Analysis of continuous 24-hour intraocular pressure patterns in glaucoma

Abstract

Purpose: To present a method to analyze circadian intraocular pressure (IOP) patterns in glaucoma patients and suspects undergoing repeated continuous 24-hour IOP monitoring.

Methods: Forty patients with established (n = 19) or suspected glaucoma (n = 21) underwent ambulatory 24-hour IOP monitoring on two sessions 1 week apart using a contact lens sensor (CLS). The CLS provides its output in arbitrary units (a.u.). A modified cosinor rhythmometry method was adapted to the CLS output to analyze 24-hour IOP patterns and their reproducibility. Nonparametric tests were used to study differences between sessions 1 and 2 (S1 and S2). Patients pursued their routine daily activities and their sleep was uncontrolled. CLS data were used to assess sleep times.

Results: Complete 24-hour data from both sessions were available for 35 patients. Mean (SD) age of the patients was 55.8 ± 15.5 years. The correlation of the cosinor fitting and measured CLS values was r = 0.38 (Spearman r; P < 0.001) for S1, r = 0.50 (P < 0.001) for S2, whereas the correlation between S1 and S2 cosinor fittings was r = 0.76 (P < 0.001). Repeated nocturnal acrophase was seen in 62.9% of patients; 17.1% of patients had no repeatable acrophase. The average amplitude of the 24-hour curve was 143.6 ± 108.1 a.u. (S1) and 130.8 ± 68.2 a.u. (S2) (P = 0.936).

Conclusions: Adapting the cosinor method to CLS data is a useful way for modeling the rhythmic nature of 24-hour IOP patterns and evaluating their reproducibility. Repeatable nocturnal acrophase was seen in 62.9% of patients. (ClinicalTrials.gov number, NCT01319617.).

Figure 1.

(A) Example of the raw data obtained from 24-hour IOP curves from the same eye during sessions 1 (yellow) and 2 (blue). (B) Cosinor rhythmometry fitting of the same curves for session 1 and session 2. The predictive value of the cosinor fitting on CLS data was r = 0.87 (P < 0.001) and r = 0.90 (P < 0.001) for sessions 1 and 2, respectively.

Figure 1

Continued.

Figure 2

Cosinor rhythmometry modeling of average circadian IOP patterns of the entire study group (n = 35), by session. (A) Session 1 (S1): Bars indicate SEM. The equations' parameters were calculated as: y(t) = 104.11 + 79.90 × cos[(2π/24) × time] + 64.71 × sin[(2π/24) × time]. (B) Session 2 (S2): The equations' parameters were calculated as: y(t) = 82.79 + 74.39 × cos[(2π/24) × time] + 73.71 × sin[(2π/24) × time]. Units on the y-axis correspond to mV. The increased variability at the end of the monitoring is assumed to be due to the increase in manipulation of the globe to remove a tightly fitting CLS in a few patients, which may have produced an artifactual signal rise. Also, the error bars increase because there are fewer data points available because not all patients' sessions lasted exactly the same time.

Figure 3.

Examples of pattern types of 24-hour curves. (A) Nocturnal acrophase. (B) No significant acrophase. (C) Low amplitude. (D) Nonmatching acrophases. There was no case of repeated diurnal acrophase in this series.

Figure 4.

Estimated 24-hour IOP rhythms in the habitual body positions. The clock time of the cosinor rhythmometry-derived acrophase (phase timing) is shown with the amplitude in the radial scale (in arbitrary units [a.u.]). Three individuals' measurements are not included due to high outlier a.u. values. Their acrophases and amplitudes were 0:22, 579 a.u.; 0:43, 427 a.u.; and 22:28, 328 a.u., respectively.