Karandeep Singh1,2,3, Alexa Thibodeau4, Leslie M Niziol4, Tejpreet K Nakai2, Jill E Bixler4, Mariam Khan4, Maria A Woodward3,4. 1. Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI. 2. Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI. 3. Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI; and. 4. Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI.
Abstract
PURPOSE: 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. METHODS: 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). RESULTS: 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%. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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). RESULTS: 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%. CONCLUSIONS: 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.
Authors: Maria A Woodward; Nita G Valikodath; Paula Anne Newman-Casey; Leslie M Niziol; David C Musch; Paul P Lee Journal: Eye Contact Lens Date: 2018-11 Impact factor: 2.018
Authors: Lisa Bennett; Hugo Y Hsu; Shannon Tai; Benjamin Ernst; Eric J Schmidt; Rohit Parihar; Chelsea Horwood; Sean L Edelstein Journal: Eye Contact Lens Date: 2019-09 Impact factor: 2.018
Authors: Joshua D Stein; Moshiur Rahman; Chris Andrews; Joshua R Ehrlich; Shivani Kamat; Manjool Shah; Erin A Boese; Maria A Woodward; Jeff Cowall; Edward H Trager; Prabha Narayanaswamy; David A Hanauer Journal: JAMA Ophthalmol Date: 2019-05-01 Impact factor: 7.389