Elsevier

Ophthalmology

Volume 121, Issue 8, August 2014, Pages 1539-1546
Ophthalmology

Original article
Using Filtered Forecasting Techniques to Determine Personalized Monitoring Schedules for Patients with Open-Angle Glaucoma

Presented in part at: the American Glaucoma Society Annual Meeting, March 2, 2013, San Francisco, California; and the Association for Research in Vision and Ophthalmology, May 7, 2013, Seattle, Washington.
https://doi.org/10.1016/j.ophtha.2014.02.021Get rights and content

Purpose

To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG).

Design

Secondary analyses using longitudinal data from 2 randomized controlled trials.

Participants

A total of 571 participants from the Advanced Glaucoma Intervention Study (AGIS) and the Collaborative Initial Glaucoma Treatment Study (CIGTS).

Methods

Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participant's disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participant's disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared with 1-, 1.5-, and 2-year fixed interval schedules of obtaining VF and IOP measurements.

Main Outcome Measures

Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, and number of VF and IOP measurements needed to assess for progression.

Results

Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (P<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (P = 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well for patients with mild and advanced disease. The model performed significantly more testing of patients who exhibited OAG progression than nonprogressing patients (1.3 vs. 1.0 tests per year; P<0.0001).

Conclusions

Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management.

Section snippets

Data Sources

Data from 2 large, multicenter, randomized, controlled clinical trials, the Collaborative Initial Glaucoma Treatment Study (CIGTS) and Advanced Glaucoma Intervention Study (AGIS), were used for parameterization and validation of a Kalman filter and scheduling algorithm. These clinical trials were chosen because they included multiple measurements of IOP (by Goldmann applanation tonometry) and VF results (using a Humphrey Field Analyzer; Carl Zeiss Meditec, Dublin, CA) for patients with mild to

Results

A total of 571 participants (571 eyes) with OAG met the study inclusion criteria. Table 1 presents a summary of the participants. Of these, 266 (47%) came from CIGTS and 305 (53%) came from AGIS. The mean (standard deviation) age of the study participants at baseline was 63.2 (10.9) years. The participants included 272 male subjects (48%) and 299 female subjects (52%). There were 263 whites (46%) and 288 blacks (50%), and 20 were classified as some other race. Participants were followed in the

Discussion

By using a forecasting technique called Kalman filtering, we parameterized an algorithm that dynamically updates the timing of future measurements for each individual on the basis of prior measurements. The Kalman filter starts with information about the population, and as patient observations are obtained, the Kalman filter incorporates these data to learn about each individual's specific progression dynamics. Our algorithm was validated using longitudinal data from 2 large, multicenter

Acknowledgments

The authors thank Leslie Niziol, MS, for formatting the data from CIGTS; Paul Van Veldhuisen, PhD, for providing the data from AGIS; and the following undergraduate students for assistance in coding and analysis of clinical trial data: Jade Watts, Amanda Bayagich, Xiang Li, and Sam Devaprasad.

References (13)

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Supplemental material is available at www.aaojournal.org.

Financial Disclosure(s): The author(s) have made the following disclosure(s): The authors of this work have a Provisionary Patent 010109-11001P filed with the U.S. Office of Patents, which covers intellectual property described in this manuscript.

Partial funding for this work has been received from National Institutes of Health Clinical and Translational Science Award Grant UL 1RR024986 and National Science Foundation Grant CMMI-1161439. The funding organizations had no role in the design or conduct of this research.

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