Dataset Information

Association between Corneal Stiffness Parameter at the First Applanation and Keratoconus Severity.

ABSTRACT:

Objective

The study aimed to evaluate the character of corneal stiffness parameter at the first applanation (SP-A1) in normal and keratoconus eyes and explore the association between SP-A1 and keratoconus severity indicators.

Methods

A total of 351 normal and 351 keratoconus eyes were included in the current study. Keratoconus was diagnosed according to the corneal topography map and slit-lamp examination. The severity of keratoconus was classified to mild (steep keratometry (Ks)?ResultsThe SP-A1 values of keratoconus were lower than that of normal eyes (72.11 (57.02, 83.08)?mmHg/mm vs 110.89 (100.45, 122.47)?mmHg/mm, P < 0.001). With the severity of keratoconus increasing, the SP-A1 decreased and the value of SP-A1 was 79.54 (70.30, 90.93)?mmHg/mm, 65.11 (53.14, 77.46)?mmHg/mm, and 47.59 (37.50, 62.14)?mmHg/mm in mild, moderate, and severe keratoconus eyes, respectively (P < 0.001). The negative association between SP-A1 and Ks was found in mild, moderate, and severe keratoconus eyes (r _mild?=?-0.171, r _moderate?=?-0.317, r _severe?=?-0.288, all P < 0.05). A positive association between SP-A1 and the thinnest corneal thickness (TCT) was found in all eyes (r_normal?=?0.687, r _mild?=?0.519, r _moderate?=?0.488, r _severe?=?0.382, all P < 0.05). SP-A1 was found to be statistically positively associated with intraocular pressure (IOP), biomechanical corrected IOP (bIOP), time from the initiation of air puff until the first applanation (A1T), corneal velocity at the second applanation (A2V), and negatively associated with deformation amplitude (DA), peak distance (PD), corneal velocity at the first applanation (A1V), time from the initiation of air puff until the second applanation (A2T), and DA Ratio Max [2?mm] both in normal and keratoconus eyes (all P < 0.05). The ROC analysis indicated that the AUC (95% CI) of SP-A1 was 0.952 (0.934-0.967) and 0.930 (0.904-0.951) in detecting keratoconus eyes and mild keratoconus eyes from normal eyes, respectively.

Conclusions

The SP-A1 value decreased while the keratoconus severity increased. It was lower in keratoconus than that in normal eyes and could be helpful in identifying keratoconus eyes from normal eyes. Further researches would be warranted to expand the clinical utility of SP-A1.

SUBMITTER: Yang K

PROVIDER: S-EPMC7726963 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association between Corneal Stiffness Parameter at the First Applanation and Keratoconus Severity.

Yang Kaili K Xu Liyan L Fan Qi Q Ren Shengwei S

Journal of ophthalmology 20201202

<h4>Objective</h4>The study aimed to evaluate the character of corneal stiffness parameter at the first applanation (SP-A1) in normal and keratoconus eyes and explore the association between SP-A1 and keratoconus severity indicators.<h4>Methods</h4>A total of 351 normal and 351 keratoconus eyes were included in the current study. Keratoconus was diagnosed according to the corneal topography map and slit-lamp examination. The severity of keratoconus was classified to mild (steep keratometry (Ks) ...[more]

PMID: 33343935

Similar Datasets

Project description:BackgroundTo investigate machine-learning (ML) algorithms to differentiate corneal biomechanical properties between different topographical stages of keratoconus (KC) by dynamic Scheimpflug tonometry (CST, Corvis ST, Oculus, Wetzlar, Germany). In the following, ML models were used to predict the severity in a training and validation dataset.MethodsThree hundred and eighteen keratoconic and one hundred sixteen healthy eyes were included in this monocentric and cross-sectional pilot study. Dynamic corneal response (DCR) and corneal thickness related (pachymetric) parameters from CST were chosen by appropriated selection techniques to develop a ML algorithm. The stage of KC was determined by the topographical keratoconus classification system (TKC, Pentacam, Oculus). Patients who were classified as TKC 1, TKC 2 and TKC 3 were assigned to subgroup mild, moderate, and advanced KC. If patients were classified as TKC 1-2, TKC 2-3 or TKC 3-4, they were assigned to subgroups according to the normative range of further corneal indices (index of surface variance, keratoconus index and minimum radius). Patients classified as TKC 4 were not included in this study due to the limited amount of cases. Linear discriminant analysis (LDA) and random forest (RF) algorithms were used to develop the classification models. Data were divided into training (70% of cases) and validation (30% of cases) datasets.ResultsLDA model predicted healthy, mild, moderate, and advanced KC eyes with a sensitivity (Sn)/specificity (Sp) of 82%/97%, 73%/81%, 62%/83% and 68%/95% from a validation dataset, respectively. For the RF model, a Sn/Sp of 91%/94%, 80%/90%, 63%/87%, 72%/95% could be reached for predicting healthy, mild, moderate, and advanced KC eyes, respectively. The overall accuracy of LDA and RF was 71% and 78%, respectively. The accuracy for KC detection including all subgroups of KC severity was 93% in both models.ConclusionThe RF model showed good accuracy in predicting healthy eyes and various stages of KC. The accuracy was superior with respect to the LDA model. The clinical importance of the models is that the standalone dynamic Scheimpflug tonometry is able to predict the severity of KC without having the keratometric data.Trial registrationNCT04251143 at Clinicaltrials.gov, registered at 12 March 2018 (Retrospectively registered).

Project description:BACKGROUND:Progressive keratoconus can lead to severely impaired vision, but there is currently no consensus on the definition of progressive disease. Errors in the measurement of the parameters commonly used to establish progressive disease were evaluated in an attempt to determine the limits at which a true change in the values can be detected. The possible association between measurement error and disease severity was also investigated to evaluate the need for limits based on disease severity. METHODS:Sixty-one eyes were studied in 61 patients with keratoconus. Four replicate measurements were made in each patient using a Scheimpflug-based tomographic system (denoted the PC) and an auto-keratometer (denoted the AK). The repeatability coefficient, i.e., the level below which differences between two measurements are found in 95% of paired observations, was calculated. Patients were further divided into three groups based on disease severity (parameter magnitude). RESULTS:Increasing magnitude of all the keratometric parameters investigated was significantly associated with increasing measurement errors, and thus worse repeatability. The maximum keratometry value (Kmax) was the least repeatable parameter (1.23 D, 95% CI 1.11-1.35 D) and showed the strongest association between parameter magnitude and measurement error. The repeatability coefficient ranged between 0.32 and 1.62 D, depending on disease severity. The most repeatable parameter was the flattest central keratometry value (K1), measured with the PC (0.51 D, 95% CI 0.46-0.56 D) and the AK (0.54 D, 95% CI 0.48-0.59 D). K1 showed the weakest association between parameter magnitude and measurement error. The repeatability coefficient for K1 ranged between 0.40 and 0.54 D when using the PC, and between 0.34 and 0.70 D when using the AK in the three groups. CONCLUSIONS:The association between the magnitude of the keratometric parameters and their measurement errors suggests that limits should be based on disease severity to ensure reliable detection of progressive keratoconus. Further studies are, however, required.

Dataset Information

Association between Corneal Stiffness Parameter at the First Applanation and Keratoconus Severity.

Objective

Methods

Conclusions

Publications

Association between Corneal Stiffness Parameter at the First Applanation and Keratoconus Severity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets