Maile, Howard P;
Li, Ji-Peng Olivia;
Fortune, Mary D;
Royston, Patrick;
Leucci, Marcello T;
Moghul, Ismail;
Szabo, Anita;
... Gore, Daniel M; + view all
(2022)
Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data.
American Journal of Ophthalmology
, 240
pp. 321-329.
10.1016/j.ajo.2022.04.004.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ajo.2022.04.004 |
| Publisher version: | https://doi.org/10.1016/j.ajo.2022.04.004 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Keratoconus, corneal cross-linking, keratoconus genetics, keratoconus prediction |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10147814 |
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