Develop the first normative database of the thickness of every inner retinal layer in the macular area in a healthy, Caucasian population between 18 to 87 years old, using Spectralis Optical Coherence Tomography (OCT). On this transversal, observational study, 300 patients between 18 to 87 years old and without an ophthalmological condition were recruited. Macular OCT scans were performed on all patients (Spectralis OCT, Heidelberg Engineering). An axial length measurement, and keratometry were performed using an optical biometer. The volume and thickness of the different macular sectors of the inner retinal layers (retinal nerve fiber layer (RNFL), ganglion cells layer (CGL) and inner plexiform layer (IPL)) were analyzed with the Spectralis OCT segmentation software. An eye was randomly selected for each patient. 297 patients (179 females and 118 males) were included in the study. The mean age was 56.07 years (range: 40.50–72). The multivariate analysis showed a positive correlation between the RNFL thickness and the axial length (p < 0.001). The mean central retinal thickness was 278.2 μm (range: 266–291), the mean central RNFL thickness was 12.61 μm (range: 11–14), the mean central CGL thickness was 17.63 μm (range: 14–21) and the mean central IPL thickness was 22.02 μm (range: 20–25). The multivariate analysis showed a negative correlation between age and CGL thickness and inner IPL thickness (p< 0.001). This study provides a normative database of the volume of each of the inner retinal layers on a Caucasian population.

Purpose: Charles Bonnet syndrome (CBS) is a condition characterized by development of visual hallucinations in patients with no cognitive impairment and significant loss of vision mainly caused by age-related macular degeneration (AMD) or glaucoma. Methods: This was a study of prevalence and characteristics of CBS diagnosed at the Neuroophthalmic Unit within the Ophthalmology Department of Hospital Clínico San Carlos (HCSC), Madrid, Spain. Results: The CBS prevalence in patients from HCSC Madrid is 0.47%, rising to 15% in patients with low vision. Women over 80 years of age comprised 58.3% of the patients, who mainly had AMD (58.3%). Main characteristics of hallucinations included animals (50%), color (58.3%), moving (75%), 6- to 12-month evolution (50%), three times a day frequency (75%), and 3- to 5-minute duration (50%). Conclusions: Charles Bonnet syndrome is a complex process that must be treated jointly by ophthalmologists, neurologists, and psychiatrists in order to ensure accurate diagnosis and adequate management. New studies are needed in order to improve awareness of clinical manifestation of this condition, the incidence of which is underestimated due to patients' fear of being branded mentally ill, as well as physicians' lack of knowledge about CBS.

Purpose: To compare central corneal thickness (CCT) values obtained by Lenstar (LE), Pentacam (PC), specular microscopy (SM) and ultrasound pachymetry (UP) in healthy corneas and study their influence on intraocular pressure (IOP) readings determined by Goldmann applanation tonometry (GAT). Methods: CCT was measured in 76 healthy subjects by LE, PC, SM and UP. We established Lin's concordance correlation coefficient (ρ-C) between different techniques. The influence of CCT on GAT was established through univariate linear regression models, IOP being the dependent variable. Results: The highest ρ-C was found between LE and SM at 0.94 (95% CI: 0.91-0.96) and between LE and UP at 0.95 (95% CI: 0.94-0.97). IOP readings showed less variability when CCT was determined using LE (7.7%, B = 0.16; 95% CI: 0.004-0.28). Conclusions: Although CCT values obtained with UP, PC, SM and LE show good correlation, these methods are not completely interchangeable. The amount of IOP variation differs when CCT is determined using LE or SM.

Purpose: To examine the diagnostic accuracy and performance of Uvemaster, a mobile application (app) or diagnostic decision support system (DDSS) for uveitis. The app contains a large database of knowledge including 88 uveitis syndromes each with 76 clinical items, both ocular and systemic (total 6688) and their respective prevalences, and displays a differential diagnoses list (DDL) ordered by sensitivity, specificity, or positive predictive value (PPV). Methods: In this retrospective case-series study, diagnostic accuracy (percentage of cases for which a correct diagnosis was obtained) and performance (percentage of cases for which a specific diagnosis was obtained) were determined in reported series of patients originally diagnosed by a uveitis specialist with specific uveitis (N = 88) and idiopathic uveitis (N = 71), respectively. Results: Diagnostic accuracy was 96.6% (95% confidence interval [CI], 93.2–100). By sensitivity, the original diagnosis appeared among the top three in the DDL in 90.9% (95% CI, 84.1–96.6) and was the first in 73.9% (95% CI, 63.6–83.0). By PPV, the original diagnosis was among the top DDL three in 62.5% (95% CI, 51.1–71.6) and the first in 29.5% (95% CI, 20.5–38.6; P < 0.001). In 71 (31.1%) patients originally diagnosed with idiopathic uveitis, 19 new diagnoses were made reducing this series to 52 (22.8%) and improving by 8.3% the new rate of diagnosed specific uveitis cases (performance = 77.2%; 95% CI, 71.1–82.9). Conclusions: Uvemaster proved accurate and based on the same clinical data was able to detect more cases of specific uveitis than the original clinician only–based method.