Quantitative and Qualitative Assessments of Retinal Structure with Variable A-Scan Rate Spectralis OCT: Insights into IPL Multilaminarity

The aim of this study was to evaluate the qualitative and quantitative differences between 20 and 85 kHz A-scan rate optical coherence tomography (OCT) images acquired by spectral domain OCT. The study included 60 healthy subjects analyzed with horizontal linear scans with a variable A-scan rate (SHIFT technology, Heidelberg Engineering, Heidelberg, Germany). The retinal thickness measurement of each retinal layer was performed in three different positions (subfoveal, nasal, and temporal). The qualitative assessment was performed by two independent observers who rated every image with a score ranging from 1 ("sufficient") to 3 ("excellent") on the basis of three parameters: visualization of the vitreo-retinal interface, characterization of the retinal layers, and visualization of the sclero-choroidal interface. No statistically significant differences in terms of retinal layer thickness between the two A-scan rate scans were observed (p > 0.05). The coefficient of variation of the retinal thickness values was lower in the 20 kHz group (25.8% versus 30.1% with the 85 kHz). The 20 kHz images showed a higher quality index for both observers. An inner plexiform layer (IPL) multilaminarity was detected in 78.3% of patients from the 20 kHz group and in 40% of patients from the 85 kHz group (p < 0.05)

Loglinear spatial factor analysis: an application to diabetes mellitus complications

The investigation of spatial variation in disease rates is a standard epidemiological practice used to describe the geographic clustering of diseases which is helpful for making hypotheses about the possible `factors' responsible for differences in risk. Up to the most recent statistical and computational developments, studies have almost entirely focused on the spatial modeling of univariate distributions of cases, that is, on the spatial modeling of single diseases. However, many diseases show similar patterns of geographical variation which may suggest the existence of common underlying risk factors, might these be related to the environment, to particular local food habits, or to the clustering of a particular population (genetic origin). In this work, for multivariate categorical data pointwise geo-referenced in a `geostatistical' fashion, we propose a model for the study of the joint spatial variation of more diseases. Our approach is based on a hierarchical (generalized linear mixed) multivariate model where the underlying latent structure is given by a Gaussian geostatistical spatial factor model. The methodology proposed can be seen as an extension of the geostatistical linear model of coregionalization, and of the related `factorial kriging analysis', to the case of geo-referenced, in general multi-way, contingency tables. An application of the proposed methodology is shown on an epidemiological data set coming from an extensive survey on diabetes mellitus patients which involved the majority of the family practitioners of the region of Umbria in central Italy in 1990. Attention is centered on the study of nephropathy and retinopathy, two of the chronic diabetic complications affecting life quality and expectancy

Disease risk mapping: a multivariate geostatistical approach

Disease mapping methods for the modelling of spatial variation in disease rates, to smooth the extreme raw rates in small areas by using information from neighboring areas, has so far almost entirely been concerned with random-effects models embodying some sort of Markov random field structure. These models consider the distribution of the relative risk of an area conditional on that of its neighbors. Typically, two areas are viewed as neighbors if they share a common boundary, regardless of their relative position, size and shape. In recent years, following a geostatistical approach, some authors have advocated more realistic models in which the spatial autocorrelation structure of the disease counts is derived through an underlying risk varying smoothly over the entire region of interest. In particular, by explicitly modelling the population density over the region of interest, these authors have considered (log) standardized relative risk as a Gaussian random field. In this work, avoiding the direct modelling of the population density over the region of interest, a different geostatistical approach to the study of spatial variation of disease risk is proposed. Our approach assumes that the raw data are available not, as in the usual practice, in the form of aggregate counts within sets of disjoint politically defined areas, but in a pointwise "geostatistical" fashion. Extending the proposal made in the literature for the univariate case, which just consider non standardized (by age, sex, etc.) risk, in this work we present an approach to disease mapping based on a hierarchical multivariate spatial model in which the latent structure is given by a Gaussian geostatistical factor model. The methodology proposed is shown on an epidemiological data set coming from an extensive survey on diabetes mellitus in Umbria (Italy)

Multivariate disease risk mapping: a geostatistical approach

Disease mapping methods for the modeling of spatial variation in disease rates, to smooth the extreme raw rates in small areas by using information from neighboring areas, has so far almost entirely been concerned with random-effects models embodying some sort of Markov random field structure. These models consider the distribution of the relative risk of an area conditional on that of its neighbors. Typically, two areas are viewed as neighbors if they share a common boundary, regardless of their relative position, size and shape. In recent years, following a geostatistical approach, some authors have advocated more realistic models in which the spatial autocorrelation structure of the disease counts is derived through an underlying risk varying smoothly over the entire region of interest. In particular, some of these authors, by explicitly modeling the population density over the region of interest, have considered the (log) standardized relative risk as a Gaussian random field. Here, avoiding the direct modeling of the population density over the region of interest, a different multivariate geostatistical approach to the study of spatial variation of disease risk, which may find application in many real situations, is proposed. Our approach assumes that the raw data are available not, as in the usual practice, in the form of aggregate counts within sets of disjoint politically defined areas, but in a pointwise "geostatistical" fashion. Moreover, the approach proposed allows the study of the spatial distribution of more than one disease at a time

Repeatability of Retinal Macular Thickness Measurements in Healthy Subjects and Diabetic Patients with Clinically Significant Macular Edema: Evaluation of the Follow-Up System of Spectralis Optical Coherence Tomography

Background: It was the aim of this study to compare the repeatability of Spectralis optical coherence tomography (OCT) retinal thickness measurements in healthy subjects and diabetic patients with clinically significant macular edema (CSME) with or without the use of the follow-up system. Methods: Thirty-eight eyes of 38 healthy subjects (control group) and 68 eyes of 68 diabetic patients with CSME were included in the study. The coefficient of repeatability (CR) and intrasession coefficients of variation were tested with 20 x 15 degree raster scans consisting of 19 high-resolution line scans (15 frames per scan) that were repeated 3 times by 1 experienced examiner.The first scan was set as the reference scan, whereas the second and third scans were the follow-up scans and were performed with and without the use of the follow-up mode, respectively. Results: The means and standard deviations for the central foveal subfield (CSF) in healthy subjects and diabetic patients were 289 +/- 21 and 402 +/- 105 mu m, respectively. Particularly in diabetic patients, examining the CSF, CR was 2.67% (10.73 mu m) and 6.73% (27.01 mu m) with and without using the follow-up mode, respectively, and the difference was statistically significant (p < 0.05). Conclusion: These results support the hypothesis that the follow-up system improves the repeatability either in healthy subjects or in diabetic patients with poor fixation. The wider improvement in repeatability in diabetic patients in the follow-up system group compared to the no follow-up system group are probably related to poor patient fixation or eye movement in patients with CSME. (C) 2015 S. Karger AG, Base

Repeatability of Retinal Macular Thickness Measurements in Patients with Clinically Significant Macular Edema Using Two Different Scanning Protocols of Spectralis Optical Coherence Tomography

Objective: To determine the repeatability of Spectralis optical coherence tomography (OCT) retinal thickness measurements in diabetic patients with clinically significant macular edema (CSME) using two different scanning protocols. Methods: Seventy-one eyes of 71 diabetic patients with CSME were included in the study. Coefficients of repeatability and intrasession variation coefficients were tested with 20 × 15 degree raster scans consisting of 19 or 37 high-resolution line scans (15 or 8 frames per scan, respectively) that were repeated 2 times by 1 experienced examiner. The first scan was set as the reference scan; the second scan was the follow-up scan and was performed with the use of the follow-up mode. Results: The mean and standard deviation for the central foveal subfield (CSF) using the first scanning method was 404 ± 88 μm, while it was 399 ± 86 μm using the second protocol, which was not statistically significantly different (p = 0.35, paired test). Particularly examining the CSF, the coefficient of repeatability was 1.48 (6.00 μm) and 1.49 (5.95 μm) for the 19-and the 37-B-scan acquisition, respectively, showing a nonstatistically significant difference (p < 0.001). Conclusions: Retinal thickness measurements in diabetic patients with CSME are repeatable using both scanning protocols (19 or 37 B-scans) with Spectralis OCT. The repeatability of the retinal thickness measurement does not improve by increasing the number of B-scans from 19 to 37. © 2015 S. Karger AG, Basel