David L. Cooke , Karanpreet S. Multani , Jascha A. Wendelstein , Kamran M. Riaz
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Using IOLCon constants and optimized lens constants, nine IOL formulas (Barrett Universal 2.0 (BU2), Cooke K6 (K6), EVO 2.0, Haigis, Hoffer Q, Holladay I, Pearl DGS, SRK/T, and T2) were used to calculate refractive prediction errors across 3 methods: (1) IOLM700 biometry (axial length (AL), anterior chamber depth (ACD), and K), (2) Pentacam AXL biometry (AL, ACD, and Sim K), and (3) combined IOLM700 AL/ACD with Pentacam AXL Sim K</div></div><div><h3>Main Outcome Measures</h3><div>Mean absolute error (MAE) and root mean squared error (RMSE) were used to assess refractive outcomes.</div></div><div><h3>Results</h3><div>Using both nonoptimized (IOLCon) and optimized lens constants, all nine formulas demonstrated the lowest MAE and RMSE with the IOLMaster 700 approach, followed by the combination approach, and then the Pentacam AXL approach. Formula rankings varied among the 3 techniques: the K6 formula had the highest ranking for the IOLM700 technique, while EVO 2.0 had the top ranking for the Pentacam and combination techniques. When applying heteroscedastic testing to compare the 3 methods within each of the nine formulas, the IOLM700 version was significantly better than the combination version (all <em>P</em> < .05), and the combination version was superior to the PC version (all <em>P</em> < .05). Among the top-performing Pentacam formulas, the BU2 and Haigis formulas were statistically similar to the EVO 2.0</div></div><div><h3>Conclusions</h3><div>Biometric values (including standard K) from the IOLM700 provided the most accurate refractive predictions across formulas, outperforming biometric values (including Sim K) from the Pentacam AXL, even after lens constant optimization. Sim K values are not directly interchangeable with SS-OCT biometer-derived K values. The performance of formulas varies based on the source of biometry and optimization.</div></div>","PeriodicalId":7568,"journal":{"name":"American Journal of Ophthalmology","volume":"281 ","pages":"Pages 10-16"},"PeriodicalIF":4.2000,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Refractive Outcomes Using Simulated Keratometry Versus Keratometry From an Optical Biometer\",\"authors\":\"David L. Cooke , Karanpreet S. Multani , Jascha A. Wendelstein , Kamran M. 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Using IOLCon constants and optimized lens constants, nine IOL formulas (Barrett Universal 2.0 (BU2), Cooke K6 (K6), EVO 2.0, Haigis, Hoffer Q, Holladay I, Pearl DGS, SRK/T, and T2) were used to calculate refractive prediction errors across 3 methods: (1) IOLM700 biometry (axial length (AL), anterior chamber depth (ACD), and K), (2) Pentacam AXL biometry (AL, ACD, and Sim K), and (3) combined IOLM700 AL/ACD with Pentacam AXL Sim K</div></div><div><h3>Main Outcome Measures</h3><div>Mean absolute error (MAE) and root mean squared error (RMSE) were used to assess refractive outcomes.</div></div><div><h3>Results</h3><div>Using both nonoptimized (IOLCon) and optimized lens constants, all nine formulas demonstrated the lowest MAE and RMSE with the IOLMaster 700 approach, followed by the combination approach, and then the Pentacam AXL approach. Formula rankings varied among the 3 techniques: the K6 formula had the highest ranking for the IOLM700 technique, while EVO 2.0 had the top ranking for the Pentacam and combination techniques. When applying heteroscedastic testing to compare the 3 methods within each of the nine formulas, the IOLM700 version was significantly better than the combination version (all <em>P</em> < .05), and the combination version was superior to the PC version (all <em>P</em> < .05). Among the top-performing Pentacam formulas, the BU2 and Haigis formulas were statistically similar to the EVO 2.0</div></div><div><h3>Conclusions</h3><div>Biometric values (including standard K) from the IOLM700 provided the most accurate refractive predictions across formulas, outperforming biometric values (including Sim K) from the Pentacam AXL, even after lens constant optimization. Sim K values are not directly interchangeable with SS-OCT biometer-derived K values. 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引用次数: 0
摘要
目的比较Scheimpflug层析仪(Pentacam AXL, Oculus)使用模拟角膜测量法(SimK)测量的屈光预测精度与使用远心角膜测量法(IOLMaster 700 (IOLM700),卡尔蔡司Meditec AG)使用现代IOL度数计算公式的光学生物计获得的角膜测量法(K)测量的屈光预测精度。设计回顾性准确性和有效性分析方法:设置:私人诊所中心研究人群:589只术前测量了SimK和K的眼睛,接受超声乳化和单焦点人工晶体植入术(Clareon SY60WF IOL,爱尔康实验室,Inc.)。使用IOLCon常数和优化的晶状体常数,使用9个IOL公式(Barrett Universal 2.0 (BU2), Cooke K6 (K6), EVO 2.0, Haigis, Hoffer Q, Holladay I, Pearl DGS, SRK/T和T2)计算三种方法的屈光预测误差:(1)IOLM700生物测量(轴长(AL),前房深度(ACD)和K), (2) Pentacam AXL生物测量(AL, ACD和Sim K), (3) IOLM700 AL/ACD与Pentacam AXL Sim K联合使用。使用平均绝对误差(MAE)和均方根误差(RMSE)来评估屈光结果。结果在非优化(IOLCon)和优化后的晶状体常数下,所有9个配方均以IOLMaster 700方法的MAE和RMSE最低,其次是组合方法,最后是Pentacam AXL方法。三种技术的公式排名各不相同,IOLM700技术的K6公式排名最高,Pentacam和组合技术的EVO 2.0公式排名最高。应用异方差检验比较9个配方内各3种方法的差异,IOLM700版本显著优于联合版本(均p < 0.05),联合版本优于PC版本(均p < 0.05)。结论:IOLM700的生物特征值(包括标准K)在所有配方中提供了最准确的屈光预测,即使经过晶状体常数优化,也优于Pentacam AXL的生物特征值(包括Sim K)。Sim K值与SS-OCT生物计衍生的K值不能直接互换。配方的性能根据生物计量和优化的来源而变化。
Refractive Outcomes Using Simulated Keratometry Versus Keratometry From an Optical Biometer
Purpose
To compare refractive prediction accuracy using simulated keratometry (SimK) measurements obtained from a Scheimpflug tomographer (Pentacam AXL, Oculus) versus keratometry (K) measurements obtained from an optical biometer utilizing telecentric keratometry (IOLMaster 700 (IOLM700), Carl Zeiss Meditec AG) applied to modern IOL power calculation formulas.
Design
Retrospective accuracy and validity analysis
Methods and Setting
Private practice center
Study Population
Five hundred eighty-nine eyes with preoperative SimK and K measurements undergoing phacoemulsification and implantation of monofocal IOL (Clareon SY60WF IOL, Alcon Laboratories, Inc.). Using IOLCon constants and optimized lens constants, nine IOL formulas (Barrett Universal 2.0 (BU2), Cooke K6 (K6), EVO 2.0, Haigis, Hoffer Q, Holladay I, Pearl DGS, SRK/T, and T2) were used to calculate refractive prediction errors across 3 methods: (1) IOLM700 biometry (axial length (AL), anterior chamber depth (ACD), and K), (2) Pentacam AXL biometry (AL, ACD, and Sim K), and (3) combined IOLM700 AL/ACD with Pentacam AXL Sim K
Main Outcome Measures
Mean absolute error (MAE) and root mean squared error (RMSE) were used to assess refractive outcomes.
Results
Using both nonoptimized (IOLCon) and optimized lens constants, all nine formulas demonstrated the lowest MAE and RMSE with the IOLMaster 700 approach, followed by the combination approach, and then the Pentacam AXL approach. Formula rankings varied among the 3 techniques: the K6 formula had the highest ranking for the IOLM700 technique, while EVO 2.0 had the top ranking for the Pentacam and combination techniques. When applying heteroscedastic testing to compare the 3 methods within each of the nine formulas, the IOLM700 version was significantly better than the combination version (all P < .05), and the combination version was superior to the PC version (all P < .05). Among the top-performing Pentacam formulas, the BU2 and Haigis formulas were statistically similar to the EVO 2.0
Conclusions
Biometric values (including standard K) from the IOLM700 provided the most accurate refractive predictions across formulas, outperforming biometric values (including Sim K) from the Pentacam AXL, even after lens constant optimization. Sim K values are not directly interchangeable with SS-OCT biometer-derived K values. The performance of formulas varies based on the source of biometry and optimization.
期刊介绍:
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