Longitudinal extrauterine growth restriction in extremely preterm infants: current status and prediction model
10.3760/cma.j.issn.2096-2932.2024.03.003
- VernacularTitle:超早产儿纵向宫外生长迟缓现状调查及预测模型的建立
- Author:
Xiaofang HUANG
1
;
Qi FENG
;
Shuaijun LI
;
Xiuying TIAN
;
Yong JI
;
Ying ZHOU
;
Bo TIAN
;
Yuemei LI
;
Wei GUO
;
Shufen ZHAI
;
Haiying HE
;
Xia LIU
;
Rongxiu ZHENG
;
Shasha FAN
;
Li MA
;
Hongyun WANG
;
Xiaoying WANG
;
Shanyamei HUANG
;
Jinyu LI
;
Hua XIE
;
Xiaoxiang LI
;
Pingping ZHANG
;
Hua MEI
;
Yanju HU
;
Ming YANG
;
Lu CHEN
;
Yajing LI
;
Xiaohong GU
;
Shengshun QUE
;
Xiaoxian YAN
;
Haijuan WANG
;
Lixia SUN
;
Liang ZHANG
;
Jiuye GUO
Author Information
1. 北京大学第一医院儿科，北京 100034
- Keywords:
Longitudinal;
Extrauterine growth restriction;
Current status;
Prediction model;
Extremely preterm infants
- From:Chinese Journal of Neonatology
2024;39(3):136-144
- CountryChina
- Language:Chinese
-
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.
