Ophtho Appraise

Early-Onset Primary Angle Closure Disease (EOPACD)

Citation: Gupta, S., Yadav, S.K., Panigrahi, A. et al. Risk factor stratification in early-onset primary angle closure disease. Eye 39, 2604–2611 (2025). https://doi.org/10.1038/s41433-025-03884-1

Background

Primary angle closure glaucoma (PACG) usually affects older adults, but a subset occurs in patients <40 years — early-onset PACD (EOPACD). This group is rare and less understood. Identifying which young patients are at greatest risk of glaucoma is crucial.

Aim

To identify ocular biometric features that help stratify risk of glaucoma in early-onset PACD.

Methods

  • Design: Prospective cross-sectional, tertiary centre (India).
  • Patients: 190 eyes, age 20–40 years.
  • Groups:
    • EOPACS (suspects)
    • EOPAC (primary angle closure)
    • EOPACG (glaucoma with optic nerve/field loss)
  • Measurements: Gonioscopy, UBM, AS-OCT, IOLMaster (biometry).
  • Key parameters: anterior chamber depth (ACD), lens thickness (LT), lens vault (LV), anterior chamber area (ACA), axial length (AL).

Results

  • Glaucoma eyes had:
    • Shallower ACD (≤2.73 mm)
    • Thicker lens (≥4.22 mm)
    • Smaller ACA (≤17.24 mm²)
    • Higher lens vault
  • Axial length was a poor predictor.
  • Mechanisms: mainly plateau and pseudo-plateau iris.

Conclusions

  • Anterior chamber crowding (shallow ACD + thick/anteriorly positioned lens) is the strongest risk factor.
  • Biometric cut-offs may help detect high-risk patients early.
  • Supports the role of lens-based surgical options even in young PACD.

Strengths & Limitations

Strengths: Largest EOPACD series, multimodal imaging, practical cut-offs.
Limitations: Cross-sectional (no progression data), single-centre, potential observer bias.

Clinical Takeaway

👉 In young patients with narrow angles, ACD ≤ 2.7 mm and LT ≥ 4.2 mm should raise strong suspicion for early glaucomatous damage.
👉 Lens surgery may need to be considered earlier, even in younger PACD eyes.

Academic Skill: ROC Curves & Cut-off Selection

This study used logistic regression + ROC analysis to define biometric cut-offs (e.g., ACD ≤2.73 mm).

  • ROC curve = plots sensitivity vs specificity for different thresholds.
  • AUC (area under curve) = measure of diagnostic accuracy (1.0 = perfect, 0.5 = useless).
  • Here, ACD had the best AUC (0.72).
  • Authors chose cut-offs by prioritising sensitivity over specificity → useful in screening (catch more at-risk patients, accept some false positives).

Lesson: Understand how cut-offs are derived — different priorities (sensitivity vs specificity) matter depending on whether you are screening or confirming a disease.