Thomas Olsen1. 1. University Eye Clinic, Aarhus Kommunehospital, Aarhus, Denmark. tkolson@dadlnet.dk
Abstract
PURPOSE: To investigate methods to predict the effective postoperative anterior chamber depth (ACD) based on a large patient sample. SETTING: University Eye Clinic, Aarhus Kommunehospital, Aarhus, Denmark. METHODS: Based on 6698 consecutive cataract operations with recorded postoperative refractive results, the postoperative effective ACD was calculated in each case and studied by multiple linear regression for covariance with a number of preoperatively defined variables including the axial length by ultrasonography, preoperative ACD, lens thickness, corneal radius by keratometry, subjective refraction, patient age, and corneal white-to-white diameter, the latter of which was available in a subgroup of 900 cases. RESULTS: The postoperative effective ACD was significantly correlated with 6 preoperative variables (in decreasing order): axial length, preoperative ACD, keratometry reading, lens thickness, refraction, and patient age (R = 0.49, P < .000001). Age showed the weakest correlation (P = .02) and could be omitted with no significant decrease in the total correlation coefficient. Using the 5 most significant variables, the ACD could be predicted according to a regression formula with an accuracy of 82.1% of the predictions within 0.5 mm. When this ACD algorithm was used in retrospect in the intraocular lens (IOL) power calculation, the refractive prediction error decreased by 10% from the error associated with a previously published 4-variable algorithm and decreased 28% from the error using no individual ACD method other than the average ACD (P < .00001). CONCLUSIONS: The postoperative ACD was significantly correlated with and hence predictable by a 5-variable regression method incorporating the preoperative axial length, ACD, keratometry reading, lens thickness, and refraction as the most significant variables. The statistical relationship can be used to create a new ACD prediction algorithm to incorporate in a modern "thick lens" IOL power calculation formula with significant improvement in the accuracy of the refractive predictions as a result.
PURPOSE: To investigate methods to predict the effective postoperative anterior chamber depth (ACD) based on a large patient sample. SETTING: University Eye Clinic, Aarhus Kommunehospital, Aarhus, Denmark. METHODS: Based on 6698 consecutive cataract operations with recorded postoperative refractive results, the postoperative effective ACD was calculated in each case and studied by multiple linear regression for covariance with a number of preoperatively defined variables including the axial length by ultrasonography, preoperative ACD, lens thickness, corneal radius by keratometry, subjective refraction, patient age, and corneal white-to-white diameter, the latter of which was available in a subgroup of 900 cases. RESULTS: The postoperative effective ACD was significantly correlated with 6 preoperative variables (in decreasing order): axial length, preoperative ACD, keratometry reading, lens thickness, refraction, and patient age (R = 0.49, P < .000001). Age showed the weakest correlation (P = .02) and could be omitted with no significant decrease in the total correlation coefficient. Using the 5 most significant variables, the ACD could be predicted according to a regression formula with an accuracy of 82.1% of the predictions within 0.5 mm. When this ACD algorithm was used in retrospect in the intraocular lens (IOL) power calculation, the refractive prediction error decreased by 10% from the error associated with a previously published 4-variable algorithm and decreased 28% from the error using no individual ACD method other than the average ACD (P < .00001). CONCLUSIONS: The postoperative ACD was significantly correlated with and hence predictable by a 5-variable regression method incorporating the preoperative axial length, ACD, keratometry reading, lens thickness, and refraction as the most significant variables. The statistical relationship can be used to create a new ACD prediction algorithm to incorporate in a modern "thick lens" IOL power calculation formula with significant improvement in the accuracy of the refractive predictions as a result.
Authors: Achim Langenbucher; Nóra Szentmáry; Alan Cayless; Michael Müller; Timo Eppig; Simon Schröder; Ekkehart Fabian Journal: Ophthalmic Res Date: 2021-02-02 Impact factor: 2.892