Roters Sigrid, Hellmich Martin, Szurman Peter
Department of Ophthalmology, University of Cologne, Cologne, Germany.
J Cataract Refract Surg. 2002 May;28(5):853-9. doi: 10.1016/s0886-3350(01)01169-5.
To examine the predictability of axial length measurement when lens thickness and vitreous body length are known.
Center of Ophthalmology, University of Cologne, Cologne, Germany.
This study comprised 227 patients with a mean age of 70.01 years +/- 12.47 (SD). None of the eyes examined had a history of surgery or trauma. Patients with systemic disease were excluded from the study. Before cataract surgery, the length of the vitreous body and thickness of the lens were measured and the correlation between these data and the axial length was evaluated. Two ultrasonic devices were used for biometric measurements: the BMS 811 Biometric System (Grieshaber) and the Cooper Vision Ultrascan Digital A+B-Scan 2000. The correlations between the axial length and vitreous body length and the lens thickness and vitreous body length were analyzed using multiple linear regression.
The BMS 811 provided the best prediction of axial length based on vitreous body length. Considering sex but not age significantly improved the model fit. With the BMS 811, the following formula was developed to predict axial length: Axial length (mm) = 7.129 mm + 0.095 mm x sex (female = 0, male = 1) + 1.040 x vitreous body length (mm). An approximate 95% prediction limit may be calculated by the following formula: Axial length (mm) +/- 2 x 0.413 mm.
This study yielded an easy-to-use formula for predicting the axial length using the vitreous body length and the patient's sex. The remaining error in prediction is likely to be the result of patient heterogeneity in age, ocular globe size, and lens thickness (cataract formation). Good prediction of the axial length is important to refractive outcomes to distinguish corneal myopia from axial length myopia to choose grafts and the opening size in penetrating keratoplasty. Further studies to detect a clinically relevant improvement in such outcomes are required to assess the utility of the prediction formula.
探讨在已知晶状体厚度和玻璃体长度时，眼轴长度测量的可预测性。
德国科隆大学眼科中心。
本研究纳入227例患者，平均年龄70.01岁±12.47（标准差）。所有受检眼均无手术或外伤史。患有全身性疾病的患者被排除在研究之外。在白内障手术前，测量玻璃体长度和晶状体厚度，并评估这些数据与眼轴长度之间的相关性。使用两种超声设备进行生物测量：BMS 811生物测量系统（Grieshaber）和库博光学Ultrascan数字A+B超2000。使用多元线性回归分析眼轴长度与玻璃体长度以及晶状体厚度与玻璃体长度之间的相关性。
基于玻璃体长度，BMS 811对眼轴长度的预测效果最佳。考虑性别而非年龄可显著改善模型拟合度。使用BMS 811，得出以下预测眼轴长度的公式：眼轴长度（mm）=7.129 mm + 0.095 mm×性别（女性=0，男性=1）+ 1.040×玻璃体长度（mm）。可通过以下公式计算近似95%的预测范围：眼轴长度（mm）±2×0.413 mm。
本研究得出了一个易于使用的公式，可利用玻璃体长度和患者性别预测眼轴长度。预测中剩余的误差可能是由于患者在年龄、眼球大小和晶状体厚度（白内障形成）方面的异质性所致。准确预测眼轴长度对于屈光手术结果很重要，有助于区分角膜近视和眼轴性近视，从而选择移植片以及穿透性角膜移植术中的开口大小。需要进一步研究以检测此类结果在临床上的相关改善情况，以评估预测公式的实用性。
