Purpose: The aim of this study was to develop a risk stratification system that predicts visual outcomes (uncorrected corrected visual acuity at one week and five weeks postoperative) in patients undergoing cataract surgery.
Methods: This was a retrospective analysis in a multitier ophthalmology network. Data from all patients who underwent phacoemulsification or manual small-incision cataract surgery between January 2018 and December 2019 were retrieved from an electronic medical record system. There were 122,911 records; 114,172 (92.9%) had complete data included. Logistic regression analyzed unsatisfactory postoperative outcomes using a main effects model only. The final model was cross-checked using forward stepwise selection. The Hosmer-Lemeshow goodness of fit test, the Bayesian information criterion, and Nagelkerke's R2 assessed model fit. Dispersion was calculated from deviance and degrees of freedom and C-stat from receiving operating characteristics analysis.
Results: The final phacoemulsification model (n = 48,169) had a dispersion of 1.08 with a Hosmer-Lemeshow goodness of fit of 0.20, a Nagelkerke R2 of 0.19, and a C-stat of 0.72. The final manual small-incision cataract surgery model (n = 66,003) had a dispersion of 1.05 with a Hosmer-Lemeshow goodness of fit of 0.00015, a Nagelkerke R2 of 0.14, and a C-stat of 0.68.
Conclusion: The phacoemulsification model had reasonable model fit; the manual small-incision cataract surgery model had poor fit and was likely missing variables. The predictive capability of these models based on a large, real-world cataract surgical dataset was suboptimal to determine which patients could benefit most from sight-restoring surgery. Appropriate patient selection for cataract surgery in developing settings should still rely on clinician thought processes, intuition, and experience, with more complex cases allocated to more experienced surgeons.
Keywords: Cataract surgery; electronic medical records; model; real-world data; risk stratification.