Department of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Department of Neonatalogy, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
BMC Ophthalmol. 2022 Jan 10;22(1):19. doi: 10.1186/s12886-021-02227-4.
Currently used screening criteria for retinopathy of prematurity (ROP) show high sensitivity for predicting treatment-requiring ROP but low specificity; over 90% of examined infants do not develop ROP that requires treatment (type 1 ROP). A novel weight gain-based prediction model was developed by the G-ROP study group to increase the specificity of the screening criteria and keep the number of ophthalmic examinations as low as possible. This retrospective cohort study aimed to externally validate the G-ROP screening criteria in a Swiss cohort.
Data from 645 preterm infants in ROP screening at Inselspital Bern between January 2015 and December 2019 were retrospectively retrieved from the screening log and analysed. The G-ROP screening criteria, consisting of 6 trigger parameters, were applied in infants with complete data. To determine the performance of the G-ROP prediction model for treatment-requiring ROP, sensitivity and specificity were calculated.
Complete data were available for 322 infants who were included in the analysis. None of the excluded infants had developed type 1 ROP. By applying the 6 criteria in the G-ROP model, 214 infants were flagged to undergo screening: among these, 14 developed type 1 ROP, 9 developed type 2 ROP, and 43 developed milder stages of ROP. The sensitivity for predicting treatment-requiring ROP was 100% (CI, 0.79-1.00), and the specificity was 41% (CI, 0.35 -0.47). Implementing the novel G-ROP screening criteria would reduce the number of infants entering ROP screening by approximately one third.
The overall prevalence of treatment-requiring ROP was low (2.15%). Previously published performance parameters for the G-ROP algorithm were reproducible in this Swiss cohort. Importantly, all treatment-requiring infants were correctly identified. By using these novel criteria, the burden of screening examinations could be significantly reduced.
目前用于早产儿视网膜病变（ROP）筛查的标准对预测需要治疗的 ROP 具有很高的敏感性，但特异性较低；超过 90%的检查婴儿不会发展为需要治疗的 ROP（1 型 ROP）。G-ROP 研究小组开发了一种新的基于体重增加的预测模型，以提高筛查标准的特异性，并尽可能减少眼科检查的数量。本回顾性队列研究旨在对瑞士队列进行 G-ROP 筛查标准的外部验证。
从 2015 年 1 月至 2019 年 12 月在伯尔尼岛医院进行 ROP 筛查的 645 名早产儿的筛查日志中回顾性检索数据，并进行分析。在有完整数据的婴儿中应用由 6 个触发参数组成的 G-ROP 筛查标准。为了确定 G-ROP 预测模型对需要治疗的 ROP 的性能，计算了敏感性和特异性。
共有 322 名婴儿可获得完整数据，纳入分析。没有被排除的婴儿发生 1 型 ROP。通过应用 G-ROP 模型中的 6 个标准，有 214 名婴儿被标记进行筛查：其中 14 名发生 1 型 ROP，9 名发生 2 型 ROP，43 名发生 ROP 较轻微的阶段。预测需要治疗的 ROP 的敏感性为 100%（95%CI：0.79-1.00），特异性为 41%（95%CI：0.35-0.47）。实施新的 G-ROP 筛查标准可使进入 ROP 筛查的婴儿数量减少约三分之一。
需要治疗的 ROP 的总体患病率较低（2.15%）。在瑞士队列中，G-ROP 算法的先前发表的性能参数具有可重复性。重要的是，所有需要治疗的婴儿都被正确识别。使用这些新的标准，可以显著减少筛查检查的负担。
