目的 基于新生儿重症监护室（neonatal intensive care unit，NICU）的多中心数据，进行胎龄<28周超早产儿（extremely preterm infants，EPIs）纵向宫外生长迟缓（extrauterine growth restriction，EUGR）现状调查，并建立预测模型。 方法 回顾性研究2017年1月至2018年12月华北地区32个NICU收治的EPIs一般情况、营养支持、住院期间并发症及体重增长情况等临床资料。出院体重Z评分较入院时下降>1定义为纵向EUGR，将EPIs分为纵向EUGR组及非纵向EUGR组，总结EPIs营养支持及体重增长现状。将EPIs按7∶3的比例随机分为训练集和验证集。在训练集中采用单因素和多因素回归分析筛选纵向EUGR的独立危险因素，利用赤池信息准则决定最优Nomogram模型并绘制列线图。对模型进行区分度、校准度和临床决策曲线评价。 结果 共纳入436例EPIs，胎龄（26.9±0.9）周，出生体重（989±171）g，纵向RUGR发生率82.3%（359/436）。最终纳入出生体重Z评分、体重下降程度、体重增长速率、出院前3 d母乳喂养比例≥75%、机械通气≥7 d、母亲完成产前促肺治疗、支气管肺发育不良7个变量构建列线图。训练集和验证集的受试者工作特征曲线下面积分别为 0.870（95%CI 0.820~0.920）和0.879（95%CI 0.815~0.942），提示模型区分度良好。校准曲线提示模型存在较好的拟合度（P>0.05）。临床决策曲线分析表明模型在所有阈值下均有正向获益。 结论 目前EPIs纵向ERGR发生率较高，本研究构建并验证了EPIs出院时纵向EUGR的预测模型，有助于尽早识别纵向EUGR高危的EPIs并进行干预。未来研究有必要扩大样本量和进行前瞻性研究来优化和验证该预测模型。 Objective To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs. Methods From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis. Results A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95%CI 0.820-0.920) and 0.879 (95%CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model (P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.
Abstract
Objective To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs. Methods From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis. Results A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95%CI 0.820-0.920) and 0.879 (95%CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model (P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.
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