Synergic effect of corneal hysteresis and central corneal thickness in the risk of early-stage primary open-angle glaucoma progression.

PURPOSE: To evaluate corneal hysteresis (CH), acquired with ocular response analyzer (ORA), as a risk factor for glaucoma progression in early-stage primary open-angle glaucoma (POAG). METHODS: In a historical cohort study, patients diagnosed in 2011 with early-stage POAG according to the Hodapp, Parrish and Anderson classification modified for Octopus perimetry and followed up until glaucomatous progression development; otherwise, observations were censored in October 2018. Cox regression was used to obtain hazard ratios (HR) to evaluate baseline variables (CH, central corneal thickness, gender, age IOP and glaucoma family history) as risk factors for perimetric glaucoma progression. A likelihood ratio test for interaction was performed in order to assess the effect of the combination of CH and CCT on the risk of progression. RESULTS: Of the cohort of 1573 patients, 11.38% developed early-stage POAG progression during the follow-up. The mean follow-up time was 3.28 ± 1.92 years. Patients without progression had a higher CH (11.35 ± 1.43 vs 9.07 ± 1.69 mmHg; p < 0.001) and CCT (570.75 ± 17.71 vs 554.51 ± 23.20; p < 0.001). In the multivariate analysis, each 1 mmHg of lower CH was associated with an increase of 2.13 times in the HR of progression (95% CI: 1.92-2.32; p < 0.001). CH hazard ratio was modified by CCT, with higher values of CCT and CH resulting in a higher HR of early glaucoma progression (p < 0.001). CONCLUSIONS: CH can be considered as a risk factor of progression in early-stage POAG. The risk associated with CH changed depending on CCT values, acting synergistically slowing the risk of glaucoma progression with higher values.

Reliability of blood test results in samples obtained using a 2-mL discard volume from the proximal lumen of a triple-lumen central venous catheter in the critically ill patient.

BACKGROUND: Intensive care patients require a high frequency of blood testing, which results in a significant amount of blood loss. When blood is obtained from a central venous catheter (CVC), a large volume is usually discarded to obtain an unaltered sample for testing. AIM: To determine the reliability of complete blood test results in blood samples obtained from the proximal lumen of a triple-lumen CVC using a 2-mL discard volume DESIGN: Observational study with the prospective collection of data METHODS: The subjects enrolled were all patients with a subclavian triple-lumen CVC, older than 17 years and consecutively admitted to intensive care over a 2-year period. In each of the 54 participants, one blood sample was drawn from the proximal lumen of the catheter, discarding 1·61 mL of blood plus 0·39 mL of catheter deadspace (2 mL) and without interrupting infusion in the middle and distal lumens. A second sample was then obtained by direct venous puncture. The reliability of blood test results was determined by comparing sets of variables recorded for the two sampling methods through intraclass correlation coefficients in the Bland-Altman method. RESULTS: Inter-method reliability for the variables examined was excellent, >0·75; range (0·868-0·998). Mean differences between the two sample types for the variables most often determined in critically ill patients were leukocytes: 0·200 × 103 /µL, 95% confidence interval (CI) (0·025 to 0·375); erythrocytes: 0·045 × 106 /uL, 95% CI (-0·003 to 0·094); sodium: 0·074 mEq/L, 95% CI (-0·369 to 0·517); potassium: -0·002mEq/L, 95% CI (-0·065 to 0·061) and glucose: 2·426 mg/dL, 95% CI (0·498-4·354). CONCLUSIONS: The sampling method proposed minimizes blood loss while offering reliable blood test results. RELEVANCE TO CLINICAL PRACTICE: The main benefit of the method proposed is reduced blood loss, improving the care of a critically ill patient.

Roughness of retinal layers in Alzheimer's disease.

There is growing evidence that thinned retinal regions are interspersed with thickened regions in all retinal layers of patients with Alzheimer's disease (AD), causing roughness to appear on layer thickness maps. The hypothesis is that roughness of retinal layers, assessed by the fractal dimension (FD) of their thickness maps, is an early biomarker of AD. Ten retinal layers have been studied in macular volumes of optical coherence tomography from 24 healthy volunteers and 19 patients with mild AD (Mini-Mental State Examination 23.42 ± 3.11). Results show that FD of retinal layers is greater in the AD group, the differences being statistically significant (p < 0.05). Correlation of layer FD with cognitive score, visual acuity and age reach statistical significance at 7 layers. Nearly all (44 out of 45) FD correlations among layers are positive and half of them reached statistical significance (p < 0.05). Factor analysis unveiled two independent factors identified as the dysregulation of the choroidal vascular network and the retinal inflammatory process. Conclusions: surface roughness is a holistic feature of retinal layers that can be assessed by the FD of their thickness maps and it is an early biomarker of AD.

Spatial analysis of thickness changes in ten retinal layers of Alzheimer's disease patients based on optical coherence tomography.

The retina is an attractive source of biomarkers since it shares many features with the brain. Thickness differences in 10 retinal layers between 19 patients with mild Alzheimer's disease (AD) and a control group of 24 volunteers were investigated. Retinal layers were automatically segmented and their thickness at each scanned point was measured, corrected for tilt and spatially normalized. When the mean thickness of entire layers was compared between patients and controls, only the outer segment layer of patients showed statistically significant thinning. However, when the layers were compared point-by point, patients showed statistically significant thinning in irregular regions of total retina and nerve fiber, ganglion cell, inner plexiform, inner nuclear and outer segment layers. Our method, based on random field theory, provides a precise delimitation of regions where total retina and each of its layers show a statistically significant thinning in AD patients. All layers, except inner nuclear and outer segments, showed thickened regions. New analytic methods have shown that thinned regions are interspersed with thickened ones in all layers, except inner nuclear and outer segments. Across different layers we found a statistically significant trend of the thinned regions to overlap and of the thickened ones to avoid overlapping.

Three-Dimensional Reconstruction of the Bony Nasolacrimal Canal by Automated Segmentation of Computed Tomography Images.

OBJECTIVE: To apply a fully automated method to quantify the 3D structure of the bony nasolacrimal canal (NLC) from CT scans whereby the size and main morphometric characteristics of the canal can be determined. DESIGN: Cross-sectional study. SUBJECTS: 36 eyes of 18 healthy individuals. METHODS: Using software designed to detect the boundaries of the NLC on CT images, 36 NLC reconstructions were prepared. These reconstructions were then used to calculate NLC volume. The NLC axis in each case was determined according to a polygonal model and to 2nd, 3rd and 4th degree polynomials. From these models, NLC sectional areas and length were determined. For each variable, descriptive statistics and normality tests (Kolmogorov-Smirnov and Shapiro-Wilk) were established. MAIN OUTCOME MEASURES: Time for segmentation, NLC volume, axis, sectional areas and length. RESULTS: Mean processing time was around 30 seconds for segmenting each canal. All the variables generated were normally distributed. Measurements obtained using the four models polygonal, 2nd, 3rd and 4th degree polynomial, respectively, were: mean canal length 14.74, 14.3, 14.80, and 15.03 mm; mean sectional area 15.15, 11.77, 11.43, and 11.56 mm2; minimum sectional area 8.69, 7.62, 7.40, and 7.19 mm2; and mean depth of minimum sectional area (craniocaudal) 7.85, 7.71, 8.19, and 8.08 mm. CONCLUSION: The method proposed automatically reconstructs the NLC on CT scans. Using these reconstructions, morphometric measurements can be calculated from NLC axis estimates based on polygonal and 2nd, 3rd and 4th polynomial models.