MODELING SERIES OF VISUAL-FIELDS TO DETECT PROGRESSION IN NORMAL-TENSION GLAUCOMA

Background: Use of statistical modelling techniques to identify models that both describe glaucomatous sensitivity decay and allow predictio ns of future field status. Method: Twelve initially normal fellow eyes of untreated patients with confirmed normal-tension glaucoma were stu died. All had in excess of 15 Humphrey fields (mean follow-up 5.7 year s). From this cohort individual field locations were selected for anal ysis if they demonstrated unequivocal deterioration at the final two f ields. Forty-seven locations from five eyes satisfied this criterion a nd were analysed using curve-fitting software which automatically appl ies 221 different models to sensitivity (y) against time of follow up (x). Curve-fitting was then repeated on the first five fields, followe d by projection to the date of the final field to generate a predicted threshold which was compared to the actual threshold. Competing model s were therefore assessed on their performance at adequately fitting t he data (R(2)) and their potential to predict future field status. Res ults: Models that provided the best fit to the data were all complex p olynomial expressions (median R(2) 0.93). Other simple expressions fit ted fewer locations and exhibited lower R(2) values. However, accuracy in predicting future deterioration was superior with these less compl ex models. In this group a linear expression demonstrated an adequate fit to the majority of the data and generated the most accurate predic tions of future field status. Conclusions: A linear model of the point wise sensitivity values against time of follow-up can provide a framew ork for detecting and forecasting glaucomatous field progression. Line ar modelling allows the clinically important rate of sensitivity loss to be estimated.