A comparison of keratoconus progression following collagen cross-linkage using standard or personalised keratometry thresholds

OBJECTIVE: To define how estimates of keratoconus progression following collagen cross-linking (CXL) vary according to the parameter selected to measure corneal shape. MATERIALS AND METHODS: We estimated progression following CXL in 1677 eyes. We compared standard definitions of keratoconus progression based on published thresholds for Kmax, front K2, or back K2, or progression of any two of these three parameters, with the option of an increased threshold for Kmax values ≥ 55D. As corneal thickness reduces unpredictably after CXL, it was excluded from the principal analysis. We then repeated the analysis using novel adaptive estimates of progression for Kmax, front K2, or back K2, developed separately using 6463 paired readings from keratoconus eyes, with a variation of the Bland–Altman method to determine the 95% regression-based limits of agreement (LoA). We created Kaplan-Meier survival plots for both standard and adaptive thresholds. The primary outcome was progression five years after a baseline visit 9–15 months following CXL. RESULTS: Progression rates were 8% with a standard (≥ 1.5D) threshold for K2 or 6% with the static multi-parameter definition. With a ≥ 1D threshold for Kmax, the progression was significantly higher at 29%. With adaptive Kmax or K2, the progression rates were similar (20%) but less than with the adaptive multi-parameter method (22%). CONCLUSIONS: Estimates of keratoconus progression following CXL vary widely according to the reference criteria. Using adaptive thresholds (LoA) to define the repeatability of keratometry gives estimates for progression that are markedly higher than with the standard multi-parameter method

Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data

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

Personalized model to predict keratoconus progression from demographic, topographic and genetic data

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

Nervous System and Intracranial Tumour Incidence by Ethnicity in England, 2001–2007: A Descriptive Epidemiological Study

BACKGROUND:There is substantial variation in nervous system and intracranial tumour incidence worldwide. UK incidence data have limited utility because they group these diverse tumours together and do not provide data for individual ethnic groups within Blacks and South Asians. Our objective was to determine the incidence of individual tumour types for seven individual ethnic groups. METHODS:We used data from the National Cancer Intelligence Network on tumour site, age, sex and deprivation to identify 42,207 tumour cases. Self-reported ethnicity was obtained from the Hospital Episode Statistics database. We used mid-year population estimates from the Office for National Statistics. We analysed tumours by site using Poisson regression to estimate incidence rate ratios comparing non-White ethnicities to Whites after adjustment for sex, age and deprivation. RESULTS:Our study showed differences in tumour incidence by ethnicity for gliomas, meningiomas, pituitary tumours and cranial and paraspinal nerve tumours. Relative to Whites; South Asians, Blacks and Chinese have a lower incidence of gliomas (p<0.01), with respective incidence rate ratios of 0.68 (confidence interval: 0.60-0.77), 0.62 (0.52-0.73) and 0.58 (0.41-0.83). Blacks have a higher incidence of meningioma (p<0.01) with an incidence rate ratio of 1.29 (1.05-1.59) and there is heterogeneity in meningioma incidence between individual South Asian ethnicities. Blacks have a higher incidence of pituitary tumours relative to Whites (p<0.01) with an incidence rate ratio of 2.95 (2.37-3.67). There is heterogeneity in pituitary tumour incidence between individual South Asian ethnicities. CONCLUSIONS:We present incidence data of individual tumour types for seven ethnic groups. Current understanding of the aetiology of these tumours cannot explain our results. These findings suggest avenues for further work