Purpose: To evaluate the effect of smoking on retinal thickness and macular and peripapillary vascular density in thyroid eye disease (TED). Methods: In this cross-sectional study, subjects diagnosed with TED were analyzed in three groups: smokers, passive smokers, and non-smokers. Ganglion cell complex thickness, total retinal thickness, macular superficial vascular plexus densities, deep vascular plexus densities, optic nerve head, and radial peripapillary capillary density were measured in each group. Results: Twenty-two eyes (21.6%) of active smokers, 11 eyes (10.8%) of passive smokers, and 69 eyes (67.6%) of non-smokers constitute the study subjects. Twenty-one eyes (12.6%) had active status (clinical activity score ≥3), 77 eyes (46.1%) were neither active nor compressive, four eyes (2.4%) of two patients constituted the compressive group. Age and disease activity adjusted analysis was performed. Ganglion cell complex thickness of smokers was significantly higher than non-smokers in the inferior hemi-parafoveal sector (p = 0.04). Active smokers had significantly higher (p < 0.01) retinal thickness in all sectors compared to non-smokers, except the foveal sector. Smokers had lower superficial vessel density in the superior parafoveal sector compared to non-smokers (p = 0.04). Considering deep vessel densities between smokers and non-smokers, no significantdifference was observed. Radial peripapillary capillary densities (significant difference was observed in the whole image and infranasal peripapillary sector), Macular vascular densities (significant difference was observed in parafoveal sectors), and optic nerve head (not reaching statistical significance level in any sectors) were highest in passive smokers. Conclusions Smoking is associated with increased total retinal thickness. Macular vascular densities were not different between smokers and non-smokers in TED.

Purpose: To evaluate the effect of smoking on retinal thickness and macular and peripapillary vascular density in thyroid eye disease (TED). Methods: In this cross-sectional study, subjects diagnosed with TED were analyzed in three groups: smokers, passive smokers, and non-smokers. Ganglion cell complex thickness, total retinal thickness, macular superficial vascular plexus densities, deep vascular plexus densities, optic nerve head, and radial peripapillary capillary density were measured in each group. Results: Twenty-two eyes (21.6%) of active smokers, 11 eyes (10.8%) of passive smokers, and 69 eyes (67.6%) of non-smokers constitute the study subjects. Twenty-one eyes (12.6%) had active status (clinical activity score ≥3), 77 eyes (46.1%) were neither active nor compressive, four eyes (2.4%) of two patients constituted the compressive group. Age and disease activity adjusted analysis was performed. Ganglion cell complex thickness of smokers was significantly higher than non-smokers in the inferior hemi-parafoveal sector (p = 0.04). Active smokers had significantly higher (p < 0.01) retinal thickness in all sectors compared to non-smokers, except the foveal sector. Smokers had lower superficial vessel density in the superior parafoveal sector compared to non-smokers (p = 0.04). Considering deep vessel densities between smokers and non-smokers, no significantdifference was observed. Radial peripapillary capillary densities (significant difference was observed in the whole image and infranasal peripapillary sector), Macular vascular densities (significant difference was observed in parafoveal sectors), and optic nerve head (not reaching statistical significance level in any sectors) were highest in passive smokers. Conclusions Smoking is associated with increased total retinal thickness. Macular vascular densities were not different between smokers and non-smokers in TED.

Purpose: To compare intraocular pressure (IOP) readings from corneas with intracorneal corneal ring segments (ICRS) using various methods, including Goldmann applanation tonometry (GAT), Tonopen, corneal-compensated IOP from the Ocular Response Analyzer (ORA), and biomechanically corrected IOP from the Corneal Visualization Scheimpflug Technology (Corvis ST). Methods: This cross-sectional observational study included participants who had undergone ICRS implantation with KeraRing at least 3 months before the study. The mean IOP recorded by different instruments was compared using analysis of variance. Agreement among the methods was assessed with Bland-Altman plots. Results: A total of 54 eyes from 27 participants were enrolled. The mean IOP measured by Tonopen was significantly lower in the center compared to the peripheral quadrants (p < 0.001). IOP measured by GAT was significantly lower than that measured by Tonopen (13.02 ± 2.31 mmHg vs. 14.50 ± 2.91 mmHg, p = 0.021). There were no significant differences between the IOP measurements provided by Tonopen, ORA, and Corvis ST. The corneal-compensated IOP from ORA and biomechanically corrected IOP from Corvis ST had the highest correlation, with a weak intraclass correlation coefficient of 0.38. Conclusions IOP measurements using Tonopen were significantly lower in the central 5-mm zone compared to other quadrants. GAT measurements were significantly lower than those from Tonopen. Different measurement tools did not show a strong correlation. Corvis ST (biomechanically corrected IOP) tended to present lower readings at higher IOP levels in eyes with ICRS.

Purpose: To compare intraocular pressure (IOP) readings from corneas with intracorneal corneal ring segments (ICRS) using various methods, including Goldmann applanation tonometry (GAT), Tonopen, corneal-compensated IOP from the Ocular Response Analyzer (ORA), and biomechanically corrected IOP from the Corneal Visualization Scheimpflug Technology (Corvis ST). Methods: This cross-sectional observational study included participants who had undergone ICRS implantation with KeraRing at least 3 months before the study. The mean IOP recorded by different instruments was compared using analysis of variance. Agreement among the methods was assessed with Bland-Altman plots. Results: A total of 54 eyes from 27 participants were enrolled. The mean IOP measured by Tonopen was significantly lower in the center compared to the peripheral quadrants (p < 0.001). IOP measured by GAT was significantly lower than that measured by Tonopen (13.02 ± 2.31 mmHg vs. 14.50 ± 2.91 mmHg, p = 0.021). There were no significant differences between the IOP measurements provided by Tonopen, ORA, and Corvis ST. The corneal-compensated IOP from ORA and biomechanically corrected IOP from Corvis ST had the highest correlation, with a weak intraclass correlation coefficient of 0.38. Conclusions IOP measurements using Tonopen were significantly lower in the central 5-mm zone compared to other quadrants. GAT measurements were significantly lower than those from Tonopen. Different measurement tools did not show a strong correlation. Corvis ST (biomechanically corrected IOP) tended to present lower readings at higher IOP levels in eyes with ICRS.

Purpose: To compare intraocular pressure (IOP) readings from corneas with intracorneal corneal ring segments (ICRS) using various methods, including Goldmann applanation tonometry (GAT), Tonopen, corneal-compensated IOP from the Ocular Response Analyzer (ORA), and biomechanically corrected IOP from the Corneal Visualization Scheimpflug Technology (Corvis ST). Methods: This cross-sectional observational study included participants who had undergone ICRS implantation with KeraRing at least 3 months before the study. The mean IOP recorded by different instruments was compared using analysis of variance. Agreement among the methods was assessed with Bland-Altman plots. Results: A total of 54 eyes from 27 participants were enrolled. The mean IOP measured by Tonopen was significantly lower in the center compared to the peripheral quadrants (p < 0.001). IOP measured by GAT was significantly lower than that measured by Tonopen (13.02 ± 2.31 mmHg vs. 14.50 ± 2.91 mmHg, p = 0.021). There were no significant differences between the IOP measurements provided by Tonopen, ORA, and Corvis ST. The corneal-compensated IOP from ORA and biomechanically corrected IOP from Corvis ST had the highest correlation, with a weak intraclass correlation coefficient of 0.38. Conclusions IOP measurements using Tonopen were significantly lower in the central 5-mm zone compared to other quadrants. GAT measurements were significantly lower than those from Tonopen. Different measurement tools did not show a strong correlation. Corvis ST (biomechanically corrected IOP) tended to present lower readings at higher IOP levels in eyes with ICRS.

Purpose: To compare intraocular pressure (IOP) readings from corneas with intracorneal corneal ring segments (ICRS) using various methods, including Goldmann applanation tonometry (GAT), Tonopen, corneal-compensated IOP from the Ocular Response Analyzer (ORA), and biomechanically corrected IOP from the Corneal Visualization Scheimpflug Technology (Corvis ST). Methods: This cross-sectional observational study included participants who had undergone ICRS implantation with KeraRing at least 3 months before the study. The mean IOP recorded by different instruments was compared using analysis of variance. Agreement among the methods was assessed with Bland-Altman plots. Results: A total of 54 eyes from 27 participants were enrolled. The mean IOP measured by Tonopen was significantly lower in the center compared to the peripheral quadrants (p < 0.001). IOP measured by GAT was significantly lower than that measured by Tonopen (13.02 ± 2.31 mmHg vs. 14.50 ± 2.91 mmHg, p = 0.021). There were no significant differences between the IOP measurements provided by Tonopen, ORA, and Corvis ST. The corneal-compensated IOP from ORA and biomechanically corrected IOP from Corvis ST had the highest correlation, with a weak intraclass correlation coefficient of 0.38. Conclusions IOP measurements using Tonopen were significantly lower in the central 5-mm zone compared to other quadrants. GAT measurements were significantly lower than those from Tonopen. Different measurement tools did not show a strong correlation. Corvis ST (biomechanically corrected IOP) tended to present lower readings at higher IOP levels in eyes with ICRS.

Purpose: To compare intraocular pressure (IOP) readings from corneas with intracorneal corneal ring segments (ICRS) using various methods, including Goldmann applanation tonometry (GAT), Tonopen, corneal-compensated IOP from the Ocular Response Analyzer (ORA), and biomechanically corrected IOP from the Corneal Visualization Scheimpflug Technology (Corvis ST). Methods: This cross-sectional observational study included participants who had undergone ICRS implantation with KeraRing at least 3 months before the study. The mean IOP recorded by different instruments was compared using analysis of variance. Agreement among the methods was assessed with Bland-Altman plots. Results: A total of 54 eyes from 27 participants were enrolled. The mean IOP measured by Tonopen was significantly lower in the center compared to the peripheral quadrants (p < 0.001). IOP measured by GAT was significantly lower than that measured by Tonopen (13.02 ± 2.31 mmHg vs. 14.50 ± 2.91 mmHg, p = 0.021). There were no significant differences between the IOP measurements provided by Tonopen, ORA, and Corvis ST. The corneal-compensated IOP from ORA and biomechanically corrected IOP from Corvis ST had the highest correlation, with a weak intraclass correlation coefficient of 0.38. Conclusions IOP measurements using Tonopen were significantly lower in the central 5-mm zone compared to other quadrants. GAT measurements were significantly lower than those from Tonopen. Different measurement tools did not show a strong correlation. Corvis ST (biomechanically corrected IOP) tended to present lower readings at higher IOP levels in eyes with ICRS.