An Exploratory Analysis of Forme Fruste Keratoconus Sensitivity Diagnostic Parameters

Purpose: To re-statistical analysis of the Pentacam or Corvis ST parameters from literatures, and to obtain more sensitive diagnostic parameters for clinical keratoconus (CKC) and forme fruste keratoconus (FFKC), respectively. Methods: The parameters and the corresponding area of ROC curve (AUC) in previous studies were extracted and screened to obtain the database of CKC (Data-CKC) and FFKC (Data-FFKC), respectively. Two different importance evaluation methods (%IncMSE and IncNodePurity) of random forest were used to preliminary select the important parameters. Then, based on the partial dependency analysis, the sensitive diagnostic parameters that had promotion to the diagnostic performance were obtained. Data-FFKC was analyzed in the same way. Finally, a diagnostic test meta-analysis on the sensitive parameter of interest was conducted to verify the reliability of the above analysis methods.Results: There were 88 parameters with 766 records in Data-CKC, 57 parameters with 346 records in Data-FFKC. Based on two importance evaluation methods, 60 important parameters were obtained, of which 20 were further screened as sensitive parameters of keratoconus, and most of these parameters were related to the thinnest point of cornea. The stiffness parameter at first applanation (SPA1) was the only Corvis ST output parameter sensitive to FFKC except the Tomographic and Biomechanical Index (TBI) and the Corvis Biomechanical Parameter (CBI). A total of 4 records were included in the meta-analysis of diagnostic tests on SPA1. The results showed that there was threshold effect, but no significant heterogeneity (I2=33%), and the area under the SROC curve was 0.87 (95% CI, 0.84-0.90).Conclusions: the sensitive diagnostic parameters of keratoconus mainly include the 7 categories. The diagnostic value of SPA1, which represents the biomechanical properties of cornea, is worthy of further attention in the diagnosis of keratoconus in forme fruste keratoconus. Moreover, the morphological parameters based on the typical position of the thinnest point of corneal thickness have potential special significance for the diagnosis of disease.

Corneal epithelial thickness analysis of forme fruste keratoconus with optical coherence tomography

AIM: To explore the significance of corneal epithelial thickness analysis in diagnosing early keratoconus. METHODS: There were 26 clinical keratoconus, 21 forme fruste keratoconus, 40 high corneal astigmatism (ΔK) and 40 low ΔK eyes involved in the study. Fourier-domain optical coherence tomography was used to measure the corneal epithelial thickness of four groups. The morphological features of topographic map and the thickness of corneal epithelial thinnest point were analyzed. The distribution curve of corneal epithelial thickness at 45°, 90°, and 135° axial directions that are through the pupil center was also analyzed. One-way ANOVA was performed to compare the data. RESULTS: The topographic map of forme fruste keratoconus corneal epithelial thickness was uniformity shape; crater shape existed only in clinical keratoconus group; and central island shape mainly existed in high ΔK group. The thinnest point of corneal epithelial thickness of forme fruste keratoconus group was significantly lower than that of low ΔK group (P=0.022). The thickness of corneal epithelium in the forme fruste keratoconus at 90° was thinner than that in the low astigmatism group at -1, and -2 mm points (P-1 mm=0.015, P-2 mm=0.036). CONCLUSION: The analysis of the thinnest point in forme fruste keratoconus corneal epithelium appears earlier than corneal epithelial remodeling. The topographic map of corneal epithelium in high ΔK eyes appears in central island shape, and can be used for the differential diagnosis of early keratoconus.

Comprehensive evaluation of corneas from normal, forme fruste keratoconus and clinical keratoconus patients using morphological and biomechanical properties

Objective To more comprehensively evaluate the ability of the parameters reflecting the morphological and biomechanical properties of the cornea to distinguish clinical keratoconus (CKC) and forme fruste keratoconus (FFKC) from normal. Methods Normal eyes (n = 50), CKC (n = 45) and FFKC (n = 15) were analyzed using Pentacam, Corvis ST and ORA. Stepwise logistic regression of all parameters was performed to obtain the optimal combination model capable of distinguishing CKC, FFKC from normal, named SLR1 and SLR2, respectively. Receiver operating characteristic (ROC) curves were applied to determine the predictive accuracy of the parameters and the two combination models, as described by the area under the curve (AUC). AUCs were compared using the DeLong method. Results The SLR1 model included only the TBI output by Pentacam, while the SLR2 model included the morphological parameter F.Ele.Th and two parameters from the Corvis ST, HC DfA and SP-A1. The majority of the parameters had sufficient strength to differentiate the CKC from normal corneas, even the seven separate parameters and the SLR1 model had a discrimination efficiency of 100%. The predictive accuracy of the parameters was moderate for FFKC, and the SLR2 model (0.965) presented an excellent AUC, followed by TBI, F.Ele.Th and BAD-D. Conclusion The F.Ele.Th from Pentacam was the most sensitive morphological parameter for FFKC, and the combination of F.Ele.Th, HC DfA and SP-A1 made the diagnosis of FFKC more efficient. The CRF and CH output by ORA did not improve the combined diagnosis, despite the corneal combination of morphological and biomechanical properties that optimized the diagnosis of FFKC.

Comparison of the Morphological and Biomechanical Characteristics of Keratoconus, Forme Fruste Keratoconus, and Normal Corneas

Background: To explore the feasibility of corneal morphological and biomechanical parameters for keratoconus and forme fruste keratoconus diagnosis.Methods: This case-control study included a total of 517 eyes from 408 keratoconus patients (KC group), 83 eyes from 83 forme fruste keratoconus patients (FFKC group), and 158 eyes from 158 patients with normal corneas (NL group). All subjects underwent routine ophthalmology examinations. Pentacam and Corneal Visualization Scheimpflug Technology were used to obtain corneal morphological and biomechanical parameters. Differences between the groups were compared using receiver operating characteristic (ROC) curve analysis.Results: Comparison of the NL group with the KC and FFKC groups (P<0.05), and the NL and KC groups alone (P<0.017), revealed statistically significant differences in corneal morphological and biomechanical parameters, except time from the start until the highest concavity (HCT), deflection length of the first applanation (A1 DfL), and deflection length of the second applanation (A2 DfL). Comparison of the NL and FFKC groups revealed 12 significantly different parameters (P<0.017). ROC analysis showed that all corneal morphological parameters and most biomechanical parameters distinguished KC from NL, with an area under the curve (AUC) greater than 0.80, of which Belin-Ambrósio enhanced ectasia total deviation index (BAD-D) and tomographic and biomechanical index (TBI) were most efficient. Except for central astigmatism from the anterior corneal surface (AstigF), the AUC that distinguished FFKC from NL was 0.862. Other parameters distinguished FFKC from NL with low efficiency. Conclusion: All corneal morphological and most biomechanical parameters are different in KC versus NL, but only a few parameters are different in FFKC versus NL. BAD-D and TBI have the highest efficiency, sensitivity, and specificity for distinguishing KC from NL. The parameters had a low ability to distinguish FFKC from NL. The application of biomechanical instruments to diagnose early keratoconus needs further study.

Comprehensive Evaluation of Corneas from Normal, Forme Fruste Keratoconus, and Clinical Keratoconus Patients using Morphological and Biomechanical Properties

Background: At present, the diagnosis of keratoconus with Pentacam (typical morphological measurement device), Ocular Response Analyzer (ORA) and Corneal Visualization Scheimpflug Technology (Corvis ST) (two main in vivo biomechanical measurement devices), is mainly for clinical keratoconus (CKC), and the analysis of diagnostic performance is only for the comparison of single output parameters, and the total number of parameters is not complete. Therefore, we intend to use the above three devices to more comprehensively evaluate the ability of the parameters reflecting the morphological and biomechanical properties of the cornea to distinguish keratoconus, especially contralateral normal eye of unilateral keratoconus, that is, the forme fruste keratoconus (FFKC), so as to to obtain further reference for early diagnosis of keratoconus.Methods: Normal eyes (n = 50), CKC eyes (n = 45), and FFKC eyes (n = 15) were analyzed using Pentacam, Corvis ST, and ORA. Stepwise logistic regression of all parameters was performed to obtain the optimal combination model capable of distinguishing CKC, FFKC from normal, named SLR1 and SLR2, respectively. Receiver operating characteristic (ROC) curves were applied to determine the predictive accuracy of the parameters and the two combination models, as described by the area under the curve (AUC). AUCs were compared using the DeLong method. Results: The SLR1 model included only the TBI output by Pentacam, while the SLR2 model included the morphological parameter F.Ele.Th, and two parameters from the Corvis ST, HC DfA, and SP-A1. The majority of the parameters had sufficient strength to differentiate the CKC from normal corneas. Even the seven separate parameters and the SLR1 model had a discrimination efficiency of 100%. The predictive accuracy of the parameters was moderate for eyes with FFKC, and the SLR2 model (0.965) presented an excellent AUC, followed by TBI, F.Ele.Th and BAD-D.Conclusion: The F.Ele.Th from Pentacam was the most sensitive morphological parameter for FFKC, and the combination of F.Ele.Th, HC DfA and SP-A1 made the diagnosis of FFKC more efficient. The CRF and CH output by ORA did not improve the combined diagnosis, despite the corneal combination of morphological and biomechanical properties that optimized the diagnosis of FFKC.