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

Hui Zhang, Lei Tian, Li-Li Guo, Xiao Qin, Di Zhang, Lin Li, Haixia Zhang, Ying Jie
2020 unpublished
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
more » ... e 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.
doi:10.21203/rs.3.rs-33632/v1 fatcat:vwpfdsb5djgjdj5angz5k3bhua