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Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Structural Outcomes of Corneal Cross-Linking for Keratoconus

Version 1 : Received: 4 June 2024 / Approved: 4 June 2024 / Online: 5 June 2024 (05:54:59 CEST)

How to cite: Statsenko, Y.; Smetanina, D.; Voitetskii, R.; Simiyu, G. L.; Pazniak, M.; Likhorad, E.; Pazniak, A.; Beliakouski, P.; Abelskyi, D.; Gorkom, K. N.-V.; Ljubisavljevic, M. Structural Outcomes of Corneal Cross-Linking for Keratoconus. Preprints 2024, 2024060185. https://doi.org/10.20944/preprints202406.0185.v1 Statsenko, Y.; Smetanina, D.; Voitetskii, R.; Simiyu, G. L.; Pazniak, M.; Likhorad, E.; Pazniak, A.; Beliakouski, P.; Abelskyi, D.; Gorkom, K. N.-V.; Ljubisavljevic, M. Structural Outcomes of Corneal Cross-Linking for Keratoconus. Preprints 2024, 2024060185. https://doi.org/10.20944/preprints202406.0185.v1

Abstract

Background: Studies have tried to identify predictors of functional markers of keratoconus progression, whereas structural outcomes of collagen cross-linking have not been properly investigated. Objective: find and set up a combination of diagnostic modalities and measurements to reliably reflect CXL efficiency. Material and methods: We retrospectively reviewed the medical histories of 107 KC patients (131 eyes) who underwent CXL in a medical center from January 2018 to December 2022. The dataset included preoperative examinations and follow-up results, which provided a total of 796 observations. The cases of other corneal diseases, repeated CXL, pregnancy, and observations without follow-up examinations were excluded. Results: The study findings revealed a significant drop in the central and corneal thickness during 14.01±9.98 months of observations. Linear and polynomial equations suggested different trends in pachymetry change after CXL. The linear model showed corneal thinning, and according to the polynomials, pachymetry findings return to baseline values two years after CXL. Conclusion: The most reliable prognosis of postoperative corneal thickness is achieved with the models trained on keratometry readings and topography indices. BAD indices are also reliable predictors of the corneal thickness after CXL. A combination of the aforementioned structural parameters and their derivatives can reliably predict CXL efficiency.

Keywords

keratoconus; corneal collagen cross-linking; CXL outcomes; machine learning models; predictive models; keratometry readings; corneal thickness; corneal thinning; individualized approach; precision medicine

Subject

Medicine and Pharmacology, Ophthalmology

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