Predictive Modeling of Risk Factors and Complications of Cataract Surgery

Abstract

Purpose To quantify the relationship between aggregated preoperative risk factors and cataract surgery complications, as well as to build a model predicting outcomes on an individual-level—given a constellation of demographic, baseline, preoperative, and intraoperative patient characteristics. Setting Stanford Hospital and Clinics between 1994 and 2013. Design Retrospective cohort study Methods Patients age 40 or older who received cataract surgery between 1994 and 2013. Risk factors, complications, and demographic information were extracted from the Electronic Health Record (EHR), based on International Classification of Diseases, 9th edition (ICD-9) codes, Current Procedural Terminology (CPT) codes, drug prescription information, and text data mining using natural language processing. We used a bootstrapped least absolute shrinkage and selection operator (LASSO) model to identify highly-predictive variables. We built random forest classifiers for each complication to create predictive models. Results Our data corroborated existing literature on postoperative complications—including the association of intraoperative complications, complex cataract surgery, black race, and/or prior eye surgery with an increased risk of any postoperative complications. We also found a number of other, less well-described risk factors, including systemic diabetes mellitus, young age (<60 years old), and hyperopia as risk factors for complex cataract surgery and intra- and post-operative complications. Our predictive models based on aggregated outperformed existing published models. Conclusions The constellations of risk factors and complications described here can guide new avenues of research and provide specific, personalized risk assessment for a patient considering cataract surgery. 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.

Auckland Cataract Study IV: Practical application of NZCRS cataract risk stratification to reduce phacoemulsification complications

“…Kim et al reported the significant benefits of cataract surgery risk stratification in New Zealand and Han et al subsequently reported a refined risk stratification system, the New Zealand Cataract Risk Stratification system. This system (Table ) incorporates elements from the Muhtaseb system, plus one higher risk parameter (score > 3) and three new elements with additional scoring for: use of oral alpha‐antagonists; prior vitrectomy, due to predisposition to intraoperative complications and “only eye” patients based on feedback on patient functioning from earlier phases of the study . Subsequently, we reported that the NZCRS system systematically identifies a greater proportion of cataract cases at higher risk of complication than the earlier Muhtaseb system …”

Analysis of a large data set to identify predictors of blood transfusion in primary total hip and knee arthroplasty

“…Among these algorithms, random forest (RF) analysis is most commonly used in medical care. It has been used in cheminformatics to predict chemical compound activities, for selecting single‐nucleotide polymorphisms predictive of phenotypes, for predicting survival outcomes, and for identifying the risk factors for complications of cataract surgery . However, RF has not yet been applied to the challenge of predicting the need for blood transfusion in total joint arthroplasty (TJA).…”

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology

“…And Gaskin et al. 15 used random forest and bootstrapped LASSO to identify and predict risks of cataract surgery complications. Moreover, Yoo and Park 25 used elastic net and LASSO to predict DR risk among diabetic patients.…”

“… 43 As shown in the Table , Apostolova et al., 23 Peissig et al., 27 and Gaskin et al. 15 describe the use of NLP in extracting cataract information from free-form text clinical notes.…”

“… 14 Likewise in ophthalmology, machine learning classifiers with EHR data have been used to predict risks of cataract surgery complications, improve diagnosis of glaucoma and age-related macular degeneration (AMD), and perform risk assessment of diabetic retinopathy (DR). 15 – 18 …”