Background: Administrative health claims data have been used for research in neuro-ophthalmology, but the validity of International Classification of Diseases (ICD) codes for identifying neuro-ophthalmic conditions is unclear.
Evidence acquisition: We performed a systematic literature review to assess the validity of administrative claims data for identifying patients with neuro-ophthalmic disorders. Two reviewers independently reviewed all eligible full-length articles and used a standardized abstraction form to identify ICD code-based definitions for 9 neuro-ophthalmic conditions and their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A quality assessment of eligible studies was also performed.
Results: Eleven articles that met criteria for inclusion are as follows: 3 studies of idiopathic intracranial hypertension (PPV 54%-91% and NPV 74%-85%), 2 studies of giant cell arteritis (sensitivity 30%-96% and PPV 94%), 3 studies of optic neuritis (sensitivity 76%-99%, specificity 83%-100%, PPV 25%-100%, and NPV 98%-100%), 1 study of neuromyelitis optica (sensitivity 60%, specificity 100%, PPV 43%-100%, and NPV 98%-100%), 1 study of ocular motor cranial neuropathies (PPV 98%-99%), and 2 studies of myasthenia gravis (sensitivity 53%-97%, specificity 99%-100%, PPV 5%-90%, and NPV 100%). No studies met eligibility criteria for nonarteritic ischemic optic neuropathy, thyroid eye disease, and blepharospasm. Approximately 45.5% provided only one measure of diagnostic accuracy. Complete information about the validation cohorts, inclusion/exclusion criteria, data collection methods, and expertise of those reviewing charts for diagnostic accuracy was missing in 90.9%, 72.7%, 81.8%, and 36.4% of studies, respectively.
Conclusions: Few studies have reported the validity of ICD codes for neuro-ophthalmic conditions. The range of diagnostic accuracy for some disorders and study quality varied widely. This should be taken into consideration when interpreting studies of neuro-ophthalmic conditions using administrative claims data.