Revisiting the Visual Acuity Curves. A Proposed Methodology for the Evaluation of Postoperative Visual Acuity in Presbyopia.

Clinical ophthalmology (Auckland, N.Z.) Pub Date : 2024-12-25 eCollection Date: 2024-01-01 DOI:10.2147/OPTH.S487400
Georgios Labiris, Minas Bakirtzis, Christos Panagis, Christina Mitsi, Eleftheria Vorgiazidou, Aristeidis Konstantinidis, Konstantinos K Delibasis
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Abstract

Purpose: To identify the minimally required number of distances of visual acuity (VA) measurements for the reliable estimation of the visual acuity curve (VAC) and area of the curve (AoC) in presbyopia correction.

Patients and methods: The study was divided into a validation and a clinical phase with a total recruitment of 120 participants (120 eyes) who underwent uncomplicated pseudophakic presbyopia surgery with bilateral premium intraocular lenses (IOL) implantation. This study was conducted in the Department of Ophthalmology, University Hospital of Alexandroupolis, Alexandroupolis, Greece. Postoperative VAC and AoC were calculated with VA measurements taken at nine pre-defined distances. A mathematical model based on cubic spline interpolation was developed and assessed comparing the VAC and AoC values obtained using nine distances, with the ones predicted when a subset of VA measurements was inserted into the model.

Results: Less than four measurements resulted in unreliable VAC and AoC assessment. Optimal distances for four to six VA measurements were determined. Mean error in the prediction of VAC and AoC of the clinical group ranged from 2.54 Letters/1.74% (6 measurements) to 2.90 letters/2.9% (4 measurements), respectively.

Conclusion: Mathematical models that use cubic spline interpolation provide reliable VAC and AoC estimation, even with four VA measurements, when obtained at specific distances.

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重新审视视力曲线。老花眼术后视力评估方法的探讨。
目的:确定老花矫正视力曲线(VAC)和曲线面积(AoC)可靠估计所需的最小视明距离(VA)测量数。患者和方法:该研究分为验证期和临床期,共招募120名参与者(120只眼睛),他们接受了无并发症的假性老花眼手术和双侧优质人工晶状体(IOL)植入术。本研究在希腊亚历山德鲁波利斯亚历山德鲁波利斯大学医院眼科进行。术后VAC和AoC通过在九个预先定义的距离处测量VA来计算。建立了一个基于三次样条插值的数学模型,并将使用9个距离获得的VAC和AoC值与插入一个VA测量子集时的预测值进行了比较。结果:少于4项测量导致VAC和AoC评估不可靠。确定了四到六次VA测量的最佳距离。临床组VAC和AoC预测的平均误差分别为2.54字母/1.74%(6次测量)至2.90字母/2.9%(4次测量)。结论:使用三次样条插值的数学模型提供了可靠的VAC和AoC估计,即使有四次VA测量,当获得特定距离时。
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