Anterior Chamber Angle, Volume, and Depth in a Normative Cohort-A Retrospective Cross-Sectional Study.

Purpose: The purpose of the study was to determine the distribution of the anterior chamber angle (ACA) within a population-based study sample in Germany and to find correlations between age, sphere, and biometric parameters of the anterior chamber. Patients and Methods: A total of 500 eyes, approximately 100 eyes per decade starting with patient age of 20 years, of 463 patients with an average age of 45.2 ± 14.1 (±values subsequent represent standard deviation) years without any known history of ocular diseases, surgery, or optic nerve head excavation or hypoplasia were included. ACAs, volume, and depth were correlated to age and sphere. Scheimpflug images (Pentacam, Oculus) with automatically measured ACAs were compared to manually measured angles (Bland Altman analysis) in this healthy population. Results: The mean manually measured ACA was 26.5° ± 3.9°; the highest average angle was found in the temporal position with 28.1° ± 4.9°, while the lowest average angle was found in nasal superior position with 25.7° ± 4.7°. Statistical analysis showed an average difference of +11.4° nasal and +12.1° temporal between the automatic measurements and the manually measured angles (P < 0.01). The analysis also revealed an independent inverted correlation between age (correlation coefficient between -0.28 and -0.38) and sphere (correlation coefficient between -0.44 and -0.51) of the participants and the anterior chamber volume, angle, and anterior chamber depth (P < 0.01 for all correlations). Conclusion: The ACA width manually measured is considerably less compared to automated imaging and formerly reported values. There is a significant difference in the ACA dependent on the position of measurement (superior, nasal, inferior, and temporal) with the average angle being inversely correlated to age and sphere. Abbreviations: AC: anterior chamber ACA: anterior chamber angle ACV: anterior chamber volume ACD: anterior chamber depth AAC: acute angle closure OAG: open-angle glaucoma OCT: optical coherence tomography ACG: angle-closure glaucoma MIGS: microinvasive glaucoma surgery PACS: primary angle-closure suspects.

Evaluation of keratoconus progression.

AIM: To define variables for the evaluation of keratoconus progression and to determine cut-off values. METHODS: In this retrospective cohort study (2010-2016), 265 eyes of 165 patients diagnosed with keratoconus underwent two Scheimpflug measurements (Pentacam) that took place 1 year apart ±3 months. Variables used for keratoconus detection were evaluated for progression and a correlation analysis was performed. By logistic regression analysis, a keratoconus progression index (KPI) was defined. Receiver-operating characteristic curve (ROC) analysis was performed and Youden Index calculated to determine cut-off values. RESULTS: Variables used for keratoconus detection showed a weak correlation with each other (eg, correlation r=0.245 between RPImin and Kmax, p<0.001). Therefore, we used parameters that took several variables into consideration (eg, D-index, index of surface variance, index for height asymmetry, KPI). KPI was defined by logistic regression and consisted of a Pachymin coefficient of -0.78 (p=0.001), a maximum elevation of back surface coefficient of 0.27 and coefficient of corneal curvature at the zone 3 mm away from the thinnest point on the posterior corneal surface of -12.44 (both p<0.001). The two variables with the highest Youden Index in the ROC analysis were D-index and KPI: D-index had a cut-off of 0.4175 (70.6% sensitivity) and Youden Index of 0.606. Cut-off for KPI was -0.78196 (84.7% sensitivity) and a Youden Index of 0.747; both 90% specificity. CONCLUSIONS: Keratoconus progression should be defined by evaluating parameters that consider several corneal changes; we suggest D-index and KPI to detect progression.

Early Tomographic Changes in the Eyes of Patients With Keratoconus.

PURPOSE: To identify tomographic variables best suited for detecting keratoconus before manifestation of ectatic changes and showing disease progression in the early stage. METHODS: Twenty-seven patients with diagnosed unilateral keratoconus were followed up for their fellow eye, which had not yet shown any ectatic changes, to determine initial change indicators toward keratoconus disease. Variables were compared to 50 normal eyes without any known disease. A following receiver operating characteristic (ROC) analysis was performed to reveal the variables best used to discriminate healthy eyes from early ectatic eyes. RESULTS: The calculated mean difference of the cylinder for total corneal refractive power was only 0.07 ± 0.32 diopters (D) (anterior astigmatism = 0.12 ± 0.28 D and posterior astigmatism = 0.02 ± 0.10 D). ROC revealed the index of height decentration with an area under the curve of 0.788 ± 0.054 as the most suitable to differentiate between eyes of healthy patients and the non-ectatic eye of patients with asymmetrical keratoconus, followed by the index of vertical asymmetry of 0.772 ± 0.057 and a keratoconus index of 0.743 ± 0.062. However, with progression of the eyes into early ectactic stages, the ROC showed the highest area under the curve for D-index (0.876 ± 0.039), followed by index of height decentration (0.855 ± 0.046) and index of vertical asymmetry (0.842 ± 0.046). CONCLUSIONS: Early stages of keratoconus are hard to diagnose and best results can be achieved by using index of height decentration and index of vertical asymmetry. As the disease progresses, D-index is better suited to diagnose an ectasia. Astigmatism, keratometry, and pachymetry barely change in the early stages, so these values are not as fitting as corneal elevation parameters for early diagnosis. [J Refract Surg. 2018;34(4):254-259.].