Predicting the outcome of laser peripheral iridotomy for primary angle closure suspect eyes using anterior segment optical coherence tomography. Issue 1 (3rd October 2018)

• Record Type: Journal Article
• Authors: Koh, Victor; Keshtkaran, Mohammad Reza; Hernstadt, David; Aquino, Maria Cecilia D.; Chew, Paul T.; Sng, Chelvin
• Is Part Of: Acta ophthalmologica. Volume 97:Issue 1(2019)
• Page Start: e57
• Page End: e63
• Publication Date: 2018-10-03
• Subjects: angle closure -- anterior segment optical coherence tomography -- glaucoma -- laser iridotomy; Ophthalmology -- Periodicals; 617.7005
• DOI: 10.1111/aos.13822
• Languages: English
• ISSNs: 1755-375X

Abstract

Abstract: Purpose: Develop an algorithm to predict the success of laser peripheral iridotomy (LPI) in primary angle closure suspect (PACS), using pretreatment anterior segment optical coherence tomography (ASOCT) scans. Methods: A total of 69 eyes with PACS underwent LPI and time‐domain ASOCT scans (temporal and nasal cuts) were performed before and after LPI. After LPI, success is defined as one or more angles changed from closed to open. All the pretreatment ASOCT scans were analysed using the Anterior Segment Analysis Program to derive anterior chamber angle (ACA) measurements. The measurements for each angle were ordered along with angle‐independent measurements totalling to 42 measurements which serve as features for the prediction algorithm. Two masked glaucoma fellowship‐trained ophthalmologists graded the pre‐LPI ASOCT scans to determine whether LPI was likely to successful. Results: There were 42 (60.9%) eyes that fulfilled the criteria for success after LPI. Iris concavity, angle recess area (750 μ m) and iris concavity ratio showed the highest predictive score and were selected using correlation‐based subset selection method. These features were classified into two ('successful' and 'unsuccessful') categories using a Bayes classifier. The algorithm predicted the success of LPI with 79.28% cross validation accuracy, which was superior to the predictive accuracy of the ophthalmologists (kappa 0.497 and 0.636 respectively). Conclusion: Using pretreatment ASOCT scans, our algorithm was superior to ophthalmologists in predicting the success of LPI for PACS eyes. This novel algorithm could aid decision making in offering LPI as a prophylaxis for PACS.