Methods: Subjects were randomly selected to undergo KC screening using a proportional stratified sampling method. Of the 648 invited subjects, 585 (90.3%) responded to the invitation. The demographic data, medical/family history, and habits of the subjects were collected using a standardized questionnaire. Subjects were classified as KC, ectasia susceptibility, and normal based on the corneal tomography. The chi-square and Kruskal-Wallis tests were used for the analysis of categorical variables and parametric values, respectively. Risk factors for KC were determined using logistic regression analysis.Results: Of the enrolled 585 subjects, the prevalence of KC was 2393/100 000 (2.4%, 95% CI: 1.3% to 4%), whereas that of ectasia susceptibility was 1538/100 000 (1.5%, 95% CI: 0.7% to 2.9%). Although the prevalence was much higher in males (4%, 95% CI: 1.7% to 7.7%) than in females (1.6%, 95% CI: 1.1% to 4.4%), the difference was not statistically significant (P = .09). Most (78.6%, n = 11) patients with KC were unaware of their disease. Eye rubbing (odds ratio [OR]: 3.53, P = .024) and consanguineous marriage (OR: 12.87, P = .032) were independent risk factors for KC. Conclusions:To the authors' knowledge, this is the first population-based KC prevalence study in a randomized sample conducted in Turkey. The prevalence of KC in Turkey was much higher than in European countries but similar to neighboring countries in the Middle East. Eye rubbing and history of consanguineous marriage were significant risk factors.

Keratoconus had traditionally been considered a rare disease at a time when the imaging technology was inept in detecting subtle manifestations, resulting in more severe disease at presentation. The increased demand for refractive surgery in recent years also made it essential to more effectively detect keratoconus before attempting any ablative procedure. Consequently, the armamentarium of tools that can be used to diagnose and treat keratoconus has significantly expanded. The advances in imaging technology have allowed clinicians and researchers alike to visualize the cornea layer by layer looking for any early changes that might be indicative of keratoconus. In addition to the conventional geometrical evaluation, efforts are also underway to enable spatially resolved corneal biomechanical evaluation. Artificial intelligence has been exploited in a multitude of ways to enhance diagnostic efficiency and to guide treatment. As for treatment, corneal cross-linking treatment remains the mainstay preventive approach, yet the current main focus of research is on increasing oxygen availability and developing new strategies to improve riboflavin permeability during the procedure. Some new combined protocols are being proposed to simultaneously halt keratoconus progression and correct refractive error. Bowman layer transplantation and additive keratoplasty are newly emerging alternatives to conventional keratoplasty techniques that are used in keratoconus surgery. Advances in tissue engineering and regenerative therapy might bring new perspectives for treatment at the cellular level and hence obviate the need for invasive surgeries. In this review, we describe the advances in the diagnosis and treatment of keratoconus primarily focusing on newly emerging approaches and strategies.

Introduction: To evaluate the corneal epithelial thickness (CET) profiles and their correlations with axial length (AL) and anterior corneal radius of curvature (Rm F) across different refractive error groups. Methods: A total of 1225 eyes of 616 normal patients were included. CET mapping, AL, and Rm F were obtained using spectral-domain optical coherence tomography, optical biometry, and Scheimpflug corneal tomography, respectively. In the CET map, one central (2 mm), eight paracentral (2-5 mm), and eight peripheral (5-6 mm) quadrants were evaluated separately. The subjects were divided into four groups based on their refractive status: hyperopia (spherical equivalent [SE] C ?0.50 D), emmetropia (SE [ -0.50 D and \ ?0.50 D), low myopia (SE B -0.50 D and [ -3.0 D), and moderate-high myopia (SE B -3.0 D) groups. Linear mixed model analysis with Bonferroni correction was used to compare CET according to refractive error groups. The correlations

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Purpose: To develop a combined biomechanical and tomographic model for identifying eyes with subclinical keratoconus (SKC) that are categorized as normal or borderline in the Pentacam Belin/Ambrósio Enhanced Ectasia Display. Methods: This case–control study comprised 62 eyes with SKC and randomly selected eyes of 186 age-matched healthy controls. SKC was defined as the presence of the following: 1) normal topography, topometric indices, and slit lamp; 2) normal or borderline Belin/Ambrósio Enhanced Ectasia Display D index, back and front elevation difference; and 3) keratoconus in the fellow eye. Stepwise logistic regression analysis was performed to identify the best variable combination for detecting SKC cases from Ocular Response Analyzer and Pentacam parameters. Receiver operating characteristic curve analysis was used to determine the predictive accuracy [area under the curve (AUC)] of the model. Based on the predictors in the final logistic regression model, a linear equation was derived using the discriminant function analysis. Results: The final model (AUC: 0.948, sensitivity: 87.1%, and specificity: 91.4%) chose corneal hysteresis (CH) and D index from a total of 63 candidate variables. The final model had a higher AUC compared with D (0.933, P = 0.053) and CH (0.80, P < 0.001) alone. According to the discriminant function analysis, a higher CH was required with increasing D index to classify an eye as normal. Conclusions: The proposed combined model provided varying cutoffs for CH and D as a function of the other. The probability plot as a function of CH and D index may be used for identifying eyes with SKC.

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Objectives: To determine corneal biomechanical and tomographic factors associated with keratoconus (KC) progression. Materials and Methods: This study included 111 eyes of 111 KC patients who were followed-up for at least 1 year. Progression was defined as the presence of progressive change between the first two consecutive baseline visits in any single parameter (A, B, or C) ≥95% confidence interval or two parameters ≥80% confidence interval for the KC population evaluated by the Belin ABCD progression display. The eye with better initial tomographic findings was chosen as the study eye. Analyzed Pentacam parameters were maximum keratometry (Kmax), minimum pachymetry (Kmin), central corneal thickness, thinnest corneal thickness, 90° vertical anterior and posterior coma data in Zernike analysis, and Belin Ambrosio Enhanced Ectasia Display Final D value. Corneal hysteresis (CH) and corneal resistance factor (CRF) were analyzed together with the waveform parameters obtained with Ocular Response Analyzer (ORA). Factors related to KC progression were evaluated using t-tests and logistic regression tests. Statistical significance was accepted as p<0.05. Results: There were 44 (mean age: 27.1±8.5 years, female: 25) and 67 (mean age: 31.1±9.1 years, female: 36) patients in the progressive and non-progressive groups, respectively. Although Pentacam parameters along with CH and CRF were similar between the two groups, ORA waveform parameter derived from the second applanation signal p2area was statistically significantly lower in the progressive group (p=0.02). Each 100-unit decrease in p2area increased the likelihood of keratoconus progression by approximately 30% in the logistic regression analysis (β=0.707, p=0.001, model r2=0.27). Conclusion: Parameters derived from the second applanation signal of ORA may be superior to conventional ORA parameters and corneal tomography in predicting KC progression.