Handheld chromatic pupillometry can accurately and rapidly reveal functional loss in glaucoma

Abstract

Background/aims: Early detection and treatment of glaucoma can delay vision loss. In this study, we evaluate the performance of handheld chromatic pupillometry (HCP) for the objective and rapid detection of functional loss in glaucoma.

Methods: In this clinic-based, prospective study, we enrolled 149 patients (median (IQR) years: 68.5 (13.6) years) with confirmed glaucoma and 173 healthy controls (55.2 (26.7) years). Changes in pupil size in response to 9 s of exponentially increasing blue (469 nm) and red (640 nm) light-stimuli were assessed monocularly using a custom-built handheld pupillometer. Pupillometric features were extracted from individual traces and compared between groups. Features with the highest classification potential, selected using a gradient boosting machine technique, were incorporated into a generalised linear model for glaucoma classification. Receiver operating characteristic curve analyses (ROC) were used to compare the performance of HCP, optical coherence tomography (OCT) and Humphrey Visual Field (HVF).

Results: Pupillary light responses were altered in glaucoma compared with controls. For glaucoma classification, HCP yielded an area under the ROC curve (AUC) of 0.94 (95% CI 0.91 to 0.96), a sensitivity of 87.9% and specificity of 88.4%. The classification performance of HCP in early-moderate glaucoma (visual field mean deviation (VFMD) > -12 dB; AUC=0.91 (95% CI 0.87 to 0.95)) was similar to HVF (AUC=0.91) and reduced compared with OCT (AUC=0.97; p=0.01). For severe glaucoma (VFMD ≤ -12 dB), HCP had an excellent classification performance (AUC=0.98, 95% CI 0.97 to 1) that was similar to HVF and OCT.

Conclusion: HCP allows for an accurate, objective and rapid detection of functional loss in glaucomatous eyes of different severities.

Keywords: glaucoma; optic nerve; pupil.

Figure 1

Diagram showing the flow of enrolment and exclusion of patients and data. A total of 470 participants including 207 controls and 263 patients were screened for this study. Out of the control group, 30 participants did not meet the study’s inclusion and exclusion criteria and the pupillometry data collected from four control participants were unreliable (n=2) or missing (n=2). Out of 263 patients screened at the Singapore National Eye Centre (SNEC) glaucoma clinics, 9 declined to participate and 103 did not meet the study’s inclusion and exclusion criteria. The pupillometry data collected from two glaucoma patients were missing or unreliable. This study included the data from 173 controls and 149 patients with confirmed unilateral or bilateral glaucoma.

Figure 2

Average baseline-adjusted pupillary responses to the 1 min light paradigm in patients with glaucoma and controls. (A) After onset, light stimuli intensity increased exponentially from 11.7 to 14.4 log photons/cm²/s for blue light and from 11.9 to 14.3 log photons/cm²/s for red light. The duration of both blue and red light exposures was 9 s. Blue light stimulus onset was preceded by 10 s of darkness to quantify baseline pupil size and followed by 22 s of darkness to assess pupillary redilation prior to red light onset. Red light was followed by 10 s of darkness to assess the pupil’s redilation process. (B) Baseline-adjusted pupil constriction increased (pupil size decreased) rapidly at light onset and progressively as a function of the gradually increasing light exposure. In comparison to controls, patients with glaucoma exhibited significant alterations in the pupillary light responses. Data are plotted as average ± SE. (C–F) Dot plots representing differences in main pupillometric features in response to blue (C, D, E) and red (F) lights in patients with glaucoma compared with controls. The median of each group is represented as a dashed grey line. Statistical comparisons between groups were performed using a Mann-Whitney U test. ***p<0.001. AUC, area under the curve; PIPR, post-illumination pupillary response.

Figure 3

Receiver operating characteristic (ROC) curves reflecting the classification performance of HCP, OCT and HVF. (A) Receiver operating characteristic curve of HCP in 173 controls and 149 patients with glaucoma. The AUC of HCP was 0.94 (95% CI 0.91 to 0.96). (B) ROC curves of HCP, HVF and OCT in a similar population of 172 controls and 132 glaucoma patients. The AUC of HCP for the classification of glaucoma was 0.93 (0.90–0.96) and was similar to that of HVF (0.94 (0.92–0.97); p=0.56) but reduced compared with OCT (0.98 (0.96–0.99); p=0.005). The AUC of HVF was significantly lower than OCT (p=0.02). AUC, area under the curve; HCP, handheld chromatic pupillometry; HVF, Humphrey Visual Field; OCT, optical coherence tomography.