The prediction capability of a cataract surgery risk stratification model based on a large electronic medical record dataset.

Abstract

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. 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. 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. 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.