No author has a financial or proprietary interest in any material or method mentioned.

Our results suggest that KC exerts a significant impact on KC patients' QOL, even in its early stages with normal best-spectacle-corrected visual acuity. Moreover, both CXL and CXL combined with t-PRK seem to exert a beneficial impact on self-reported QOL, suggesting that they should be applied as soon as possible.

ORA parameters demonstrate significant differences between KC and non-KC eyes. Both CH and CRF present significant correlations with visual acuity and corneal parameters. CXL exerts a non-significant impact on ORA measurements.

ABSTRACT.Purpose: To evaluate the diagnostic capacity of the Ocular Response Analyser's keratoconus match index (KMI) and keratoconus match probability (KMP) classification in a sample of keratoconus (KC) patients. Methods: Keratoconus match index and KMP from 114 KC eyes, randomly selected from 114 patients with bilateral keratoconus (KCG), were compared with the corresponding ones from 109 normal eyes (CG). Keratoconus match index's predictive accuracy was assessed by receiver operating curves (ROC). Keratoconus match probability level of agreement was evaluated at the different KC stages of the Amsler-Krumeich classification. Correlations were estimated with topographic keratoconus classification (TKC), keratoconus index (KI), index of surface variance (ISV), vertical asymmetry (IVA), height asymmetry (IHA), height decentration (IHD), minimal radius (Rmin), central corneal thickness (CCT), thinnest corneal thickness (TCT) mean keratometry (Km) and intraocular pressure (IOPg). Results: Mean KMI in KCG and CG was 0.20 ± 0.38 and 0.98 ± 0.25, respectively (p < 0.01). Significant KMI differences (p < 0.01) were detected in different KC groups [range: 0.62 ± 0.38 (KC 1), )0.62 ± 0.04 (KC 4)]. Significant correlation was detected between KC staging and KMI (r = )0.56, p < 0.0001). Keratoconus match probability identified 22.03% of the CG eyes as suspect. Moreover, KMP identified 7.01% and 23.68% of the KCG eyes as normal and suspect, respectively. Receiver operating curves analysis for KMI parameter indicated a predictive accuracy of 97.7% (cut-off point: 0.512, sensitivity: 91.18%, specificity: 94.34%). Conclusions: Keratoconus match index seems to be a reliable index in keratoconus diagnosis and staging. Keratoconus match probability identifies a significant percentage of topographically defined KC and CG eyes as suspect. Diagnostic capacity of these novel indexes needs to be further explored.

No author has a financial or proprietary interest in any material or method mentioned.

Purpose. To evaluate the sensitivity and specificity of corneal biomechanical metrics, anterior segment data, and a combination model in differentiating forme fruste keratoconus (FFK) from normal corneas. Methods. A total of 50 FFK eyes were identified by calculation of the KISA index and recruited FFK group. Results were compared with 50 normal eyes (NG group) randomly selected from 50 patients. The following parameters were evaluated for their diagnostic capacity by evaluation of their receiver operating characteristic curves (ROC): corneal hysteresis (CH), corneal resistance factor (CRF), corneal astigmatism (Cyl), anterior chamber depth (ACD), corneal volume (CV) at 3 mm (CV3) and at 5 mm (CV5), maximum posterior elevation value (PEL), central corneal thickness (CCT), thinnest corneal thickness (TCT) and its coordinates (TCTx, TCTy), the ratio TCT/CCT, pachymetric progression indexes (PPImin, PPIavg, and PPImax), and Ambrósio's relational thickness (ARTmin, ARTavg, and ARTmax). Logistic regression was attempted for identification of a combined diagnostic model. Results. Significant differences were detected in all studied parameters except the Cyl, ACD, TCTx, and CV. Among individual parameters, the highest predictive accuracy was for ARTavg (area under the curve [AUC] 95.4%, sensitivity 90%, specificity 88.9%) and TCT (AUC 95.3%, sensitivity 90.9%, specificity 89%). Sufficient predictive accuracy (AUC 99.4%, sensitivity 98.8%, specificity 94.6%) was identified in a diagnostic model that combined the CRF, ARTavg, and PEL parameters. Conclusions. None of the individual parameters provide sufficient diagnostic capacity in FFK. However, diagnostic models that combine biomechanical and tomographic data seem to provide high accuracy in differentiating FFK from normal corneas.

No abstract

The posterior corneal elevation values and the RMS parameter can effectively discriminate KC from normal corneas, although measured values and cutoff points depend on the selection of reference body. Moreover, the use of a toric ellipsoid reference body, with a fixed eccentricity of 0.4, seems to be an effective tool even in diagnosis of FFK.