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Article Abstract
Objective: To compare the 24-hour intraocular pressure (IOP) fluctuation slope curve between newly diagnosed patients with ocular hypertension (OHT) and primary open-angle glaucoma (POAG).
Methods And Analysis: Newly diagnosed and untreated OHT and POAG patients who underwent 24-hour IOP monitoring were consecutively enrolled in the study. IOP measurements were taken every 2 hours from 8:00 to 6:00 hours the following day using an iCare PRO tonometer. Patients maintained their daily routines, with IOP measured in a seated position during the day and supine at night. The 24-hour IOP fluctuation indices, including peak, trough and overall fluctuation, were calculated. Differences in the 24-hour IOP fluctuation slope curves over time between groups were analysed using a generalised additive mixed model.
Results: 46 patients with OHT and 41 with POAG were included. From 2:00 to 10:00 hours, mean IOP increased by 0.69 mm Hg every 2 hours in the POAG group (p<0.0001) and by 0.40 mm Hg in the OHT group (p<0.0001). After 10:00, the IOP showed a downward trend, decreasing by 0.31 mm Hg in the POAG group (p<0.0001) and by 0.17 mm Hg in the OHT group (p=0.0003) every 2 hours. The rate of slope change in the upward phase differed significantly between the groups (0.30 mm Hg per 2 hours; p=0.02), as did the rate in the downward phase (0.14 mm Hg per 2 hours; p for interaction=0.04). Multivariate models showed that each 1 mm Hg increase in circadian and diurnal IOP fluctuation was associated with a 27% and 21% higher likelihood of POAG presence, respectively.
Conclusion: The 24-hour IOP slope curve differed between POAG and OHT, with a steeper slope observed in the POAG group. However, the study is limited by potential confounding factors, reliance on a single 24-hour measurement period and the need for further longitudinal studies to validate these findings.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418482 | PMC |
| http://dx.doi.org/10.1136/bmjophth-2024-001821 | DOI Listing |
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Article Synopsis
- - The study aims to predict 24-hour intraocular pressure (IOP) variations to improve glaucoma management, using machine learning techniques based on daytime IOP measurements instead of traditional methods.
- - Researchers collected IOP data and performed feature selection to classify fluctuations as either more than or equal to 8 mmHg. Five machine learning algorithms were tested, focusing on metrics like accuracy, specificity, and precision for evaluation.
- - The XGBoost algorithm showed the best performance with an accuracy of 88.6% and a notable AUC of 0.890, highlighting its potential for predicting IOP fluctuations and encouraging further research in clinical settings.