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Bowman Club 2022, 25 March 2022
Oral abstract presentation
OP-3 Personalised model to predict keratoconus progression from demographic, topographic and genetic data
  1. Olivia Li1,
  2. Howard P Maile2,
  3. Mary D Fortune3,
  4. Patrick J Royston4,
  5. Marcello T Leucci2,
  6. Bruce D Allan2,
  7. Alison J Hardcastle2,
  8. Pirro Hysi5,
  9. Nikolas Pontikos1,
  10. Stephen J Tuft1,2,
  11. Daniel M Gore1,2
  1. 1Moorfields Eye Hospital, London, UK
  2. 2UCL Institute of Ophthalmology, London, UK
  3. 3MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, UK
  4. 4MRC Clinical Trials Unit at UCL, London, UK
  5. 5Section of Ophthalmology, School of Life Course Sciences, King’s College, London, UK

Abstract

*Correspondence – Olivia Li: o.li@nhs.net

Objective To generate a personalised prognostic model to predict keratoconus progression to corneal collagen cross-linking (CXL).

Methods and Analysis In this retrospective cohort study, we recruited 5,025 patients (9,341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients was available. We evaluated both change in keratometry or CXL as indices of 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.

Results After exclusions, model-fitting comprised 8,701 eyes, of which 3,232 underwent CXL. For early keratoconus CXL provided a more robust prognostic model than keratometric progression. The final model explains 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.

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.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

http://dx.doi.org/10.1136/bmjophth-2022-BCM.3

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