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Editors Selection IGR 20-1
Progression: Combination of risk factors and SAP data
Comment by Murray Fingeret on:
49196 Incorporating risk factors to improve the assessment of rates of glaucomatous progression, Medeiros FA; Zangwill LM; Mansouri K et al., Investigative Ophthalmology and Visual Science, 2012; 53: 2199-2207
Visual fields are the most common method used to monitor individuals with glaucoma for progression. Fields are performed over time with the clinician deciding if change has occurred, and if so, what the rate of change is. Analyzing progressing fields for the rate of change (trend analysis) is relatively new, with a regression analysis program commonly used. Measurement variability or noise must be considered as the clinician decides whether the results are real and glaucoma has gotten worse. This is an important decision as it is usually associated with an advancement of therapy, and one the clinician struggles with as he/she decides whether the results are credible. Often additional fields are performed for confirmation, delaying the decision making. The doctor will also incorporate other information as they decide whether to believe the results, such as which eye has the higher intraocular pressure (IOP), how high the IOP is, which optic nerve has greater damage, has the optic nerve(s) changed recently, etc. The authors address the question of analyzing trend analysis, investigating whether there is a more accurate method to assess the rate of change and how to incorporate clinical information into a statistical model to assess fields for change.
In this study by Medeiros et al., the authors use two methods to assess the rate of change: the commonly used regression equation as well as a Bayesian regression model that incorporated additional information such as risk factors and optic disc damage. The dataset analyzed was a long-term ongoing study; the Diagnostic Innovations in Glaucoma Study (DIGS) in which fields and other tests are done on a preset, ongoing basis. The risk factors found to be associated with progression were higher IOP, thinner corneas, and presence of disc hemorrhages. Age was not found to be a factor. The Bayesian slope measurement in which prior information was included was found to be more accurate in measuring change than the regression method. The implications for these results are significant as software may be made commercially available using the Bayesian model that allows one to include clinical data such as IOP, corneal thickness and optic nerve status. The clinician may then have a better idea of who is changing. When a limited number of fields are available, one can still address whether progression has occurred by relying on other clinical measurements.
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