生后第一周个体化体重变化预测。

Personalized weight change prediction in the first week of life.

机构信息

Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital (UKBB), Basel, Switzerland.

Division of Neonatology, University of Basel Children's Hospital (UKBB), Basel, Switzerland.

出版信息

Clin Nutr. 2019 Apr;38(2):689-696. doi: 10.1016/j.clnu.2018.04.001. Epub 2018 Apr 11.

DOI:10.1016/j.clnu.2018.04.001

PMID:29703559

Abstract

BACKGROUND & AIMS: Almost all neonates show physiological weight loss and consecutive weight gain after birth. The resulting weight change profiles are highly variable as they depend on multiple neonatal and maternal factors. This limits the value of weight nomograms for the early identification of neonates at risk for excessive weight loss and related morbidities. The objective of this study was to characterize weight changes and the effect of supplemental feeding in late preterm and term neonates during the first week of life, to identify and quantify neonatal and maternal influencing factors, and to provide an educational online prediction tool.

METHODS

Longitudinal weight data from 3638 healthy term and late preterm neonates were prospectively recorded up to 7 days of life. Two-thirds (n = 2425) were randomized to develop a semi-mechanistic model characterizing weight change as a balance between time-dependent rates of weight gain and weight loss. The dose-dependent effect of supplemental feeding on weight gain was characterized. A population analysis applying nonlinear mixed-effects modeling was performed using NONMEM 7.3. The model was evaluated on the remaining third of neonates (n = 1213).

RESULTS

Key population characteristics (median [range]) of the whole sample were gestational age 39.9 [34.4-42.4] weeks, birth weight 3400 [1980-5580] g, maternal age 32 [15-51] years, cesarean section 26%, and girls 50%. The model demonstrated good predictive performance (bias 0.01%, precision 0.56%), and is able to accurately predict individual weight change (bias 0.15%, precision 1.43%) and the dose-dependent effects of supplemental feeding up to 1 week after birth based on weight measurements during the first 3 days of life, including birth weight, and the following characteristics: gestational age, gender, delivery mode, type of feeding, maternal age, and parity.

CONCLUSIONS

We present the first mathematical model not only to describe weight change in term and late preterm neonates but also to provide an educational online tool for personalized weight prediction in the first week of life.

摘要

背景与目的

几乎所有新生儿在出生后都会经历生理性体重下降和随后的体重增加。体重变化的情况因受多种新生儿和产妇因素的影响而存在较大差异，这限制了体重图表在早期识别体重下降过多及相关并发症风险方面的作用。本研究的目的是描述晚期早产儿和足月儿在生命的第一周内体重变化情况，并确定和量化影响新生儿和产妇的因素，提供一个在线预测工具。

方法

前瞻性记录了 3638 例健康足月儿和晚期早产儿的体重纵向数据，直至生命的第 7 天。其中三分之二（n=2425）随机分组，以建立一个半机械模型，将体重变化描述为体重增加和体重下降的时间依赖性速率之间的平衡。同时描述了补充喂养对体重增加的剂量依赖性影响。采用 NONMEM 7.3 进行非线性混合效应模型分析。使用剩余的三分之一新生儿（n=1213）对模型进行评估。

结果

全样本的关键人口统计学特征（中位数[范围]）为：胎龄 39.9[34.4-42.4]周，出生体重 3400[1980-5580]g，产妇年龄 32[15-51]岁，剖宫产率 26%，女孩占 50%。该模型具有良好的预测性能（偏差 0.01%，精度 0.56%），能够准确预测个体体重变化（偏差 0.15%，精度 1.43%）以及出生后第 1 周内补充喂养的剂量依赖性影响，依据生命最初 3 天的体重测量值，包括出生体重以及以下特征：胎龄、性别、分娩方式、喂养类型、产妇年龄和产次。