Tomographic and aberrometric assessment of first‐time diagnosed paediatric keratoconus based on age ranges: a multicentre study

Abstract

Purpose To describe paediatric keratoconus (KC) patients by tomographic and aberrometric characteristics at first diagnosis, in a multicentre study. Methods We included 278 eyes from 139 paediatric patients, with a first tomographic diagnosis (Pentacam®) of KC prior to 18 years old. KC classification was based on the KC Index (≥ 1.07) and Topographic Keratoconus Classification (TKC ≥ 1). Patients were divided based on age ranges (14 and under and over 14 years) and gender. Statistical analysis was performed with SPSS statistics 25.0. ANOVA factor was carried out comparing to compare groups. Results 278 eyes were screened, and 230 eyes were diagnosed with paediatric KC. Mean age was 15.48 ± 2.33 (6 to 18) years. We found differences in terms of TKC (2.08 ± 0.89 and 2.38 ± 0.82, p < 0.05) and spherical aberration (−0.71 ± 0.97 and −1.07 ± 1.36, p < 0.05) among the 14 years old or under and above 14 years old groups, respectively. Overall, female paediatric KC patients presented a more severe TKC, Belin Ambrosio Display, maximum keratometry, asphericity and primary and secondary coma aberrations compared to male KC patients. We observed a correlation between CDVA and asphericity (r = 0.71, p < 0.01), as well as between CDVA and spherical aberration (r = 0.69, p < 0.01). Conclusion Our findings revealed that the debut of KC is usually in a moderate to advanced stage in the paediatric population at first diagnosis, particularly in female patients. Corneal tomography should be systematically performed in children with recent onset of corneal astigmatism.

Distribution of pediatric keratoconus by different age and gender groups

“…It has been reported that nearly half of the pediatric keratoconus cases present with progressive disease, which is more aggressive than in adult patients ( 13 , 17 ). Therefore, the distribution of pediatric keratoconus, which is in the early stages of the disease, has been reported inadequately but is of great value, especially in different age and gender groups ( 18 , 19 ). A recent multicenter study of pediatric keratoconus showed that the age was not different between male and female patients, with female patients exhibiting a higher topographic keratoconus classification (TKC) value than male patients ( 18 ).…”

Forecasting Progressive Trends in Keratoconus by Means of a Time Delay Neural Network

“…Based on the top 10 parameters from that list (see Table S1 ), 6 nonredundant, potentially platform-independent parameters were selected: age, known to affect KC progression [ 11 , 26 ]; the average keratometry in a 3 mm area around the maximum keratometry (KmaxZonalMean3mm) [ 27 ]; the steepest radius (RsF) and best fit sphere over an area of 8 mm (BFSF) of the front surface; and the average radius of the back surface (RmB) and LOGIK [ 18 ], which is based on the elevation maps of both corneal surfaces and the minimum pachymetry. None of the parameters included were based on a single corneal point.…”

Keratoconus detection of changes using deep learning of colour-coded maps

“…The diagnosis often requires an assessment of the topography and/or tomography of the cornea to reveal subtle changes in corneal morphology. [3][4][5][6] The evaluation of corneal topography and tomography may itself be challenging and open to misinterpretation. 5 Several indices have been proposed to facilitate differentiation between keratoconus and normal eyes, such as the zone of increasing corneal power, inferiorsuperior corneal power asymmetry, steepest radial axes, posterior and anterior ectasia and corneal pachymetry.…”