Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning. (arXiv:2309.15867v1 [cs.LG])

Purpose: To identify ocular hypertension (OHT) subtypes with different trends
of visual field (VF) progression based on unsupervised machine learning and to
discover factors associated with fast VF progression. Participants: A total of
3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with
at least five follow-up VF tests were included in the study. Methods: We used a
latent class mixed model (LCMM) to identify OHT subtypes using standard
automated perimetry (SAP) mean deviation (MD) trajectories. We characterized
the subtypes based on demographic, clinical, ocular, and VF factors at the
baseline. We then identified factors driving fast VF progression using
generalized estimating equation (GEE) and justified findings qualitatively and
quantitatively. Results: The LCMM model discovered four clusters (subtypes) of
eyes with different trajectories of MD worsening. The number of eyes in
clusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labelled the
clusters as Improvers, Stables, Slow progressors, and Fast progressors based on
their mean of MD decline, which were 0.08, -0.06, -0.21, and -0.45 dB/year,
respectively. Eyes with fast VF progression had higher baseline age,
intraocular pressure (IOP), pattern standard deviation (PSD) and refractive
error (RE), but lower central corneal thickness (CCT). Fast progression was
associated with calcium channel blockers, being male, heart disease history,
diabetes history, African American race, stroke history, and migraine
headaches.