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远程医疗评估急性早产儿视网膜病变(e-ROP)研究中的 Plus 病:图像分级的特征、预测因素和准确性。

Plus Disease in Telemedicine Approaches to Evaluating Acute-Phase ROP (e-ROP) Study: Characteristics, Predictors, and Accuracy of Image Grading.

机构信息

School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania.

Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.

出版信息

Ophthalmology. 2019 Jun;126(6):868-875. doi: 10.1016/j.ophtha.2019.01.021. Epub 2019 Jan 25.

Abstract

PURPOSE

To describe characteristics and predictors of plus disease, and the accuracy of image grading for plus disease in the e-ROP Study.

DESIGN

Secondary analyses of data from 13 North American centers.

PARTICIPANTS

Premature infants with birth weight (BW) <1251 g.

METHODS

Infants underwent regularly scheduled diagnostic examinations by ophthalmologists and digital imaging by trained imagers using a wide-field digital camera. Two masked nonphysician trained readers independently evaluated images for posterior pole abnormality (normal, preplus, plus), with discrepancies adjudicated by a reading supervisor. Logistic regression models were used to determine predictors for plus disease. The sensitivity and specificity of image grading for plus disease were calculated using the clinical examination finding as reference standard.

MAIN OUTCOME MEASURES

Odds ratios (OR), sensitivity, and specificity.

RESULTS

Among 1239 infants (mean BW 864 g, mean gestational age [GA] 27 weeks), 129 infants (10%) (226 eyes, 75% bilateral) had plus disease from clinical examination. When plus disease was first diagnosed in clinical examination at median postmenstrual age (PMA) of 36 weeks (range: 32-43 weeks), 94% to 96% of plus occurred in the superior or inferior temporal quadrant. Significant predictors for plus disease from multivariate analysis were as follows: GA (OR = 3.2 for ≤24 vs. ≥28 weeks, P = 0.004), race (OR = 2.4 for white vs. black, P = 0.01), respiratory support (OR = 7.1, P = 0.006), weight gain (OR = 1.5 for weight gain ≤12 vs. >18 g/day, P = 0.03), and image findings at the first image session, including presence of preplus/plus disease (OR = 2.7, P = 0.003), ROP stage (OR = 4.2 for stage 3 ROP vs. no ROP, P = 0.006), and blot hemorrhage (OR = 3.1, P = 0.003). These features predicted plus disease with an area under the receiver operating characteristic curve of 0.89 (95% confidence interval [CI]: 0.85-0.92). The image grading using preplus as the cut point had sensitivity of 94% (95% CI: 90%-97%) and specificity of 81% (95% CI: 79%-82%) for detecting plus disease in an eye.

CONCLUSIONS

Among e-ROP infants, plus disease developed in 10% of infants at a median PMA of 37 weeks, with the majority being bilateral and mostly in the superior or inferior temporal quadrant. GA, race, respiratory support, postnatal weight gain, image findings of the posterior pole, and ROP predict development of plus disease. Nonphysician image grading can detect almost all plus disease with good specificity.

摘要

目的

描述 e-ROP 研究中累及周边的视网膜病变(plus disease)的特征和预测因素,以及图像分级对其的准确性。

设计

北美 13 个中心数据的二次分析。

参与者

出生体重(BW)<1251g 的早产儿。

方法

由眼科医生定期进行眼科检查,由经过培训的成像技师使用宽视野数字相机进行数字成像。两名独立的、非医师的经过培训的读者对眼底后极部异常(正常、preplus、plus)进行独立评估,由一位阅读主管对有分歧的结果进行裁决。采用逻辑回归模型确定 plus 疾病的预测因素。使用临床检查结果作为参考标准,计算图像分级对 plus 疾病的敏感性和特异性。

主要观察指标

比值比(OR)、敏感性和特异性。

结果

在 1239 名婴儿(平均 BW 864g,平均胎龄 [GA] 27 周)中,有 129 名婴儿(10%)(226 只眼,75%为双眼)患有临床检查发现的 plus 疾病。当临床检查中在中位胎龄(PMA)36 周(范围:32-43 周)时首次诊断为 plus 疾病时,94%-96%的 plus 疾病发生在颞上或颞下象限。多变量分析中 plus 疾病的显著预测因素如下:GA(≤24 周与≥28 周相比,OR=3.2,P=0.004)、种族(白种人 vs. 黑人,OR=2.4,P=0.01)、呼吸支持(OR=7.1,P=0.006)、体重增加(OR=1.5,体重增加≤12g/天与>18g/天相比,P=0.03),以及首次成像时的图像发现,包括存在 preplus/plus 疾病(OR=2.7,P=0.003)、ROP 分期(OR=4.2,3 期 ROP 与无 ROP 相比,P=0.006)和斑点状出血(OR=3.1,P=0.003)。这些特征对 plus 疾病的预测,其受试者工作特征曲线下面积为 0.89(95%置信区间:0.85-0.92)。使用 preplus 作为截断点的图像分级对检测眼内的 plus 疾病具有 94%的敏感性(95%置信区间:90%-97%)和 81%的特异性(95%置信区间:79%-82%)。

结论

在 e-ROP 婴儿中,在中位 PMA 为 37 周时,约 10%的婴儿出现 plus 疾病,大多数为双眼发病,且主要发生在颞上或颞下象限。GA、种族、呼吸支持、出生后体重增加、后极部图像表现和 ROP 可预测 plus 疾病的发生。非医师的图像分级可检测出几乎所有的 plus 疾病,具有良好的特异性。

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