Yoo Young-Sik, Whang Woong-Joo
Department of Ophthalmology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu-si 11765, Korea.
Department of Ophthalmology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Korea.
J Clin Med. 2022 Mar 8;11(6):1469. doi: 10.3390/jcm11061469.
To predict the effective lens position (ELP) using conditional process analysis according to preoperative axial length.
Yeouido St. Mary hospital.
A retrospective case series.
This study included 621 eyes from 621 patients who underwent conventional cataract surgery at Yeouido St. Mary Hospital. Preoperative axial length (AL), mean corneal power (K), and anterior chamber depth (ACD) were measured by partial coherence interferometry. AL was used as an independent variable for the prediction of ELP, and 621 eyes were classified into four groups according to AL. Using conditional process analysis, we developed 24 structural equation models, with ACD and K acting as mediator, moderator or not included as variables, and investigated the model that best predicted ELP.
When AL was 23.0 mm or shorter, the predictability for ELP was highest when ACD and K acted as moderating variables (R2 = 0.217). When AL was between 23.0 mm and 24.5 mm or longer than 26.0 mm, the predictability was highest when K acted as a mediating variable and ACD acted as a moderating variable (R2 = 0.217 and R2 = 0.401). On the other hand, when AL ranged from 24.5 mm to 26.0 mm, the model with ACD as a mediating variable and K as a moderating variable was the most accurate (R2 = 0.220).
The optimal structural equation model for ELP prediction in each group varied according to AL. Conditional process analysis can be an alternative to conventional multiple linear regression analysis in ELP prediction.
根据术前眼轴长度，采用条件过程分析预测有效晶状体位置（ELP）。
首尔圣母医院。
回顾性病例系列研究。
本研究纳入了在首尔圣母医院接受传统白内障手术的621例患者的621只眼。采用部分相干干涉测量法测量术前眼轴长度（AL）、平均角膜曲率（K）和前房深度（ACD）。将AL作为预测ELP的自变量，并根据AL将621只眼分为四组。使用条件过程分析，我们构建了24个结构方程模型，其中ACD和K作为中介变量、调节变量或不包含在变量中，并研究了最能预测ELP的模型。
当AL为23.0mm或更短时，当ACD和K作为调节变量时，ELP的预测能力最高（R2 = 0.217）。当AL在23.0mm至24.5mm之间或大于26.0mm时，当K作为中介变量且ACD作为调节变量时，预测能力最高（R2 = 0.217和R2 = 0.401）。另一方面，当AL在24.5mm至26.0mm之间时，则以ACD作为中介变量且K作为调节变量的模型最为准确（R2 = 0.220）。
每组中预测ELP的最佳结构方程模型因AL而异。在ELP预测中，条件过程分析可以替代传统的多元线性回归分析。
