Cao Jennifer H, Wagner Brandie D, McCourt Emily A, Cerda Ashlee, Sillau Stefan, Palestine Alan, Enzenauer Robert W, Mets-Halgrimson Rebecca B, Paciuc-Beja Miguel, Gralla Jane, Braverman Rebecca S, Lynch Anne
Department of Ophthalmology, University of Colorado School of Medicine, Aurora.
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora.
J AAPOS. 2016 Feb;20(1):19-24. doi: 10.1016/j.jaapos.2015.10.017.
To describe a novel retinopathy of prematurity (ROP) screening model incorporating birth weight, gestational age, and postnatal weight gain that maintains sensitivity but improves specificity in detecting all grades of ROP compared to current 2013 screening guidelines.
The medical records of 499 neonates from a single tertiary referral center who met the 2013 screening guidelines for ROP were retrospectively reviewed. Weekly weights were analyzed using standard logistic regression to determine the age at which the weekly net weight gain best predicted the development of ROP, which was designated as the postnatal weight gain criterion. The 2013 birth weight and gestational age criteria were included in an "and" fashion to form the CO-ROP model. Sensitivities and specificities in detecting high grade (type 1 and 2) and all grades of ROP were calculated.
The CO-ROP model screens infants with a gestational age at birth of ≤30 weeks and birth weight of ≤1500 g and net weight gain of ≤650 g between birth and 1 month of age. In our cohort, CO-ROP had a sensitivity of 100% (95% CI, 92.1%-100.0%) for high-grade (type 1 and 2) ROP and 96.4% (95% CI, 92.3%-98.7%) for all grades of ROP. It would reduce the number of infants screened by 23.7% compared to 2013 guidelines. Calibrating the model to detect only high-grade ROP would result in a 45.9% reduction in the total number of infants screened.
CO-ROP is a simple model that maintains a statistically similar sensitivity in detecting all grades of ROP while significantly reducing the total number of required ROP screenings compared to 2013 guidelines. The study had a small sample size but shows promise for future research and clinical efforts.
描述一种新型的早产儿视网膜病变(ROP)筛查模型,该模型纳入了出生体重、胎龄和出生后体重增加情况,与当前2013年筛查指南相比,在检测所有等级的ROP时保持敏感性但提高了特异性。
回顾性分析来自单一三级转诊中心的499例符合2013年ROP筛查指南的新生儿的病历。使用标准逻辑回归分析每周体重,以确定每周净体重增加最能预测ROP发生的年龄,该年龄被指定为出生后体重增加标准。2013年的出生体重和胎龄标准以“与”的方式纳入,形成CO-ROP模型。计算检测高级别(1型和2型)和所有等级ROP的敏感性和特异性。
CO-ROP模型筛查出生时胎龄≤30周、出生体重≤1500g且出生至1月龄期间净体重增加≤650g的婴儿。在我们的队列中,CO-ROP对高级别(1型和2型)ROP的敏感性为100%(95%CI,92.1%-100.0%),对所有等级ROP的敏感性为96.4%(95%CI,92.3%-98.7%)。与2013年指南相比,它将筛查的婴儿数量减少23.7%。校准模型以仅检测高级别ROP将导致筛查的婴儿总数减少45.9%。
CO-ROP是一个简单的模型,在检测所有等级的ROP时保持统计学上相似的敏感性,同时与2013年指南相比显著减少所需的ROP筛查总数。该研究样本量较小,但为未来研究和临床工作显示出前景。