Primary open angle glaucoma (POAG) is a chronic progressive optic neuropathy and potentially blinding disease. It thus places a social and economic burden on patients, their families, as well as the general society. Glaucoma damage is irreversible because nothing yet can restore the optic nerve cells once they are dead. Current strategies for glaucoma treatment are often aimed to treat all patients rather aggressively, based on the assumption that all patients will get worse over time and eventually the disease will impact each patient. However, rates of visual field (VF) progression can vary substantially from patient to patient and it is crucial to identify these patients whose VFs deteriorate rapidly. Early identification of patients with VF progression would allow for prompt intensification of treatment to prevent further damage. Conversely, for patients whose visual fields remain stable the use of limited healthcare resources could be reduced and the morbidity associated with over-treatment could be avoided. The goal of this proposal is to develop a surveillance model to estimate the risk of VF progression in patients with early glaucoma. The model will be based on standard clinical measures and easily accessible to clinicians and patients. Our hypothesis is that a model incorporating both VF data and clinical factors would have a better performance in early identification of VF progression than the current surveillance focusing on VF data alone. This application is greatly strengthened by the quality and completeness of the analysis sample. We will use the cohort of 279 participants (362 eyes) who developed POAG in the Ocular Hypertension Treatment Study. It contains high-quality bi-annual visual field (VF) test results with a median follow-up of thirteen years. This is the largest inception cohort. All patients were followed prospectively according to a standardized protocol prior to and after POAG diagnosis. Case definition was standardized, masked and cause-specific. Each eye has a time zero representing the date of POAG diagnosis. Time zero allows us to examine factors before and after POAG ascertainment, as well as time to progression from the ascertainment date. The surveillance model will be cross-validated in a sub-sample of 234 patients in the Collaborative Initial Glaucoma Treatment Study (CIGTS) who had point-wise VF thresholds recorded electronically. We will put the surveillance model on the same, highly patronized web site as the OHTS prediction model for the development of POAG. This proposal represents an important step towards personalizing glaucoma care. Our long-term objective is to assist a personalized management procedure through an accurate estimating of patients'individualized risk of glaucoma progression, so that clinicians and patients can make individualized, evidence- based decisions as to the frequency of monitoring and need for aggressive treatment.

Public Health Relevance

We propose a surveillance model to estimate the risk of disease progression in patients with early primary open angle glaucoma. This represents an important step towards personalizing glaucoma care. The availability of an accurate individualized risk of progression would allow clinicians and patients to make evidence-based decisions as to the frequency of monitoring as well as the need for aggressive treatment.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21EY023452-02
Application #
8629751
Study Section
Special Emphasis Panel (ZEY1-VSN (04))
Program Officer
Everett, Donald F
Project Start
2013-04-01
Project End
2015-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
2
Fiscal Year
2014
Total Cost
$191,540
Indirect Cost
$65,527
Name
Washington University
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Gao, Feng; Philip Miller, J; Xiong, Chengjie et al. (2017) Estimating correlation between multivariate longitudinal data in the presence of heterogeneity. BMC Med Res Methodol 17:124