Use of predictive models to identify patients who are likely to benefit from refraction at a follow-up visit after cataract surgery

Abstract

Purpose: To develop predictive models to identify cataract surgery patients who are more likely to benefit from refraction at a four-week postoperative exam. Methods: In this retrospective study, we used data of all 86,776 cataract surgeries performed in 2015 at a large tertiary-care eye hospital in India. The outcome variable was a binary indicator of whether the difference between corrected distance visual acuity and uncorrected visual acuity at the four-week postoperative exam was at least two lines on the Snellen chart. We examined the following statistical models: logistic regression, decision tree, pruned decision tree, random forest, weighted k-nearest neighbor, and a neural network. Predictor variables included in each model were patient sex and age, source eye (left or right), preoperative visual acuity, first-day postoperative visual acuity, intraoperative and immediate postoperative complications, and combined surgeries. We compared the predictive performance of models and assessed their clinical impact in test samples. Results: All models demonstrated predictive accuracy better than chance based on area under the receiver operating characteristic curve. In a targeting exercise with a fixed intervention budget, we found that gains from predictive models in identifying patients who would benefit from refraction ranged from 7.8% (increase from 1500 to 1617 patients) to 74% (increase from 250 to 435 patients). Conclusion: The use of predictive statistical models to identify patients who are likely to benefit from refraction at follow-up can improve the economic efficiency of interventions. Simpler models like logistic regression perform almost as well as more complex machine-learning models, but are easier to implement.