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Publication Detail
Personalized model to predict keratoconus progression from demographic, topographic and genetic data.
  • Publication Type:
    Journal article
  • Publication Sub Type:
  • Authors:
    Maile HP, Li J-PO, Fortune MD, Royston P, Leucci MT, Moghul I, Szabo A, Balaskas K, Allan BD, Hardcastle AJ, Hysi P, Pontikos N, Tuft SJ, Gore DM
  • Publisher:
    Elsevier Masson
  • Publication date:
  • Journal:
    American Journal of Ophthalmology
  • Status:
    Published online
  • Country:
    United States
  • Print ISSN:
  • PII:
  • Language:
  • Keywords:
    Keratoconus, corneal cross-linking, keratoconus genetics, keratoconus prediction
PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal cross-linking (CXL). DESIGN: Retrospective cohort study. METHODS: We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients was available. We investigated both keratometry or CXL as end-points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HR) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions. RESULTS: After exclusions, model-fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time-to-event: age HR [95% confidence limits] 0.9 [0.90-0.91], maximum anterior keratometry (Kmax) 1.08 [1.07-1.09], and minimum corneal thickness 0.95 [0.93-0.96] as significant covariates. Single nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability. CONCLUSIONS: A prognostic model to predict keratoconus progression could aid patient empowerment, triage and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.
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