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Michael V. Boland, Allison N. McCoy, Harry A. Quigley, Neil R. Miller, Prem S. Subramanian, Pradeep Y. Ramulu, Peter Murakami, Helen V. Danesh-Meyer; Evaluation of an Algorithm for Detecting Visual Field Defects Due to Chiasmal and Postchiasmal Lesions: The Neurological Hemifield Test. Invest. Ophthalmol. Vis. Sci. 2011;52(11):7959-7965. doi: 10.1167/iovs.11-7868.
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To develop an automated neurologic hemifield test (NHT) to detect visual field loss caused by chiasmal or postchiasmal lesions.
Visual field locations from 24-2 pattern automated visual fields were grouped into two symmetric regions with 16 points on either side of the vertical meridian. A scoring system similar to the Glaucoma Hemifield Test (GHT) was used to calculate point scores using the pattern deviation values from the right and left regions. The cross-vertical difference in the sum of these values was the NHT score. The NHT was evaluated using visual fields from subjects with known neurologic disease, subjects with glaucoma, and glaucoma suspects (92 pairs of eyes each). The NHT score was calculated for each eye. Four masked reviewers scored all pairs of visual fields with regard to the likelihood of neurologic and glaucomatous optic neuropathy. Both NHT score and expert field ratings were compared with clinical diagnosis by receiver operating characteristic (ROC) analysis.
The NHT effectively discriminated neurologic fields from those of glaucoma patients and glaucoma suspects (area under the ROC curve [AUC] = 0.90; 95% confidence interval [CI], 0.86–0.94). The NHT score correlated well with clinician grading (Pearson correlation estimates, 0.74–0.78). Even when field defects were subtle, the NHT had some ability to discriminate neurologic from nonneurologic fields (AUC 0.68; 95% CI, 0.56–0.79).
The NHT distinguished neurologic field defects from those of glaucoma and glaucoma suspects, rivaling the performance of subspecialist clinicians. Its implementation may help identify unsuspected neurologic disease.
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