Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data
Copyright © 2022 Elsevier Inc. All rights reserved..
PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal crosslinking (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 were 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 (HRs) 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% CI) 0.9 (0.90-0.91), maximum anterior keratometry 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.
| Errataetall: |
CommentIn: Am J Ophthalmol. 2023 May;249:191-192. - PMID 36379321 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:240 |
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| Enthalten in: |
American journal of ophthalmology - 240(2022) vom: 01. Aug., Seite 321-329 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Maile, Howard P [VerfasserIn] |
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| Links: |
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| Themen: |
9007-34-5 |
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| Anmerkungen: |
Date Completed 26.07.2022 Date Revised 20.04.2023 published: Print-Electronic CommentIn: Am J Ophthalmol. 2023 May;249:191-192. - PMID 36379321 Citation Status MEDLINE |
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| doi: |
10.1016/j.ajo.2022.04.004 |
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| Förderinstitution / Projekttitel: |
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NLM339988460 |
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| 245 | 1 | 0 | |a Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data |
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| 500 | |a CommentIn: Am J Ophthalmol. 2023 May;249:191-192. - PMID 36379321 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022 Elsevier Inc. All rights reserved. | ||
| 520 | |a PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL) | ||
| 520 | |a DESIGN: Retrospective cohort study | ||
| 520 | |a METHODS: We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were 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 (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions | ||
| 520 | |a 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% CI) 0.9 (0.90-0.91), maximum anterior keratometry 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 | ||
| 520 | |a 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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