title: Regression analysis of ranked segment parameters for optic nerve head classification: a pilot study.
abstract: OBJECTIVE
  To determine whether curve-fitting analysis of the ranked segment distributions of topographic optic nerve head (ONH) parameters, derived using the Heidelberg Retina Tomograph (HRT), provide a more effective statistical descriptor to differentiate the normal from the glaucomatous ONH.
  
  
  METHODS
  The sample comprised of 22 normal control subjects (mean age 66.9 years; S.D. 7.8) and 22 glaucoma patients (mean age 72.1 years; S.D. 6.9) confirmed by reproducible visual field defects on the Humphrey Field Analyser. Three 10 degree-images of the ONH were obtained using the HRT. The mean topography image was determined and the HRT software was used to calculate the rim volume, rim area to disc area ratio, normalised rim area to disc area ratio and retinal nerve fibre cross-sectional area for each patient at 10 degree-sectoral intervals. The values were ranked in descending order, and each ranked-segment curve of ordered values was fitted using the least squares method.
  
  
  RESULTS
  There was no difference in disc area between the groups. The group mean cup-disc area ratio was significantly lower in the normal group (0.204 +/- 0.16) compared with the glaucoma group (0.533 +/- 0.083) (p < 0.001). The visual field indices, mean deviation and corrected pattern S.D., were significantly greater (p < 0.001) in the glaucoma group (-9.09 dB +/- 3.3 and 7.91 +/- 3.4, respectively) compared with the normal group (-0.15 dB +/- 0.9 and 0.95 dB +/- 0.8, respectively). Univariate linear regression provided the best overall fit to the ranked segment data. The equation parameters of the regression line manually applied to the normalised rim area-disc area and the rim area-disc area ratio data, correctly classified 100% of normal subjects and glaucoma patients. In this study sample, the regression analysis of ranked segment parameters method was more effective than conventional ranked segment analysis, in which glaucoma patients were misclassified in approximately 50% of cases. Further investigation in larger samples will enable the calculation of confidence intervals for normality. These reference standards will then need to be investigated for an independent sample to fully validate the technique.
  
  
  CONCLUSIONS
  Using a curve-fitting approach to fit ranked segment curves retains information relating to the topographic nature of neural loss. Such methodology appears to overcome some of the deficiencies of conventional ranked segment analysis, and subject to validation in larger scale studies, may potentially be of clinical utility for detecting and monitoring glaucomatous damage.
authors.affiliation: Ophthalmic Research Group, School of Life & Health Sciences, Aston University, Birmingham, UK. r.p.cubbidge@aston.ac.uk
authors.forename: Robert P
authors.initials: RP
authors.lastname: Cubbidge
authors.forename: Sarah L
authors.initials: SL
authors.lastname: Hosking
authors.forename: Emma J
authors.initials: EJ
authors.lastname: Hilton
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Gibson
mesh_terms: D000368:Aged; D003941:Diagnostic Techniques, Ophthalmological; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008875:Middle Aged; D009898:Optic Disk / Q000473:pathology*; D009901:Optic Nerve Diseases / Q000175:diagnosis*; D012044:Regression Analysis; D041622:Tomography, Optical