April 2014
Volume 55, Issue 13
ARVO Annual Meeting Abstract  |   April 2014
Automated Assessment of Episcleral Venous Pressure during Venomanometry
Author Affiliations & Notes
  • Jay W McLaren
    Ophthalmology, Mayo Clinic, Rochester, MN
  • Arthur J Sit
    Ophthalmology, Mayo Clinic, Rochester, MN
  • Footnotes
    Commercial Relationships Jay McLaren, None; Arthur Sit, AcuMEMS, Inc. (C), Allergan, Inc. (C), Glaukos Corp. (C), Sensimed, AG (C), Sucampo Pharmaceuticals, Inc. (C)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 2912. doi:
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      Jay W McLaren, Arthur J Sit; Automated Assessment of Episcleral Venous Pressure during Venomanometry. Invest. Ophthalmol. Vis. Sci. 2014;55(13):2912.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose: Episcleral venous pressure (EVP) is determined from the applied pressure that begins to collapse an episcleral vein. However, identifying this endpoint from a video and pressure recording during venomanometry is time consuming. In this study we developed an automated method to determine the pressure that initiates vascular collapse.

Methods: EVP was measured by using a custom computer-controlled episcleral venomanometer in 57 eyes from 29 normal participants. An episcleral vein was recorded by video microscopy through a transparent silicone bulb over the vein while the bulb was inflated linearly to 22 mmHg. After low-pass and homogenization filtering of the green (red-free) image channel in each frame, the average brightness profile across a 0.5 - 1 mm segment of vein was determined by using a custom program, and the peak brightness difference from the center to the edge of the vessel was recorded. A sigmoid function was fitted to the peak brightness vs applied pressure and EVP was assumed to be the pressure where this curve decreased to 0.935 of the initial peak brightness (automated method). EVP was also determined manually by identifying the initial stable peak brightness and the pressure at the beginning of the decrease of peak brightness, determined by back-projection. The automated EVP was compared with manual EVP by using generalized estimating equation models to account to for possible correlation between fellow eyes of the same subjects. The relationship between methods was illustrated by Pearson correlation, and limits of agreement were expressed as the mean difference ± 2 standard deviations of the difference.

Results: Mean EVP was 6.6 ± 2.9 mmHg (± SD) and 6.7 ± 2.7 mmHg by using the automated and manual methods respectively (p=0.46). The two methods were correlated (r=0.96, p<0.001); the mean difference was -0.1 ± 0.8 mmHg and the limits of agreement were -1.7 to 1.5 mmHg. The pattern of vascular collapse was not consistent among trials. A biphasic collapse with an initial slow phase followed by a fast phase was noted in 22 of 57 trials. Vessel brightness was variable during the initial stable phase in 36 trials and during the transition in 13 trials.

Conclusions: Use of the fitted sigmoid curve provides a fast and accurate method to determine EVP from the vessel brightness in episcleral venomanometry. However, variability of vascular collapse must be considered in all methods of analysis.

Keywords: 421 anterior segment • 427 aqueous • 549 image processing  

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