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PURPOSE: This study was designed to determine and compare the relationships between visual function measured with standard automated perimetry (SAP) and structure, either as neuroretinal rim area measured with confocal scanning laser ophthalmoscopy (CSLO), or as retinal nerve fiber layer thickness determined by scanning laser polarimetry with variable corneal compensation (SLP-VCC). METHODS: Forty-six healthy subjects and 76 glaucoma patients were examined with SAP, with CSLO by means of the commercially available Heidelberg Retina Tomograph I (HRT), and with SLP-VCC by means of the commercially available GDx VCC. The relationships between SAP, expressed either in the typically used decibel scale or as number of abnormal points in the total deviation probability plot, and CSLO and between SAP and SLP-VCC were described with linear and logarithmic regression analysis for global data and six individual sectors. The relationship between measurements with CSLO and SLP-VCC was fit with linear regression analysis. RESULTS: The relationships between SAP and CSLO and between SAP and SLP-VCC appeared curvilinear for all sectors except the temporal one between SAP and SLP-VCC. For CSLO, a logarithmic fit was significantly better than a linear one for the global data and in the superotemporal and inferonasal sectors. For SLP-VCC, a curvilinear fit was better for the global data and in the superotemporal, superonasal, and inferonasal sectors. CSLO data correlated linearly with SLP-VCC data in all sectors, except temporally. CONCLUSIONS: CSLO and SLP-VCC showed a very similar curvilinear relationship with SAP. The observed curvilinear relationships confirm earlier reports that these imaging devices appear to detect glaucomatous loss earlier than SAP.
Dr. N.J. Reus, Glaucoma Service, The Rotterdam Eye Hospital, Rotterdam, The Netherlands. reus@eyehospitals.nl
6.6.2 Automated (Part of: 6 Clinical examination methods > 6.6 Visual field examination and other visual function tests)
6.9.1 Laser scanning (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)