Zhang Jia-Ju, Li Jian-Qing, Li Chen, Cao Yi-Hong, Lu Pei-Rong
Department of Ophthalmology, the First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China.
Int J Ophthalmol. 2021 Jul 18;14(7):1006-1012. doi: 10.18240/ijo.2021.07.07. eCollection 2021.
To predict postoperative intraocular lens (IOL) position using the Sirius anterior segment analysis system and investigate the effect of lens position and IOL type on postoperative refraction.
A total of 97 patients (102 eyes) were enrolled in the final analysis. An anterior segment biometry measurement was performed preoperatively with Sirius and Lenstar. The results of predicted lens position (PLP) and IOL power were automatically calculated by the software used by the instruments. Effective lens position (ELP) was measured manually using Sirius 3mo postoperatively. Pearson's correlation analysis and linear regression analysis were used to determine the correlation of lens position to other parameters.
PLP and ELP were positively correlated to axial length (AL; =0.42, <0.0001 and =0.49, <0.0001, respectively). There was a weak correlation between the peLP (ELP-PLP) and the prediction error of spherical refraction (peSR; =0.34, <0.0001). The peLP of Softec HD IOL differed statistically from those of both the TECNIS ZCB00 and Sensor AR40E IOLs. Multiple linear regression was used to obtain the prediction formula: ELP=0.66+0.63×[aqueous depth (AQD)+0.6LT] (=0.61, <0.0001), and a new variable (AQD+0.6 LT) was found to have the strongest correlation with ELP.
The Sirius anterior segment analysis system is helpful to predict ELP, which reduces postoperative refraction error.
使用Sirius眼前节分析系统预测人工晶状体(IOL)术后位置,并研究晶状体位置和IOL类型对术后屈光的影响。
共有97例患者(102只眼)纳入最终分析。术前使用Sirius和Lenstar进行眼前节生物测量。仪器使用的软件自动计算预测晶状体位置(PLP)和IOL屈光度的结果。术后3个月使用Sirius手动测量有效晶状体位置(ELP)。采用Pearson相关分析和线性回归分析确定晶状体位置与其他参数之间的相关性。
PLP和ELP与眼轴长度(AL)呈正相关(分别为r = 0.42,P < 0.0001和r = 0.49,P < 0.0001)。预测有效晶状体位置(peLP,ELP - PLP)与球镜屈光预测误差(peSR)之间存在弱相关性(r = 0.34,P < 0.0001)。Softec HD IOL的peLP与TECNIS ZCB00和Sensor AR40E IOL的peLP在统计学上存在差异。采用多元线性回归获得预测公式:ELP = 0.66 + 0.63×[前房深度(AQD)+0.6×晶状体厚度(LT)](r = 0.61, P <0.0001),并且发现一个新变量(AQD + 0.6 LT)与ELP相关性最强
Sirius眼前节分析系统有助于预测ELP,减少术后屈光误差