Ricard Caroline A, Dammann Christiane E L, Dammann Olaf
Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA.
Neonatology. 2017;112(2):130-136. doi: 10.1159/000464459. Epub 2017 May 13.
Retinopathy of prematurity (ROP) is a disorder of the preterm newborn characterized by neurovascular disruption in the immature retina that may cause visual impairment and blindness.
To develop a clinical screening tool for early postnatal prediction of ROP in preterm newborns based on risk information available within the first 48 h of postnatal life.
Using data submitted to the Vermont Oxford Network (VON) between 1995 and 2015, we created logistic regression models based on infants born <28 completed weeks gestational age. We developed a model with 60% of the data and identified birth weight, gestational age, respiratory distress syndrome, non-Hispanic ethnicity, and multiple gestation as predictors of ROP. We tested the model in the remaining 40%, performed tenfold cross-validation, and tested the score in ELGAN study data.
Of the 1,052 newborns in the VON database, 627 recorded an ROP status. Forty percent had no ROP, 40% had mild ROP (stages 1 and 2), and 20% had severe ROP (stages 3-5). We created a weighted score to predict any ROP based on the multivariable regression model. A cutoff score of 5 had the best sensitivity (95%, 95% CI 93-97), while maintaining a strong positive predictive value (63%, 95% CI 57-68). When applied to the ELGAN data, sensitivity was lower (72%, 95% CI 69-75), but PPV was higher (80%, 95% CI 77-83).
STEP-ROP is a promising screening tool. It is easy to calculate, does not rely on extensive postnatal data collection, and can be calculated early after birth. Early ROP screening may help physicians limit patient exposure to additional risk factors, and may be useful for risk stratification in clinical trials aimed at reducing ROP.
早产儿视网膜病变（ROP）是一种早产儿疾病，其特征是未成熟视网膜中的神经血管破坏，可能导致视力损害和失明。
基于出生后48小时内可得的风险信息，开发一种用于早产儿ROP早期出生后预测的临床筛查工具。
利用1995年至2015年间提交给佛蒙特牛津网络（VON）的数据，我们基于孕周小于28周的婴儿创建了逻辑回归模型。我们用60%的数据开发了一个模型，并确定出生体重、孕周、呼吸窘迫综合征、非西班牙裔种族和多胎妊娠为ROP的预测因素。我们在其余40%的数据中测试该模型，进行十折交叉验证，并在ELGAN研究数据中测试该评分。
在VON数据库的1052例新生儿中，627例记录了ROP状态。40%没有ROP，40%有轻度ROP（1期和2期），20%有重度ROP（3 - 5期）。我们基于多变量回归模型创建了一个加权评分来预测任何ROP。截断评分为5时具有最佳敏感性（95%，95%可信区间93 - 97），同时保持较强的阳性预测值（63%，95%可信区间57 - 68）。当应用于ELGAN数据时，敏感性较低（72%，95%可信区间69 - 75），但PPV较高（80%，95%可信区间77 - 83）。
STEP - ROP是一种有前景的筛查工具。它易于计算，不依赖广泛的出生后数据收集，且可在出生后早期计算。早期ROP筛查可能有助于医生限制患者接触额外的风险因素，并且可能对旨在降低ROP的临床试验中的风险分层有用。
