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Conditional Process Analysis for Effective Lens Position According to Preoperative Axial Length.

Young-Sik Yoo1, Woong-Joo Whang2

  • 1Department of Ophthalmology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu-si 11765, Korea.

Journal of Clinical Medicine
|March 25, 2022
PubMed
Summary

Predicting effective lens position (ELP) after cataract surgery is crucial. This study shows that optimal prediction models vary by preoperative axial length (AL), suggesting conditional process analysis is a valuable tool.

Keywords:
axial lengthconditional process analysiseffective lens positionintraocular lens power calculation

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Statistical Modeling

Background:

  • Accurate prediction of effective lens position (ELP) is essential for achieving target refractive outcomes after cataract surgery.
  • Preoperative ocular biometry, including axial length (AL), anterior chamber depth (ACD), and corneal power (K), are key factors influencing ELP.
  • Conventional methods for ELP prediction may not fully capture the complex relationships between biometric parameters.

Purpose of the Study:

  • To predict the effective lens position (ELP) using conditional process analysis based on preoperative axial length (AL).
  • To investigate the role of anterior chamber depth (ACD) and corneal power (K) as mediators or moderators in ELP prediction across different AL groups.
  • To identify the optimal structural equation model for ELP prediction tailored to specific AL ranges.

Main Methods:

  • A retrospective case series of 621 eyes undergoing conventional cataract surgery.
  • Preoperative AL, ACD, and K were measured using partial coherence interferometry.
  • Conditional process analysis was employed to develop and evaluate 24 structural equation models, with ACD and K acting as mediators or moderators.

Main Results:

  • The best ELP prediction models varied significantly across different axial length (AL) groups.
  • For AL ≤ 23.0 mm, ACD and K as moderators yielded the highest predictability (R² = 0.217).
  • For 23.0 mm < AL < 24.5 mm and AL > 26.0 mm, K as a mediator and ACD as a moderator provided the best fit (R² = 0.217 and R² = 0.401, respectively).
  • For 24.5 mm ≤ AL ≤ 26.0 mm, ACD as a mediator and K as a moderator offered the highest accuracy (R² = 0.220).

Conclusions:

  • The optimal structural equation model for predicting effective lens position (ELP) is dependent on the preoperative axial length (AL).
  • Conditional process analysis offers a flexible and potentially superior alternative to traditional multiple linear regression for ELP prediction in cataract surgery.
  • Tailoring prediction models based on AL can improve refractive outcomes in patients undergoing cataract surgery.