Intraocular Pressure Monitoring Using An Implantable Sensor Detects Structural Glaucoma Progression in the EYEMATE-IO Trial.

Abstract

OBJECTIVE AND 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 This case series is part of the prospective, open-label, multicenter interventional EYEMATE-SC trial. SUBJECTS, PARTICIPANTS, AND/OR CONTROLS Patients implanted with the EYEMATE-IO sensor system (Implandata, Hanover, Germany) were enrolled in the 3 years' ARGOS-03 follow-up study. METHODS, INTERVENTION, OR TESTING All patients enrolled had at least five optical coherence tomography (OCT) examinations six-months apart with minimum two years follow-up. A minimum of 4 IOP measurements daily at different times of the day were obtained with the EYEMATE-IO. MAIN OUTCOME MEASURES Mean IOP, peak IOP, and fluctuation of IOP measured by EYEMATE-IO sensor during the period between two 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 OCT visit 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 to 17247), with a mean follow-up time of 2.88 ± 0.19 years (range 2.43 to 3.01). The mean number of OCT examinations per patient was 6.38 ± 0.74 (range 5 to 7). Overall, the mean rate of RNFL thinning during the follow-up was -0.62 ± 1.06 um/year (P=0.274). 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=0.005 and -0.76 [-1.31;-0.20], P=0.007, respectively), whereas no association was found for in-office mean IOP measured by GAT (coefficient 95%CI]: 0.04 [-0.12;0.20], P=0.616). 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.