Predictive modeling of risk factors and complications of cataract surgery.

Purpose: Cataract surgery is generally safe; however, severe complications exist. Preexisting conditions are known to predispose patients to intraoperative and postoperative complications. This study quantifies the relationship between aggregated preoperative risk factors and cataract surgery complications, and builds a model predicting outcomes on an individual level, given a constellation of patient characteristics.

Methods: This study utilized a retrospective cohort of patients age 40 years or older who received cataract surgery. Risk factors, complications, and demographic information were extracted from the Electronic Health Record based on International Classification of Diseases, 9th edition codes, Current Procedural Terminology codes, drug prescription information, and text data mining. We used a bootstrapped least absolute shrinkage and selection operator model to identify highly associated variables. We built random forest classifiers for each complication to create predictive models.

Results: Our data corroborated existing literature, including the association of intraoperative complications, complex cataract surgery, black race, and/or prior eye surgery with increased risk of any postoperative complications. We also found other, less well-described risk factors, including diabetes mellitus, young age (<60 years), and hyperopia, as risk factors for complex cataract surgery and intraoperative and postoperative complications. Our predictive models outperformed existing published models.

Conclusions: The aggregated risk factors and complications described here can guide new avenues of research and provide specific, personalized risk assessment for a patient considering cataract surgery. Furthermore, the predictive capacity of our models can enable risk stratification of patients, which has utility as a teaching tool as well as informing quality/value-based reimbursements.