Detection of SCKC has always been a challenge for ophthalmologists, especially when there are no clinical signs or symptoms in the patient.
The rotary camera Scheimpflug (Pentacam®) topography is usually used to diagnose keratoconus in daily clinical practice [ 13-14, 20-26, 30]. The topographic parameters of clinical keratoconus are recognizable. However, it is not easy to diagnose subclinical keratoconus based on topographic variables. This study calculates a diagnostic model based on the aberrometry data of the anterior and posterior corneal surface provided by the Pentacam
The selection of the sample was made that there were no differences between the age groups [12-15, 17-19, 21, 24, 29], sex [14, 21-22, 29], and eye [21-22]. This is an advantage when interpreting results that aren´t biased by age and sex. As reported by Koçamis et al. [22], there are significant differences for age between keratoconus (26.19 ± 7.90) and healthy (30.88 ± 7.57)
Pupillary dilatation is another parameter that can modify the aberrometry results [27]. In this study, it was prefixed in 6 mm. In previous studies [8, 12-14, 20-23, 29] like Hondur et al [27] established it in 5 mm.
Many studies have been made between healthy patients with Keratoconus [8-10, 12-14, 20-27] or healthy patients with SCKC [15-19, 21-23, 26, 28-31]. The purpose in most of them was to analyze the topographic parameters to find differences between a healthy patient and an incipient corneal ectasia without symptoms. However in all of them, many different classification methods have been used: Amsler-Krumeich [24, 27- 28], Alió and Shabayek [13, 20], KISA % index [21] or KSS [29]. All this methodological variability leads to an outstanding selection and classification bias when making comparisons between studies.
If we analyze the refractive parameters of our study, statistically significant differences were obtained between the three groups analyzed for the sphere, the cylinder and the spherical equivalent (p < 0.05, Kruskal-Wallis), as in other studies[19, 30]. However, when comparing normal corneas with SCKC, we obtained statistically significant differences only for the sphere (p = 0.012, U Mann-Whitney). Saad and Gatinel [17] obtained that the mean of the sphere was significantly higher in their normal group than in their SCKC group (p < 0.001). Reddy et al [19] observed statistically significant differences for the cylinder (p < 0.001) not for the sphere (p = 0.08). However, Naderan et al [29] didn´t find statistically significant differences for sphere (p = 0.136) or cylinder (p = 0.108). In our study, we found statistically significant differences between the visual acuity of the three groups, but it wasn´t differences between normal corneas and SCKC. These values are consistent with previous studies [8, 20-22, 27, 30-31]. When we analyzed a bivariate analysis between normal corneas and SCKC, statistically significant differences were only obtained for variables of vertical asymmetry, total coma to 90º and corneal thickness (p < 0.05). Bührenet al [15], found that the anterior coma to 90º would be the most useful parameter to differentiate normal corneas from SCKC. Other parameters such as the posterior coma to 90º and the minimum corneal thickness didn't exceed the value of the anterior surface for this author. In our study, when the total corneal coma to 90º was analyzed in absolute value, we found that it was higher in SCKC (|-0.404| ± 0.319) than in normal (0.0123 ± 0.209), but lower than in keratoconus (|-1.877| ± 1.413). This value indicates that the parameter total corneal coma to 90º had increased with the natural history of the disease [17]. The negative sign of the corneal coma to 90º refers to the lower decentration of the cone in the y-axis [17]. More recently, Naderan et al [29] and Xu et al [30] indicated the importance of posterior surface aberrations to differentiate normal from SCKC corneas. In the first study, they obtained that the values for posterior coma to 90º of the healthy group were 0.032 ± 0.363 and for the SCKC group were 0.193 ± 0.264 with statistically significant differences between groups (p = 0.003, U Mann-Whitney). In our database, the posterior coma to 90º for normal corneas were -0.008±0.049 and for SCKC were 0.112±0.103, (p < 0.05, U Mann-Whitney). The relationship between coma-like aberrations of the anterior surface and the degree of manifest keratoconus is well known [8, 12, 22, 24-27]. Piñero et al [13] were the first to attempt to characterize the posterior corneal surface and its aberrations in patients with normal corneas and keratoconus, finding results that were not concordant by the optical theory of the corneal surface. In Piñero´s study, have obtained values of anterior coma to 90º of 0.001 ± 0.225 and posterior coma to 90º of 0.319 ± 0.372 from the healthy patients. In keratoconus were -1.754 ± 0.976 and -3.692 ± 1.81 respectively.
If we analyze the results of our study, in healthy patients the anterior coma to 90º was 0.01 ± 0.20 and posterior coma to 90º was -0.01 ± 0.05 (the same mean but opposite sign) and in keratoconus, we obtained -2.06 ± 1.51 and 0.53 ± 0.38 respectively. In our case, the anterior coma to 90º, in absolute value, were higher than the posterior ones. In subclinical keratoconus, the anterior coma to 90º was 0.49±0.43 and the posterior coma to 90º was 0.11±0.10. Comparing the results, we observed that both anterior and posterior coma at 90º increase with the appearance of the corneal alterations of keratoconus from early stages but with opposite signs, while the anterior coma to 90º becomes negative, the posterior coma to 90º becomes positive
Attempted to other parameters, several studies like Buhrenet al [15] observed that the MCT was the most discriminating parameter between normal corneas and SCKC. However, they concluded that the posterior surface was not discriminate as to the anterior surface, and this surface was not sufficient for the diagnosis of the subclinical keratoconus. Otherway Safarzadeh et al [28] reflected that minimum corneal thickness and posterior corneal elevation would be the best parameters for differentiating suspicious keratoconus from healthy eyes in concordance with our results.
Finally, we propose a binary logistic model to predictive the subclinical keratoconus. Other authors [17, 21, 30] have established binary logistic models for keratoconus diagnosis but not for subclinical keratoconus. Our results analyze the probability of subclinical keratoconus using three variables: MCT, anterior coma to 90º and posterior to 90º. The validation of this model with Hosmer Lemeshow test and the AUC (Area Under Curve of the sensibility vs specificity graphic), suggest a good calibration in 92% of cases The main limitation of our results is the population size of the subclinical keratoconus. We need to increase the number of subclinical keratoconus to improve the sensibility of the model