Machine Learning-Based Prediction of Long-Term Intraocular Pressure Fluctuations in Open-Angle Glaucoma.

Short-term fluctuations correlated only weakly with long-term variability (Pearson r ≤ 0.33) and explained at most 15.2% in regression analysis.

On average, 5563 ± 116 valid pairings from 9.2 ± 0.8 patients were used per configuration.

Five final configurations were selected for each threshold-horizon combination based on the highest F1 values.

All models included 7, 14, and 28-day fluctuations, mean nyctohemeral IOP, ocular pulse amplitude, age, body mass index, and central corneal thickness as predictors, with mean nyctohemeral IOP contributing most (38%-55%).