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Purpose <p>To investigate the association between vascular factors affecting optic nerve perfusion and glaucoma progression in a clinical setting with systematic monitoring of blood pressure and intraocular pressure (IOP).</p> Study design <p>Retrospective cohort study.</p> Methods <p>This retrospective study analyzed 428 primary open-angle glaucoma (POAG; n=295) and exfoliation glaucoma (EXG; n=133) patients from Shimane University Hospital. We employed a validated Bidirectional Encoder Representations from Transformers (BERT)-based natural language processing system to extract clinical data from electronic medical records of 36,561 visits, including blood pressure measurements, IOP, and medication details across 216 clinical parameters. Mixed-effects regression models were employed to identify factors associated with visual field mean deviation (MD) and the rate of MD decline.</p> Results <p>The cohort demonstrated consistent pressure management (median IOP: 13.0 mmHg; median systolic: 138.0 mmHg; median diastolic: 79.0 mmHg). Ocular perfusion pressure (OPP) showed no significant association with either visual field MD or the rate of MD decline. Significant predictors of worse visual field MD included age (coefficient: −0.28, 95% CI −0.33 to −0.23, p&lt;0.001), history of filtration surgery (coefficient: −3.8, 95% CI −5.7 to −1.9, p&lt;0.001), and number of glaucoma medications (coefficient: −0.19, 95% CI −0.35 to −0.030, p=0.020). Factors significantly associated with faster MD decline were age, initial MD severity, and medication burden.</p> Conclusions <p>In a clinical setting with systematic monitoring, OPP provided limited prognostic value for glaucoma progression, while traditional clinical factors were stronger predictors. Our BERT-based approach demonstrates how artificial intelligence (AI)-powered data extraction enables comprehensive analyses of clinical records, offering new insights for routine ophthalmological care.</p>

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Role of perfusion-related metrics in visual field progression of primary open-angle and exfoliation glaucoma: a natural language processing approach

  • 期刊论文

  • 发表日期：

    2025年9月29日

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  • Ayumu Akagi,
  • Kaito Narimoto,
  • Kanta Ueda,
  • Noriko Koizumi,
  • Naoki Okumura,
  • Masaki Tanito

摘要

Purpose

To investigate the association between vascular factors affecting optic nerve perfusion and glaucoma progression in a clinical setting with systematic monitoring of blood pressure and intraocular pressure (IOP).

Study design

Retrospective cohort study.

Methods

This retrospective study analyzed 428 primary open-angle glaucoma (POAG; n=295) and exfoliation glaucoma (EXG; n=133) patients from Shimane University Hospital. We employed a validated Bidirectional Encoder Representations from Transformers (BERT)-based natural language processing system to extract clinical data from electronic medical records of 36,561 visits, including blood pressure measurements, IOP, and medication details across 216 clinical parameters. Mixed-effects regression models were employed to identify factors associated with visual field mean deviation (MD) and the rate of MD decline.

Results

The cohort demonstrated consistent pressure management (median IOP: 13.0 mmHg; median systolic: 138.0 mmHg; median diastolic: 79.0 mmHg). Ocular perfusion pressure (OPP) showed no significant association with either visual field MD or the rate of MD decline. Significant predictors of worse visual field MD included age (coefficient: −0.28, 95% CI −0.33 to −0.23, p<0.001), history of filtration surgery (coefficient: −3.8, 95% CI −5.7 to −1.9, p<0.001), and number of glaucoma medications (coefficient: −0.19, 95% CI −0.35 to −0.030, p=0.020). Factors significantly associated with faster MD decline were age, initial MD severity, and medication burden.

Conclusions

In a clinical setting with systematic monitoring, OPP provided limited prognostic value for glaucoma progression, while traditional clinical factors were stronger predictors. Our BERT-based approach demonstrates how artificial intelligence (AI)-powered data extraction enables comprehensive analyses of clinical records, offering new insights for routine ophthalmological care.