Suppr超能文献

科罗拉多早产儿视网膜病变筛查模型的验证

Validation of the Colorado Retinopathy of Prematurity Screening Model.

作者信息

McCourt Emily A, Ying Gui-Shuang, Lynch Anne M, Palestine Alan G, Wagner Brandie D, Wymore Erica, Tomlinson Lauren A, Binenbaum Gil

机构信息

Department of Ophthalmology, University of Colorado School of Medicine, Aurora.

Scheie Eye Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia.

出版信息

JAMA Ophthalmol. 2018 Apr 1;136(4):409-416. doi: 10.1001/jamaophthalmol.2018.0376.

Abstract

IMPORTANCE

The Colorado Retinopathy of Prematurity (CO-ROP) model uses birth weight, gestational age, and weight gain at the first month of life (WG-28) to predict risk of severe retinopathy of prematurity (ROP). In previous validation studies, the model performed very well, predicting virtually all cases of severe ROP and potentially reducing the number of infants who need ROP examinations, warranting validation in a larger, more diverse population.

OBJECTIVE

To validate the performance of the CO-ROP model in a large multicenter cohort.

DESIGN, SETTING, PARTICIPANTS: This study is a secondary analysis of data from the Postnatal Growth and Retinopathy of Prematurity (G-ROP) Study, a retrospective multicenter cohort study conducted in 29 hospitals in the United States and Canada between January 2006 and June 2012 of 6351 premature infants who received ROP examinations.

MAIN OUTCOMES AND MEASURES

Sensitivity and specificity for severe (early treatment of ROP [ETROP] type 1 or 2) ROP, and reduction in infants receiving examinations. The CO-ROP model was applied to the infants in the G-ROP data set with all 3 data points (infants would have received examinations if they met all 3 criteria: birth weight, <1501 g; gestational age, <30 weeks; and WG-28, <650 g). Infants missing WG-28 information were included in a secondary analysis in which WG-28 was considered fewer than 650 g.

RESULTS

Of 7438 infants in the G-ROP study, 3575 (48.1%) were girls, and maternal race/ethnicity was 2310 (31.1%) African American, 3615 (48.6%) white, 233 (3.1%) Asian, 40 (0.52%) American Indian/Alaskan Native, and 93 (1.3%) Pacific Islander. In the study cohort, 747 infants (11.8%) had type 1 or 2 ROP, 2068 (32.6%) had lower-grade ROP, and 3536 (55.6%) had no ROP. The CO-ROP model had a sensitivity of 96.9% (95% CI, 95.4%-97.9%) and a specificity of 40.9% (95% CI, 39.3%-42.5%). It missed 23 (3.1%) infants who developed severe ROP. The CO-ROP model would have reduced the number of infants who received examinations by 26.1% (95% CI, 25.0%-27.2%).

CONCLUSIONS AND RELEVANCE

The CO-ROP model demonstrated high but not 100% sensitivity for severe ROP and missed infants who might require treatment in this large validation cohort. The model requires all 3 criteria to be met to signal a need for examinations, but some infants with a birth weight or gestational age above the thresholds developed severe ROP. Most of these infants who were not detected by the CO-ROP model had obvious deviation in expected weight trajectories or nonphysiologic weight gain. These findings suggest that the CO-ROP model needs to be revised before considering implementation into clinical practice.

摘要

重要性

科罗拉多早产儿视网膜病变(CO-ROP)模型利用出生体重、胎龄以及出生后第一个月的体重增长(WG-28)来预测早产儿严重视网膜病变(ROP)的风险。在之前的验证研究中,该模型表现出色,几乎能预测所有严重ROP病例,并有可能减少需要进行ROP检查的婴儿数量,因此有必要在更大、更多样化的人群中进行验证。

目的

在一个大型多中心队列中验证CO-ROP模型的性能。

设计、地点、参与者:本研究是对早产产后生长与视网膜病变(G-ROP)研究数据的二次分析,这是一项回顾性多中心队列研究,于2006年1月至2012年6月在美国和加拿大的29家医院对6351名接受ROP检查的早产儿进行。

主要结局和测量指标

严重(早期治疗性ROP [ETROP] 1型或2型)ROP的敏感性和特异性,以及接受检查的婴儿数量的减少。将CO-ROP模型应用于G-ROP数据集中具有所有3个数据点的婴儿(如果婴儿满足所有3条标准:出生体重<1501 g;胎龄<30周;以及WG-28<650 g,他们就会接受检查)。缺少WG-28信息的婴儿被纳入一项二次分析,其中将WG-28视为少于650 g。

结果

在G-ROP研究的7438名婴儿中,3575名(48.1%)为女孩,母亲种族/族裔为非裔美国人2310名(31.1%)、白人3615名(48.6%)、亚洲人233名(3.1%)、美洲印第安人/阿拉斯加原住民40名(0.52%)、太平洋岛民93名(1.3%)。在研究队列中,747名婴儿(11.8%)患有1型或2型ROP,2068名(32.6%)患有较低级别的ROP,3536名(55.6%)没有ROP。CO-ROP模型的敏感性为96.9%(95% CI,95.4%-97.9%),特异性为40.9%(95% CI, 39.3%-42.5%)。它遗漏了23名(3.1%)发生严重ROP的婴儿。CO-ROP模型将使接受检查的婴儿数量减少26.1%(95% CI, 25.0%-27.2%)。

结论与意义

在这个大型验证队列中,CO-ROP模型对严重ROP显示出高敏感性但并非100%,并遗漏了可能需要治疗的婴儿。该模型要求满足所有3条标准才提示需要进行检查,但一些出生体重或胎龄高于阈值的婴儿发生了严重ROP。这些未被CO-ROP模型检测到的婴儿大多在预期体重轨迹上有明显偏差或体重非生理性增加。这些发现表明,在考虑将CO-ROP模型应用于临床实践之前需要对其进行修订。

相似文献

1
Validation of the Colorado Retinopathy of Prematurity Screening Model.
JAMA Ophthalmol. 2018 Apr 1;136(4):409-416. doi: 10.1001/jamaophthalmol.2018.0376.
3
Validation of the Postnatal Growth and Retinopathy of Prematurity Screening Criteria.
JAMA Ophthalmol. 2020 Jan 1;138(1):31-37. doi: 10.1001/jamaophthalmol.2019.4517.
4
Validation of the Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) Model.
JAMA Ophthalmol. 2017 Aug 1;135(8):871-877. doi: 10.1001/jamaophthalmol.2017.2295.
6
Colorado retinopathy of prematurity model: a multi-institutional validation study.
J AAPOS. 2016 Jun;20(3):220-5. doi: 10.1016/j.jaapos.2016.01.017. Epub 2016 May 7.
7
Individual Risk Prediction for Sight-Threatening Retinopathy of Prematurity Using Birth Characteristics.
JAMA Ophthalmol. 2020 Jan 1;138(1):21-29. doi: 10.1001/jamaophthalmol.2019.4502.
9
Retrospective Validation of the Postnatal Growth and Retinopathy of Prematurity (G-ROP) Criteria in a Japanese Cohort.
Am J Ophthalmol. 2019 Sep;205:50-53. doi: 10.1016/j.ajo.2019.03.027. Epub 2019 Apr 4.

引用本文的文献

1
Retrospective validation of the postnatal growth and retinopathy of prematurity criteria in a Chinese cohort.
Front Pediatr. 2025 Jun 4;13:1509106. doi: 10.3389/fped.2025.1509106. eCollection 2025.
2
A fundus image dataset for intelligent retinopathy of prematurity system.
Sci Data. 2024 May 27;11(1):543. doi: 10.1038/s41597-024-03362-5.
3
Nomogram to predict severe retinopathy of prematurity in Southeast China.
Int J Ophthalmol. 2024 Feb 18;17(2):282-288. doi: 10.18240/ijo.2024.02.09. eCollection 2024.
5
Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population.
J Ophthalmol. 2023 Aug 17;2023:8406287. doi: 10.1155/2023/8406287. eCollection 2023.
6
Jeddah Retinopathy of Prematurity (JED-ROP) Screening Algorithm.
Glob Pediatr Health. 2023 Aug 27;10:2333794X231182524. doi: 10.1177/2333794X231182524. eCollection 2023.
7
Modifiable Risk Factors and Preventative Strategies for Severe Retinopathy of Prematurity.
Life (Basel). 2023 Apr 24;13(5):1075. doi: 10.3390/life13051075.
8
Retrospective validation of G-ROP, CO-ROP, Alex-ROP, and ROPscore predictive algorithms in two Chinese medical centers.
Front Pediatr. 2023 Feb 22;11:1079290. doi: 10.3389/fped.2023.1079290. eCollection 2023.
9
A risk scoring model to predict progression of retinopathy of prematurity for Indonesia.
PLoS One. 2023 Feb 3;18(2):e0281284. doi: 10.1371/journal.pone.0281284. eCollection 2023.
10
Retinopathy of prematurity: risk stratification by gestational age.
J Perinatol. 2023 Jun;43(6):694-701. doi: 10.1038/s41372-023-01604-9. Epub 2023 Jan 18.

本文引用的文献

1
Comparison between weight gain and Fenton preterm growth z scores in assessing the risk of retinopathy of prematurity.
J AAPOS. 2019 Oct;23(5):281-283. doi: 10.1016/j.jaapos.2019.06.007. Epub 2019 Sep 11.
2
Validation of the Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) Model.
JAMA Ophthalmol. 2017 Aug 1;135(8):871-877. doi: 10.1001/jamaophthalmol.2017.2295.
3
Validation of the CHOP model for detecting severe retinopathy of prematurity in a cohort of Colorado infants.
Acta Ophthalmol. 2018 May;96(3):e404-e405. doi: 10.1111/aos.13506. Epub 2017 Jun 27.
4
Validation of WINROP for detecting retinopathy of prematurity in a North American cohort of preterm infants.
J AAPOS. 2017 Jun;21(3):229-233. doi: 10.1016/j.jaapos.2017.05.004. Epub 2017 May 12.
6
Postnatal Growth and Retinopathy of Prematurity Study: Rationale, Design, and Subject Characteristics.
Ophthalmic Epidemiol. 2017 Feb;24(1):36-47. doi: 10.1080/09286586.2016.1255765. Epub 2016 Dec 20.
7
Predictive algorithms for early detection of retinopathy of prematurity.
Acta Ophthalmol. 2017 Mar;95(2):158-164. doi: 10.1111/aos.13117. Epub 2016 Jun 20.
8
Colorado retinopathy of prematurity model: a multi-institutional validation study.
J AAPOS. 2016 Jun;20(3):220-5. doi: 10.1016/j.jaapos.2016.01.017. Epub 2016 May 7.
9
10
Clinical Models and Algorithms for the Prediction of Retinopathy of Prematurity: A Report by the American Academy of Ophthalmology.
Ophthalmology. 2016 Apr;123(4):804-16. doi: 10.1016/j.ophtha.2015.11.003. Epub 2016 Jan 28.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验