TY - JOUR
T1 - Development and validation of an associative model for the detection of glaucoma using pupillography
AU - Chang, Dolly S.
AU - Arora, Karun S.
AU - Boland, Michael V.
AU - Supakontanasan, Wasu
AU - Friedman, David S.
N1 - Funding Information:
All authors have completed and submitted the ICMJE form for disclosure of potential conflicts of interest, and the following were reported. Dr Chang received a training grant ( 2T32AG000247-16 ) from the National Institute on Aging . Dr Friedman received the RAPiD pupillometry device from Konan Medical, Inc, on loan, and is a consultant to Alcon, Allergan, Bausch & Lomb, Merck, Pfizer, QLT, and Epidemiology International. Design and conduct of study (D.C., K.A., M.B., W.S., D.F.); Collection and management of data (D.C., K.A., W.S.); Management of data (D.C., M.B., D.F.); Analysis of data (D.C.); Interpretation of data (D.C., M.B., D.F.); Preparation and review of manuscript (D.C., K.A., M.B., D.F.); and Approval of manuscript (D.C., K.A., M.B., W.S., D.F.). Each of the coauthors has seen and agrees with each of the changes made to this manuscript in the revision and with the way his or her name is listed.
PY - 2013/12
Y1 - 2013/12
N2 - Purpose To develop and validate an associative model using pupillography that best discriminates those with and without glaucoma. Design A prospective case-control study. Methods We enrolled 148 patients with glaucoma (mean age 67 ± 11) and 71 controls (mean age 60 ± 10) in a clinical setting. This prototype pupillometer is designed to record and analyze pupillary responses at multiple, controlled stimulus intensities while using varied stimulus patterns and colors. We evaluated three approaches: (1) comparing the responses between the two eyes; (2) comparing responses to stimuli between the superonasal and inferonasal fields within each eye; and (3) calculating the absolute pupil response of each individual eye. Associative models were developed using stepwise regression or forward selection with Akaike information criterion and validated by fivefold cross-validation. We assessed the associative model using sensitivity, specificity and the area-under-the-receiver operating characteristic curve. Results Persons with glaucoma had more asymmetric pupil responses in the two eyes (P < 0.001); between superonasal and inferonasal visual field within the same eye (P = 0.014); and smaller amplitudes, slower velocities and longer latencies of pupil responses compared to controls (all P < 0.001). A model including age and these three components resulted in an area-under-the-receiver operating characteristic curve of 0.87 (95% CI 0.83 to 0.92) with 80% sensitivity and specificity in detecting glaucoma. This result remained robust after cross-validation. Conclusions Using pupillography, we were able to discriminate among persons with glaucoma and those with normal eye examinations. With refinement, pupil testing may provide a simple approach for glaucoma screening.
AB - Purpose To develop and validate an associative model using pupillography that best discriminates those with and without glaucoma. Design A prospective case-control study. Methods We enrolled 148 patients with glaucoma (mean age 67 ± 11) and 71 controls (mean age 60 ± 10) in a clinical setting. This prototype pupillometer is designed to record and analyze pupillary responses at multiple, controlled stimulus intensities while using varied stimulus patterns and colors. We evaluated three approaches: (1) comparing the responses between the two eyes; (2) comparing responses to stimuli between the superonasal and inferonasal fields within each eye; and (3) calculating the absolute pupil response of each individual eye. Associative models were developed using stepwise regression or forward selection with Akaike information criterion and validated by fivefold cross-validation. We assessed the associative model using sensitivity, specificity and the area-under-the-receiver operating characteristic curve. Results Persons with glaucoma had more asymmetric pupil responses in the two eyes (P < 0.001); between superonasal and inferonasal visual field within the same eye (P = 0.014); and smaller amplitudes, slower velocities and longer latencies of pupil responses compared to controls (all P < 0.001). A model including age and these three components resulted in an area-under-the-receiver operating characteristic curve of 0.87 (95% CI 0.83 to 0.92) with 80% sensitivity and specificity in detecting glaucoma. This result remained robust after cross-validation. Conclusions Using pupillography, we were able to discriminate among persons with glaucoma and those with normal eye examinations. With refinement, pupil testing may provide a simple approach for glaucoma screening.
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U2 - 10.1016/j.ajo.2013.07.026
DO - 10.1016/j.ajo.2013.07.026
M3 - Article
C2 - 24011523
AN - SCOPUS:84887883389
VL - 156
SP - 1285-1296.e2
JO - American Journal of Ophthalmology
JF - American Journal of Ophthalmology
SN - 0002-9394
IS - 6
ER -