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The accuracy of the Edinburgh diplopia diagnostic algorithm.

L Butler1, T Yap1, M Wright1

  • 1Princess Alexandra Eye Pavilion, Edinburgh, UK.

Eye (London, England)
|March 19, 2016
PubMed
Summary

The Edinburgh diplopia diagnostic algorithm significantly improved diagnostic accuracy for double vision cases, from 24% to 82%. This tool aids non-specialists in correctly identifying causes of diplopia.

Area of Science:

  • Ophthalmology
  • Diagnostic Medicine
  • Clinical Algorithms

Background:

  • Diplopia (double vision) diagnosis by non-specialists has historically low accuracy.
  • Accurate and timely diagnosis of diplopia is crucial for appropriate patient triage and management.
  • The Edinburgh diplopia diagnostic algorithm was developed to aid clinicians in diagnosing double vision.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of the Edinburgh diplopia diagnostic algorithm.
  • To compare the algorithm-assisted diagnosis with a gold standard diagnosis by a consultant ophthalmologist.
  • To assess the impact of the algorithm on the diagnostic accuracy of non-ophthalmologists.

Main Methods:

  • Prospective study involving consecutive patients referred with diplopia.

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  • Data collection by the initial clinician and subsequent assessment using the Edinburgh algorithm.
  • Comparison of algorithm-assisted diagnoses against a consultant ophthalmologist's diagnosis.

    • Main Results:

    • Pre-algorithm diagnostic accuracy by referrers was 24%.
    • The algorithm-assisted diagnosis achieved 82% accuracy.
    • The algorithm demonstrated high accuracy in diagnosing specific conditions like cranial nerve palsies and restrictive myopathies.

    Conclusions:

    • The Edinburgh diplopia diagnostic algorithm substantially increases diagnostic accuracy for double vision.
    • The algorithm shows potential as an effective learning tool for inexperienced clinicians.
    • Improved diagnostic accuracy can lead to more efficient triage of patients with diplopia within hospital eye services.