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Prediction of the effective postoperative (intraocular lens) anterior chamber depth

角膜曲率计 医学 人工晶状体 眼科 折射误差 线性回归 超声乳化术 白内障手术 均方预测误差 数学 外科 角膜 眼病 视力 统计
作者
Thomas Olsen
出处
期刊:Journal of Cataract and Refractive Surgery [Lippincott Williams & Wilkins]
卷期号:32 (3): 419-424 被引量:247
标识
DOI:10.1016/j.jcrs.2005.12.139
摘要

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.
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