Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Division of Perinatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan.
Acta Obstet Gynecol Scand. 2021 Jun;100(6):1089-1096. doi: 10.1111/aogs.14136. Epub 2021 Mar 18.
In extremely and very preterm infants, predicting individual risks for adverse outcomes antenatally is challenging but necessary for risk-stratified perinatal management and parents' participation in decision-making about treatment. Our aim was to develop and validate prediction models for short-term (neonatal period) and medium-term (3 years of age) outcomes based on antenatal maternal and fetal factors alone.
A population-based study was conducted on 31 157 neonates weighing ≤1500 g and born between 22 and 31 weeks of gestation registered in the Neonatal Research Network of Japan during 2006-2015. Short-term outcomes were assessed in 31 157 infants and medium-term outcomes were assessed in 13 751 infants among the 31 157 infants. The clinical data were randomly divided into training and validation data sets in a ratio of 2:1. The prediction models were developed by factors selected using stepwise logistic regression from 12 antenatal maternal and fetal factors with the training data set. The number of factors incorporated into the model varied from 3 to 10, on the basis of each outcome. To evaluate predictive performance, the area under the receiver operating characteristics curve (AUROC) was calculated for each outcome with the validation data set.
Among short-term outcomes, AUROCs for in-hospital death, chronic lung disease, intraventricular hemorrhage (grade III or IV) and periventricular leukomalacia were 0.85 (95% CI 0.83-0.86), 0.80 (95% CI 0.79-0.81), 0.78 (95% CI 0.75-0.80), and 0.58 (95% CI 0.55-0.61), respectively. Among medium-term outcomes, AUROCs for cerebral palsy and developmental quotient of <70 at 3 years of age were 0.66 (95% CI 0.63-0.69) and 0.72 (95% CI 0.70-0.74), respectively.
Although the predictive performance of these models varied for each outcome, their discriminative ability for in-hospital death, chronic lung disease, and intraventricular hemorrhage (grade III or IV) was relatively good. We provided a bedside prediction tool for calculating the likelihood of various infant complications for clinical use. To develop these prediction models would be valuable in each country, and these risk assessment tools could facilitate risk-stratified perinatal management and parents' shared understanding of their infants' subsequent risks.
在极早产儿和超早产儿中，产前预测个体不良结局的风险具有挑战性，但这对于进行风险分层围产期管理和让父母参与治疗决策是必要的。我们的目的是基于产前母婴和胎儿因素，建立并验证短期（新生儿期）和中期（3 岁）结局的预测模型。
本研究为基于人群的队列研究，共纳入 2006 年至 2015 年期间在日本新生儿研究网络登记的出生体重≤1500g、胎龄为 22-31 周的 31157 例新生儿。在 31157 例新生儿中评估了短期结局，在其中的 13751 例新生儿中评估了中期结局。临床数据采用 2:1 的比例随机分为训练数据集和验证数据集。使用逐步逻辑回归从 12 项产前母婴和胎儿因素中选择因素，通过训练数据集建立预测模型。基于每个结局，模型中纳入的因素数量从 3 个到 10 个不等。使用验证数据集计算每个结局的接收者操作特征曲线下面积（AUROC），以评估预测性能。
在短期结局中，院内死亡、慢性肺疾病、脑室内出血（III 级或 IV 级）和脑室周围白质软化的 AUROC 分别为 0.85（95%CI 0.83-0.86）、0.80（95%CI 0.79-0.81）、0.78（95%CI 0.75-0.80）和 0.58（95%CI 0.55-0.61）。在中期结局中，3 岁时脑瘫和发育商<70 的 AUROC 分别为 0.66（95%CI 0.63-0.69）和 0.72（95%CI 0.70-0.74）。
尽管这些模型对每个结局的预测性能不同，但它们对院内死亡、慢性肺疾病和脑室内出血（III 级或 IV 级）的鉴别能力相对较好。我们提供了一种床边预测工具，用于计算各种婴儿并发症的发生概率，供临床使用。在每个国家开发这些预测模型都具有重要价值，这些风险评估工具有助于进行风险分层围产期管理，并使父母能够更好地了解其婴儿的后续风险。
