Ma Shan, Li Cheng, Sun Jing, Yang Jun, Wen Kai, Chen Xiteng, Zhao Fangyu, Sun Xuequan, Tian Fang
Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute, School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.
Zhengda Guangming Eye Group, Weifang, China.
BMC Ophthalmol. 2025 Apr 22;25(1):236. doi: 10.1186/s12886-025-04067-y.
Achieving accurate intraocular lens (IOL) power calculation is crucial for successful refractive outcomes in cataract surgery. This study aimed to evaluate the interchangeability of keratometry (K) values obtained from four biometric devices (IOLMaster 700, CASIA2, Pentacam, and iTrace) and assess the predictive accuracy of five modern IOL calculation formulas (Barrett Universal II, Cooke K6, EVO 2.0, Kane, and PEARL-DGS) when using K values from these different devices.
This prospective study included K values obtained from four biometric devices for use in five IOL power calculation formulas. Predictive accuracy was assessed using multiple statistical parameters, including standard deviation (SD), mean absolute error (MAE), median absolute error (MedAE) and root mean square absolute error (RMSAE). The interchangeability of devices was evaluated by comparing predictive outcomes across devices and formulas, with statistical analyses focusing on consistency and agreement.
Predictive accuracy across the five IOL formulas was stable and showed no statistically significant differences when using keratometry measurements from the same biometric device. However, significant variability was noted when comparing K values from different devices using the same formula. The SS-OCT-based devices (IOLMaster 700 and CASIA2) showed higher consistency in predictive accuracy compared to Scheimpflug-based Pentacam and ray-tracing-based iTrace. Despite this inter-device variability, all five IOL formulas showed overall robust performance across different devices.
Our findings indicate that keratometry measurements from different biometric devices are not fully interchangeable. SS-OCT-based devices (IOLMaster 700 and CASIA2) provided superior consistency in refractive prediction accuracy. Therefore, clinicians should carefully select biometric device-formula combinations based on the specific measurement principles and desired refractive outcomes. Further research involving larger sample sizes, additional IOL types and biometric devices, as well as assessment of surgeon-related factors, is warranted to optimize refractive accuracy in cataract surgery.
实现准确的人工晶状体（IOL）屈光力计算对于白内障手术成功的屈光结果至关重要。本研究旨在评估从四种生物测量设备（IOLMaster 700、CASIA2、Pentacam和iTrace）获得的角膜曲率计（K）值的互换性，并评估当使用来自这些不同设备的K值时五种现代IOL计算公式（Barrett Universal II、Cooke K6、EVO 2.0、Kane和PEARL-DGS）的预测准确性。
这项前瞻性研究纳入了从四种生物测量设备获得的K值，用于五种IOL屈光力计算公式。使用多个统计参数评估预测准确性，包括标准差（SD）、平均绝对误差（MAE）、中位数绝对误差（MedAE）和均方根绝对误差（RMSAE）。通过比较不同设备和公式的预测结果来评估设备的互换性，统计分析侧重于一致性和一致性。
当使用来自同一生物测量设备的角膜曲率测量值时，五种IOL公式的预测准确性稳定，且无统计学显著差异。然而，当使用相同公式比较来自不同设备的K值时，发现存在显著差异。与基于Scheimpflug的Pentacam和基于光线追踪的iTrace相比，基于扫频光学相干断层扫描（SS-OCT）的设备（IOLMaster 700和CASIA2）在预测准确性方面表现出更高的一致性。尽管存在设备间差异，但所有五种IOL公式在不同设备上总体表现稳健。
我们的研究结果表明，来自不同生物测量设备的角膜曲率测量值并非完全可互换。基于SS-OCT的设备（IOLMaster 700和CASIA2）在屈光预测准确性方面提供了更高的一致性。因此，临床医生应根据特定的测量原理和期望的屈光结果仔细选择生物测量设备-公式组合。有必要进行进一步的研究，包括更大的样本量、更多的IOL类型和生物测量设备，以及评估与外科医生相关的因素，以优化白内障手术的屈光准确性。
