Keratoconus Prediction Model Shows Top Progression Risk Factors

Keratoconus. (Photo By BSIP/UIG Via Getty Images)
The study shows age at presentation is the most significant of the risk factors.

A personalized prognostic model for the progression of keratoconus may improve patients’ understanding of their condition and the need for  corneal crosslinking (CXL), according to a study published in American Journal of Ophthalmology. 

The model generates a time-to-event curve (with the need for CXL being defined as the “event”) by taking into account age, maximum anterior keratometry (Kmax), Front K1, and minimum pachymetry from time of presentation. 

Investigators looked at 9341 eyes of 5025 patients  with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available for the investigation of both keratometry or CXL as end-points for progression. Researchers then used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model while also calculating hazard ratios (HR) for each significant covariate.

The research shows that “parameters recorded at the first examination (age, Kmax, Front K1, minimum pachymetry) can produce a time-to-event curve to calculate a personalized risk for keratoconus progression,” the investigators report.

The final model explained 33% of the variation in time-to-event: age HR [95% confidence limits] 0.9 [0.90-0.91], 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 researchers explain that their model shows age at presentation is the most significant predictor of progression risk.

“Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability,” investigators note.

Study limitations include unreliable recording of patients who may have previously undergone CXL at another hospital; the unavailability of ethnicity information for 50% of the dataset.


Maile H, Li J, Fortune M et al. Personalized model to predict keratoconus progression from demographic, topographic and genetic dataAm J Ophthalmol. Published online April 22, 2022. doi:10.1016/j.ajo.2022.04.004