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Published on: June 20, 2015
Intraocular Pressure Monitoring Using an Implantable Sensor Detects Structural Glaucoma Progression in the EYEMATE-IO
Eleonora Micheletti1, Harsha Rao2, Robert N Weinreb3
1Moorfields Eye Hospital (E.M.), London, UK.
Purpose:
To evaluate the association between intraocular pressure (IOP) measurements and concurrent rates of retinal nerve fiber layer (RNFL) thinning in primary open-angle glaucoma (POAG) eyes previously implanted with a sulcus-based IOP sensor.
Design:
Prospective.
Methods:
In this case series, part of the prospective, open-label, multicenter interventional EYEMATE-IO trial, patients implanted with the EYEMATE-IO sensor system (Implandata) were enrolled in the 3-year ARGOS-03 follow-up study. All patients enrolled had at least 5 optical coherence tomography (OCT) examinations 6 months apart, with a minimum 2-year follow-up. A minimum of 4 IOP measurements daily at different times of the day were obtained with the EYEMATE-IO. Mean IOP, peak IOP, and fluctuation of IOP measured by EYEMATE-IO sensor during the period between 2 consecutive OCT examinations were calculated, and the relationship with OCT RNFL thinning was analyzed using mixed-effects models. The relationship of mean IOP measured by Goldmann applanation tonometry (GAT) on the day of the OCT examination with RNFL thinning was also analyzed.
Results:
Eight eyes of 8 patients with the EYEMATE-IO sensor were included in the analysis. The mean number of self-measurements of IOP per patient was 7283 ± 5562 (range 1478-17247), with a mean follow-up time of 2.88 ± 0.19 years (range 2.43-3.01). The mean number of OCT examinations per patient was 6.38 ± 0.74 (range 5-7). Overall, the mean rate of RNFL thinning during the follow-up was -0.62 ± 1.06 μm/y (P = .274). In the linear mixed-effects model analysis, both peak IOP and IOP fluctuations measured using the EYEMATE-IO sensor were significantly associated with RNFL thinning (coefficient [95% CI]: -0.11 [-0.19; -0.34], P = .005, and -0.76 [-1.31; -0.20], P = .007, respectively), whereas no association was found for in-office mean IOP measured by GAT (95% CI: [-0.12; 0.20], P = .616).
Conclusions:
Peak IOP and IOP fluctuations in glaucoma patients derived from measurements with the EYEMATE-IO sensor were associated with progression of the disease, whereas GAT measurements were not. These findings suggest that self-measurements of IOP throughout the day with an implantable IOP sensor can predict glaucoma progression.
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