Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, No.253 Gongyedadao Middle Road, Guangzhou, 510282, Guangdong, China.
Department of Ophthalmology, Peking Union Medical College Hospital, Beijing, China.
BMC Ophthalmol. 2021 May 27;21(1):236. doi: 10.1186/s12886-021-01952-0.
We aimed to validate the predictive performance of the DIGIROP-Birth model for identifying treatment-requiring retinopathy of prematurity (TR-ROP) in Chinese preterm infants to evaluate its generalizability across countries and races.
We retrospectively reviewed the medical records of preterm infants who were screened for retinopathy of prematurity (ROP) in a single Chinese hospital between June 2015 and August 2020. The predictive performance of the model for TR-ROP was assessed through the construction of a receiver-operating characteristic (ROC) curve and calculating the areas under the ROC curve (AUC), sensitivity, specificity, and positive and negative predictive values.
Four hundred and forty-two infants (mean (SD) gestational age = 28.8 (1.3) weeks; mean (SD) birth weight = 1237.0 (236.9) g; 64.7% males) were included in the study. Analyses showed that the DIGIROP-Birth model demonstrated less satisfactory performance than previously reported in identifying infants with TR-ROP, with an area under the receiver-operating characteristic curve of 0.634 (95% confidence interval = 0.564-0.705). With a cutoff value of 0.0084, the DIGIROP-Birth model showed a sensitivity of 48/93 (51.6%), which increased to 89/93 (95.7%) after modification with the addition of postnatal risk factors. In infants with a gestational age < 28 weeks or birth weight < 1000 g, the DIGIROP-Birth model exhibited sensitivities of 36/39 (92.3%) and 20/23 (87.0%), respectively.
Although the predictive performance was less satisfactory in China than in developed countries, modification of the DIGIROP-Birth model with postnatal risk factors shows promise in improving its efficacy for TR-ROP. The model may also be effective in infants with a younger gestational age or with an extremely low birth weight.
我们旨在验证 DIGIROP-Birth 模型在中国早产儿中预测治疗性早产儿视网膜病变（TR-ROP）的性能，以评估其在不同国家和种族中的通用性。
我们回顾性分析了 2015 年 6 月至 2020 年 8 月期间在中国一家医院接受早产儿视网膜病变（ROP）筛查的早产儿的病历。通过构建受试者工作特征（ROC）曲线和计算 ROC 曲线下面积（AUC）、灵敏度、特异性、阳性和阴性预测值来评估该模型对 TR-ROP 的预测性能。
共有 442 名婴儿（平均（标准差）胎龄=28.8（1.3）周；平均（标准差）出生体重=1237.0（236.9）g；64.7%为男性）纳入研究。分析表明，与先前报道相比，DIGIROP-Birth 模型在识别患有 TR-ROP 的婴儿方面表现出较差的性能，ROC 曲线下面积为 0.634（95%置信区间=0.564-0.705）。当截断值为 0.0084 时，DIGIROP-Birth 模型的灵敏度为 48/93（51.6%），在添加出生后危险因素后修改为 89/93（95.7%）。在胎龄<28 周或出生体重<1000g 的婴儿中，DIGIROP-Birth 模型的灵敏度分别为 36/39（92.3%）和 20/23（87.0%）。
尽管该模型在中国的预测性能不如发达国家，但添加出生后危险因素后修改的 DIGIROP-Birth 模型显示出提高 TR-ROP 疗效的潜力。该模型在胎龄较小或出生体重极低的婴儿中也可能有效。
