Combining Structural and Functional Measurements to Improve Estimates of Rates of Glaucomatous Progression

详细信息	查看全文 | 推荐本文 |

• 作者：Felipe A. Medeiros^a ; ^{fmedeiros@glaucoma.ucsd.edu}  ; [Author Vitae] ; Linda M. Zangwill^a ; Christopher A. Girkin^b ; Jeffrey M. Liebmann^c ; Robert N. Weinreb^a
• 刊名：American Journal of Ophthalmology
• 出版年：2012
• 期刊代码：7_00029394
• 类别：med
• 出版时间：June, 2012
• 卷：153
• 期：6
• 页码：1197-1205.e1
• 文件大小：1369 K

摘要

| Figures/TablesFigures/Tables | ReferencesReferences

Purpose

To evaluate whether a combination of topographic optic disc measurements and visual field data may improve the estimation of rates of structural and functional progression in glaucoma and the prediction of future outcomes in the disease.

Design

Observational cohort study.

Methods

The study included 242 eyes of 179 glaucoma patients followed for an average of 6.4 卤 1.1 years. Subjects were longitudinally monitored with standard automated perimetry (SAP) and confocal scanning laser ophthalmoscopy (CSLO). Slopes of functional and structural change over time were evaluated by the parameters mean deviation (MD) and rim area, respectively. For each eye, the follow-up time was divided into 2 equal periods: the first half was used to obtain the slopes of change and the second period was used to test the predictions. Slopes of change were calculated using 2 methods, the conventional approach of ordinary least squares linear regression and a Bayesian joint regression model integrating structural and functional information. The mean square error (MSE) of the predictions was used to compare the predictive performance of the different methods.

Results

Bayesian slopes were more accurate than those obtained by the ordinary least squares method in predicting future MD (MSE: 5.13 vs 11.2, respectively; P < .001) and rim area values (MSE: 0.016 vs 0.027, respectively; P < .01).

Conclusion

A Bayesian joint regression model combining structure and function resulted in more accurate and precise estimates of slopes of change compared to the conventional method of ordinary least squares linear regression.