In American journal of ophthalmology ; h5-index 67.0
PURPOSE : Test the hypothesis that visual field (VF) progression can be predicted from baseline and longitudinal optical coherence tomography (OCT) structural measures.
DESIGN : Prospective cohort study.
METHODS : 104 eyes (104 patients) with ≥3 years of follow-up and ≥5 VF exams were enrolled. We defined VF progression based on pointwise linear regression on 24-2 VF (≥3 locations with slope ≤-1.0 dB/year and p<0.01). We used elastic net logistic regression (ENR) and machine learning (ML) to predict VF progression with demographics, baseline circumpapillary retinal nerve fiber layer (RNFL) and macular ganglion cell/inner plexiform layer (GCIPL) thickness and RNFL and GCIPL change rates at central 24 superpixels and 3 eccentricities 3.4°, 5.5° and 6.8° from fovea and hemimaculas. Areas-under-ROC curves (AUC) were used to compare models.
RESULTS : Average (SD) follow-up and VF exams were 4.5 (0.9) years and 8.7 (1.6), respectively. VF progression was detected in 23 eyes (22%). ENR selected rates of change of superotemporal RNFL sector and GCIPL change rates in five central superpixels and at 3.4° and 5.6° eccentricity as best predictor subset (AUC=0.79±0.12). Best ML predictors consisted of baseline superior hemimacular GCIPL thickness and GCIPL change rates at 3.4° eccentricity and three central superpixels (AUC=0.81±0.10). Models using GCIPL-only structural variables performed better than RNFL-only models.
CONCLUSIONS : VF progression can be predicted with clinically relevant accuracy from baseline and longitudinal structural data. Further refinement of proposed models would assist clinicians with timely prediction of functional glaucoma progression and clinical decision-making.
Nouri-Mahdavi Kouros, Mohammadzadeh Vahid, Rabiolo Alessandro, Edalati Kiumars, Caprioli Joseph, Yousefi Siamak
Elastic net, GCIPL, Ganglion cell/inner plexiform layer, Glaucoma, Machine learning, Prediction, Progression, RNFL, Retinal nerve Fiber layer