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Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data.

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Summary

A new prognostic model accurately predicts keratoconus progression to corneal crosslinking (CXL). Age at presentation is the most significant factor, not genetic markers, aiding patient management.

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Area of Science:

  • Ophthalmology
  • Genetics
  • Biostatistics

Background:

  • Keratoconus is a progressive corneal disease affecting vision.
  • Predicting keratoconus progression is crucial for timely intervention with corneal crosslinking (CXL).

Purpose of the Study:

  • To develop and validate a prognostic model for predicting keratoconus progression.
  • To identify key predictors of progression that may necessitate CXL.

Main Methods:

  • Retrospective cohort study of 5025 early keratoconus patients (8701 eyes).
  • Utilized Royston-Parmar method to model time-to-event for keratometric progression and CXL.
  • Investigated age, keratometry, corneal thickness, and genetic data (SNPs) as covariates.

Main Results:

  • Corneal crosslinking (CXL) served as a more robust endpoint than keratometric progression for the prognostic model.
  • The final model explained 33% of variation; significant predictors included age, maximum anterior keratometry, and minimum corneal thickness.
  • Identified SNPs associated with keratoconus did not significantly contribute to progression risk.

Conclusions:

  • A validated prognostic model can improve patient empowerment, triage, and healthcare service provision for keratoconus.
  • Age at presentation is the primary predictor of keratoconus progression risk.
  • Genetic factors (SNPs) do not appear to influence keratoconus progression in this cohort.