Department of Ophthalmology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Statistiska Konsultgruppen, Gothenburg, Sweden.
JAMA Ophthalmol. 2020 Jan 1;138(1):21-29. doi: 10.1001/jamaophthalmol.2019.4502.
To prevent blindness, repeated infant eye examinations are performed to detect severe retinopathy of prematurity (ROP), yet only a small fraction of those screened need treatment. Early individual risk stratification would improve screening timing and efficiency and potentially reduce the risk of blindness.
To create and validate an easy-to-use prediction model using only birth characteristics and to describe a continuous hazard function for ROP treatment.
DESIGN, SETTING, AND PARTICIPANTS: In this retrospective cohort study, Swedish National Patient Registry data from infants screened for ROP (born between January 1, 2007, and August 7, 2018) were analyzed with Poisson regression for time-varying data (postnatal age, gestational age [GA], sex, birth weight, and important interactions) to develop an individualized predictive model for ROP treatment (called DIGIROP-Birth [Digital ROP]). The model was validated internally and externally (in US and European cohorts) and compared with 4 published prediction models.
The study outcome was ROP treatment. The measures were estimated momentary and cumulative risks, hazard ratios with 95% CIs, area under the receiver operating characteristic curve (hereinafter referred to as AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Among 7609 infants (54.6% boys; mean [SD] GA, 28.1 [2.1] weeks; mean [SD] birth weight, 1119 [353] g), 442 (5.8%) were treated for ROP, including 142 (40.1%) treated of 354 born at less than 24 gestational weeks. Irrespective of GA, the risk for receiving ROP treatment increased during postnatal weeks 8 through 12 and decreased thereafter. Validations of DIGIROP-Birth for 24 to 30 weeks' GA showed high predictive ability for the model overall (AUC, 0.90 [95% CI, 0.89-0.92] for internal validation, 0.94 [95% CI, 0.90-0.98] for temporal validation, 0.87 [95% CI, 0.84-0.89] for US external validation, and 0.90 [95% CI, 0.85-0.95] for European external validation) by calendar periods and by race/ethnicity. The sensitivity, specificity, PPV, and NPV were numerically at least as high as those obtained from CHOP-ROP (Children's Hospital of Philadelphia-ROP), OMA-ROP (Omaha-ROP), WINROP (weight, insulinlike growth factor 1, neonatal, ROP), and CO-ROP (Colorado-ROP), models requiring more complex postnatal data.
This study validated an individualized prediction model for infants born at 24 to 30 weeks' GA, enabling early risk prediction of ROP treatment based on birth characteristics data. Postnatal age rather than postmenstrual age was a better predictive variable for the temporal risk of ROP treatment. The model is an accessible online application that appears to be generalizable and to have at least as good test statistics as other models requiring longitudinal neonatal data not always readily available to ophthalmologists.
为了预防失明，需要对婴儿进行反复的眼部检查，以检测严重的早产儿视网膜病变（ROP），但只有一小部分筛查出的婴儿需要治疗。早期进行个体化风险分层可以改善筛查时机和效率，并有可能降低失明的风险。
利用仅有的出生特征创建和验证一个易于使用的预测模型，并描述 ROP 治疗的连续危险函数。
设计、地点和参与者：在这项回顾性队列研究中，对瑞典全国患者注册数据中接受 ROP 筛查的婴儿（出生于 2007 年 1 月 1 日至 2018 年 8 月 7 日）进行分析，采用泊松回归进行时间变化数据（出生后年龄、胎龄 [GA]、性别、出生体重和重要的相互作用），以开发 ROP 治疗的个体化预测模型（称为 DIGIROP-Birth [数字 ROP]）。该模型在内部和外部（美国和欧洲队列）进行了验证，并与 4 个已发表的预测模型进行了比较。
研究结局为 ROP 治疗。测量指标为估计的瞬时和累积风险、95%CI 下的危险比、接受者操作特征曲线下的面积（以下简称 AUC）、灵敏度、特异性、阳性预测值（PPV）和阴性预测值（NPV）。
在 7609 名婴儿（54.6%为男孩；平均 [标准差]GA 为 28.1 [2.1] 周；平均 [标准差]出生体重为 1119 [353]g）中，有 442 名（5.8%）接受了 ROP 治疗，其中 142 名（40.1%）治疗的婴儿出生时胎龄小于 24 周。无论 GA 如何，接受 ROP 治疗的风险在出生后第 8 周到第 12 周期间增加，并在此后降低。对 24 至 30 周 GA 的 DIGIROP-Birth 进行验证，结果显示该模型具有较高的整体预测能力（内部验证的 AUC 为 0.90 [95%CI，0.89-0.92]，时间验证为 0.94 [95%CI，0.90-0.98]，美国外部验证为 0.87 [95%CI，0.84-0.89]，欧洲外部验证为 0.90 [95%CI，0.85-0.95]），按日历期和种族/族裔分类均具有较高的预测能力。灵敏度、特异性、PPV 和 NPV 的数值至少与 CHOP-ROP（费城儿童医院-ROP）、OMA-ROP（奥马哈-ROP）、WINROP（体重、胰岛素样生长因子 1、新生儿、ROP）和 CO-ROP（科罗拉多-ROP）模型相同，后 4 个模型需要更复杂的出生后数据。
本研究验证了一种适用于 24 至 30 周 GA 出生婴儿的个体化预测模型，能够基于出生特征数据对 ROP 治疗的早期风险进行预测。出生后年龄而不是月经后年龄是 ROP 治疗时间风险的更好预测变量。该模型是一个易于访问的在线应用程序，似乎具有可推广性，并且至少具有与其他需要并非总是易于获得的纵向新生儿数据的模型相同的测试统计数据。
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