Dependence of dynamic contour and Goldmann applanation tonometries on peripheral corneal thickness

Autor: Federico Saenz-Frances, Claudia Sanz-Pozo, Lara Borrego-Sanz, Luis Jañez, Laura Morales-Fernandez, Jose Maria Martinez-de-la-Casa, Julian Garcia-Sanchez, Julian Garcia-Feijoo, Enrique Santos-Bueso
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: International Journal of Ophthalmology, Vol 10, Iss 10, Pp 1521-1527 (2017)
Druh dokumentu: article
ISSN: 2222-3959
2227-4898
DOI: 10.18240/ijo.2017.10.07
Popis: AIM: To determine the effects of peripheral corneal thickness (PCT) on dynamic contour tonometry(DCT) and Goldmann applanation tonometry (GAT). METHODS: A cross-sectional study. We created a software which calculates the corneal contour (CC) as a function of the radius from the corneal apex to each pixel of the contour. The software generates a central circumference with a radius of 1 mm and the remainder of the cornea is segmented in 5 rings concentric with corneal apex being its diameter not constant around the corneal circumference as a consequence of the irregular CC but keeping constant the diameter of each ring in each direction of the contour. PCT was determined as the mean thickness of the most eccentric ring. Locally weighted scatterplot smoothing (LOWESS) regression was used to determine the pattern of the relationship between PCT and both DCT and GAT respectively. Thereafter, two multivariable linear regression models were constructed. In each of them, the dependant variable was intraocular pressure (IOP) as determined using GAT and DCT respectively. In both of the models the predictive variable was PCT though LOWESS regression pattern was used to model the relationship between the dependant variables and the predictor one. Age and sex were also introduced control variables along with their first-degree interactions with PCT. Main outcome measures include amount of IOP variation explained through regression models (R2) and regression coefficients (B). RESULTS: Subjects included 109 eyes of 109 healthy individuals. LOWESS regression suggested that a 2nd-degree polynomial would be suitable to model the relationship between both DCT and GAT with PCT. Hence PCT was introduced in both models as a linear and quadratic term. Neither age nor sex nor interactions were statistically significant in both models. For GAT model, R2 was 17.14% (F=9.02; P=0.0002), PCT linear term B was -1.163 (95% CI: -1.163, -0.617). PCT quadratic term B was 0.00081 (95% CI: 0.00043, 0.00118). For DCT model R2 was 14.28% (F=9.29; P=0.0002), PCT linear term B was -0.712 (95% CI: -1.052, -0.372), PCT quadratic term was B=0.0005 (95% CI: 0.0003, 0.0007). CONCLUSION: DCT and GAT measurements are conditioned by PCT though this effect, rather than linear, follows a 2nd-degree polynomial pattern.
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