Advances in ophthalmology practice and research · 2025
The influence of ocular parameters on intraocular pressure values measured by different devices and the development of an optimized prediction model in a myopic population.
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Gan Wei, Zhang Yu, Yang Shizhao, He Peiyun, Hu Qingqing, Du Zhiyu
Аннотация
OBJECTIVE: To investigate the associations between ocular parameters-mean keratometry (Km), spherical equivalent (SE), central corneal thickness (CCT), axial length (AL), corneal hysteresis (CH), and corneal resistance factor (CRF)-and intraocular pressure (IOP) measurements obtained by non-contact tonometry (IOPNCT), Schiøtz tonometry (IOPSchiøtz), dynamic contour tonometry (IOPDCT), corneal-compensated (IOPcc), and Goldmann-correlated IOP (IOPg) in a myopic population. Correlation and regression analyses were performed to identify the IOP measurement method least affected by ocular parameters. An optimized prediction model was then constructed based on conventional tonometry.
METHODS: This study included 64 patients (128 eyes). All participants underwent comprehensive ocular examinations (CCT, AL, SE, Km, CH, and CRF) and IOP measurements using multiple tonometers. Pearson correlation and multiple linear regression analyses were employed to identify key influencers on IOP readings. An optimized prediction model was subsequently developed using NCT, CCT, CH, and CRF as independent variables, and its goodness-of-fit was evaluated.
RESULTS: Age, AL, Km, IOPSchiøtz, IOPDCT, IOPcc, and IOPg showed statistically significant differences between sexes. Multiple regression identified CRF as the strongest positive predictor for all IOP measurements (P < 0.001), while CH was a consistent negative predictor (P < 0.01). Notably, dynamic contour tonometry (DCT) demonstrated the smallest regression coefficients for CH (B = -0.745) and CRF (B = 0.812), indicating minimal influence from corneal parameters. An optimized prediction model was developed: DCT-pred = 18.657 + 0.394 × NCT - 0.008 × CCT - 0.415 × CH + 0.107 × CRF (MSE = 3.30).
CONCLUSIONS: DCT measurements were less influenced by CCT, CH, and CRF, supporting their potential reliability for IOP assessment in patients with myopia. The optimized prediction model based on multiple regression analysis was able to predict DCT values to some extent, thereby reducing the influence of corneal biomechanical properties on IOP measurement. However, the model requires further validation and refinement in studies with larger sample sizes.
METHODS: This study included 64 patients (128 eyes). All participants underwent comprehensive ocular examinations (CCT, AL, SE, Km, CH, and CRF) and IOP measurements using multiple tonometers. Pearson correlation and multiple linear regression analyses were employed to identify key influencers on IOP readings. An optimized prediction model was subsequently developed using NCT, CCT, CH, and CRF as independent variables, and its goodness-of-fit was evaluated.
RESULTS: Age, AL, Km, IOPSchiøtz, IOPDCT, IOPcc, and IOPg showed statistically significant differences between sexes. Multiple regression identified CRF as the strongest positive predictor for all IOP measurements (P < 0.001), while CH was a consistent negative predictor (P < 0.01). Notably, dynamic contour tonometry (DCT) demonstrated the smallest regression coefficients for CH (B = -0.745) and CRF (B = 0.812), indicating minimal influence from corneal parameters. An optimized prediction model was developed: DCT-pred = 18.657 + 0.394 × NCT - 0.008 × CCT - 0.415 × CH + 0.107 × CRF (MSE = 3.30).
CONCLUSIONS: DCT measurements were less influenced by CCT, CH, and CRF, supporting their potential reliability for IOP assessment in patients with myopia. The optimized prediction model based on multiple regression analysis was able to predict DCT values to some extent, thereby reducing the influence of corneal biomechanical properties on IOP measurement. However, the model requires further validation and refinement in studies with larger sample sizes.