Development and Validation of a Model to Predict Anterior Segment Vision-Threatening Eye Disease Using Primary Care Clinical Notes.

Abstract

The purpose of this study was to develop a decision-support tool to predict anterior segment vision-threatening disease (asVTD) to aid primary care physicians (PCPs) with patient triage and referral. The University of Michigan electronic health record data between January 1, 2016, and May 31, 2019, were obtained from patients presenting to a PCP with anterior eye symptoms and then saw an ophthalmologist within 30 days. asVTD included diagnosis of corneal ulcer, iridocyclitis, hyphema, anterior scleritis, or scleritis with corneal involvement by an ophthalmologist. Elastic net logistic regression with 10-fold cross-validation was used for prediction modeling of asVTD. Predictors evaluated included patient demographics and PCP notes processed using clinical natural language processing software (clinspacy). Two thousand nine hundred forty-two patients met the inclusion criteria, of which 133 patients (4.5%) had asVTD. The age was significantly lower among those with asVTD versus those without (median = 42 vs. 53 yrs, P < 0.001). Sex ( P = 0.8) and race ( P = 0.9) were not significantly different between groups. The final prediction model had an area under the curve of 0.72 (95% confidence interval 0.67-0.77). At a threshold achieving a sensitivity of 90%, the specificity was 30%, the positive predictive value was 5.8%, and the negative predictive value was 99%. The use of the prediction model increased the positive predictive value for asVTD compared with referral based on prevalence probabilities (17 patients vs. 22 patients needing to be evaluated to identify 1 case of asVTD). A prediction algorithm has potential to improve triage and initial management decision-making for PCPs because it performs better than probabilities in the absence of such a tool.