedures between January 2010 and December 2020. Using the chronological data-splitting method, the study's subjects were split into two groups: a training set of 9,749 eyes and a validation set of 4,374 eyes.
Methods
Follow-up was conducted for three months to two years for patients with myopia and astigmatism who underwent FS-LASIK or SMILE procedures between January 2010 and December 2020. Myopic regression was defined as residual myopia of less than -0.50 D and a shift toward myopia of more than 0.50 D during at least three months of follow-up. Predictors were evaluated using multivariate Cox PH analysis. A nomogram model was created with Cox PH to identify factors predicting myopic regression, which was then validated in a separate group. C-index and calibration plots were utilised to measure discrimination and calibration, respectively, to assess the model's performance.
Results
By multivariate Cox PH analysis, wearing contact lenses, low myopia, preCCT, Kmin, optical zone (OZ), and ablation depth (AD) were protectors, yet SMILE, female, age, and PreHOARMS3 were independent risk factors. Myopia regression's top five predictors were anterior chamber depth, pre-sphere, preIOP, planned AD, and OZ. A model for predicting myopic regression has been established by integrating all preoperative and operative factors using the Cox PH model. The C-index of the model was found to be 0.674 and 0.651 in the training and validation groups, respectively. The model's predicted probabilities for myopic regression were consistent with the actual probabilities at 0.5, 1, and 2 years, indicating favourable performance in prediction.
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Conclusions
Five primary predictors were identified: anterior chamber depth, pre-sphere, preIOP, planned AD, and OZ. A novel nomogram model with discrimination and calibration was constructed to predict high-risk patients of myopic regression and provides an efficient strategy for preoperative assessment. This can benefit surgeons by delivering better patient counselling and helping them make the right surgical decisions.