Abstract
Purpose
To assess the diagnostic accuracy of the Edinburgh diplopia diagnostic algorithm.
Methods
This was a prospective study. Details of consecutive patients referred to ophthalmology clinics at Falkirk Community Hospital and Princess Alexandra Eye Pavilion, Edinburgh, with double vision were collected by the clinician first seeing the patient and passed to the investigators. The investigators then assessed the patient using the algorithm. An assessment of the degree of concordance between the ‘algorithm assisted’ diagnosis and the ‘gold standard’ diagnosis, made by a consultant ophthalmologist was then carried out. The accuracy of the pre-algorithm diagnosis made by the referrer was also noted.
Results
All patients referred with diplopia were eligible for inclusion. Fifty-one patients were assessed; six were excluded. The pre-algorithm accuracy of referrers was 24% (10/41). The algorithm assisted diagnosis was correct 82% (37/45) of the time. It correctly diagnosed: cranial nerve (CN) III palsy in 6/6, CN IV palsy in 7/8, CN VI palsy in 12/12, internuclear ophthalmoplegia in 4/4, restrictive myopathy in 4/4, media opacity in 1/1, and blurred vision in 3/3. The algorithm assisted diagnosis was wrong in 18% (8/45) of the patients.
Conclusions
The baseline diagnostic accuracy of non-ophthalmologists rose from 24 to 82% when patients were assessed using the algorithm. The improvement in the diagnostic accuracy resulting from the use of the algorithm would, hopefully, result in more accurate triage of patients with diplopia that are referred to the hospital eye service. We hope we have demonstrated its potential as a learning tool for inexperienced clinicians.
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MW has received minimal royalties from the sales of ‘Ophthalmology Pocket Tutor’ where the algorithm has been published. The remaining authors declare no conflict of interest.
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Butler, L., Yap, T. & Wright, M. The accuracy of the Edinburgh diplopia diagnostic algorithm. Eye 30, 812–816 (2016). https://doi.org/10.1038/eye.2016.44
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DOI: https://doi.org/10.1038/eye.2016.44
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