C Goudie1, A Khan1, C Lowe2, M Wright1,2. 1. Department of Ophthalmology, Princess Alexandra Eye Pavilion, Edinburgh, Scotland. 2. College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, Scotland.
Abstract
PURPOSE: To assess the diagnostic accuracy of the Edinburgh visual loss algorithm. METHODS: This was a prospective study. Patients referred to the Edinburgh Eye Pavilion with visual loss were assessed using the Edinburgh Visual Loss Algorithm by either a medical student, an inexperienced ophthalmology trainee or an optometrist in the Lothian Optometry Treat and Teach clinic. Accuracy of this 'algorithm-assisted' diagnosis was then compared with the 'gold-standard' diagnosis, made by an experienced ophthalmologist. Accuracy of the pre-algorithm diagnosis, made by the referrer, was also compared with the algorithm-assisted diagnosis. RESULTS: All patients referred with visual loss were eligible for inclusion. Seventy patients were assessed; two were excluded. Pre-algorithm accuracy of referral of patients with visual loss was 51% (30/59). Overall, the algorithm-assisted diagnosis was correct 84% (57/68) of the time. The algorithm correctly diagnosed: retina in 71% of cases (5/7), macula in 86% (25/29), peripheral retina in 100% (2/2), optic nerve in 71% (5/7), media opacity in 89% (16/18), post chiasmal in 100% (4/4), and refractive error in 0% (0/1). Accuracy of diagnosis was similar for each algorithm user; medical student 81%, inexperienced ophthalmology trainee 84% and optometrist 92%. DISCUSSION: The baseline diagnostic accuracy of clinicians who are inexperienced in ophthalmology rose from 51 to 84% when patients were assessed using the algorithm. This algorithm significantly improves the diagnostic accuracy of referrals to the hospital eye service, regardless of the user's previous ophthalmic experience. We hope we have demonstrated its potential as a learning tool for inexperienced clinicians.
PURPOSE: To assess the diagnostic accuracy of the Edinburgh visual loss algorithm. METHODS: This was a prospective study. Patients referred to the Edinburgh Eye Pavilion with visual loss were assessed using the Edinburgh Visual Loss Algorithm by either a medical student, an inexperienced ophthalmology trainee or an optometrist in the Lothian Optometry Treat and Teach clinic. Accuracy of this 'algorithm-assisted' diagnosis was then compared with the 'gold-standard' diagnosis, made by an experienced ophthalmologist. Accuracy of the pre-algorithm diagnosis, made by the referrer, was also compared with the algorithm-assisted diagnosis. RESULTS: All patients referred with visual loss were eligible for inclusion. Seventy patients were assessed; two were excluded. Pre-algorithm accuracy of referral of patients with visual loss was 51% (30/59). Overall, the algorithm-assisted diagnosis was correct 84% (57/68) of the time. The algorithm correctly diagnosed: retina in 71% of cases (5/7), macula in 86% (25/29), peripheral retina in 100% (2/2), optic nerve in 71% (5/7), media opacity in 89% (16/18), post chiasmal in 100% (4/4), and refractive error in 0% (0/1). Accuracy of diagnosis was similar for each algorithm user; medical student 81%, inexperienced ophthalmology trainee 84% and optometrist 92%. DISCUSSION: The baseline diagnostic accuracy of clinicians who are inexperienced in ophthalmology rose from 51 to 84% when patients were assessed using the algorithm. This algorithm significantly improves the diagnostic accuracy of referrals to the hospital eye service, regardless of the user's previous ophthalmic experience. We hope we have demonstrated its potential as a learning tool for inexperienced clinicians.
Authors: Ranjan Duara; David A Loewenstein; Maria Greig; Amarilis Acevedo; Elizabeth Potter; Jason Appel; Ashok Raj; John Schinka; Elizabeth Schofield; Warren Barker; Yougui Wu; Huntington Potter Journal: Am J Geriatr Psychiatry Date: 2010-04 Impact factor: 4.105
Authors: Hillary Rono; Andrew Bastawrous; David Macleod; Emmanuel Wanjala; Stephen Gichuhi; Matthew Burton Journal: Trials Date: 2019-08-14 Impact factor: 2.279