Suppr超能文献

新生儿低血糖危险因素预测模型的建立:一项回顾性研究。

Development of a prediction model for neonatal hypoglycemia risk factors: a retrospective study.

机构信息

School of Nursing, Wuhan University, Wuhan, Hubei, China.

Department of Obstetrics, Wuhan Central Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

出版信息

Front Endocrinol (Lausanne). 2023 Jul 17;14:1199628. doi: 10.3389/fendo.2023.1199628. eCollection 2023.

Abstract

BACKGROUND

It's challenging for healthcare workers to detect neonatal hypoglycemia due to its rapid progression and lack of aura symptoms. This may lead to brain function impairment for the newborn, placing a significant care burden on the family and creating an economic burden for society. Tools for early diagnosis of neonatal hypoglycemia are lacking. This study aimed to identify newborns at high risk of developing neonatal hypoglycemia early by developing a risk prediction model.

METHODS

Using a retrospective design, pairs (470) of women and their newborns in a tertiary hospital from December 2021 to September 2022 were included in this study. Socio-demographic data and clinical data of mothers and newborns were collected. Univariate and multivariate logistic regression were used to screen optimized factors. A neonatal hypoglycemia risk nomogram was constructed using R software, and the calibration curve and receiver operator characteristic curve (ROC) was utilized to evaluate model performance.

RESULTS

Factors integrated into the prediction risk nomogram were maternal age (odds ratio [OR] =1.10, 95% CI: 1.04, 1.17), fasting period (OR=1.07, 95% CI: 1.03, 1.12), ritodrine use (OR=2.00, 95% CI: 1.05, 3.88), gestational diabetes mellitus (OR=2.13, 95% CI: 1.30, 3.50), gestational week (OR=0.80, 95% CI: 0.66, 0.96), fetal distress (OR=1.76, 95% CI: 1.11, 2.79) and neonatal body mass index (OR=1.50, 95% CI: 1.24, 1.84). The area under the curve (AUC) was 0.79 (95% confidence interval [CI]: 0.75, 0.82), specificity was 0.82, and sensitivity was 0.62.

CONCLUSION

The prediction model of this study demonstrated good predictive performance. The development of the model identifies advancing maternal age, an extended fasting period before delivery, ritodrine use, gestational diabetes mellitus diagnosis, fetal distress diagnosis and an increase in neonatal body mass index increase the probability of developing neonatal hypoglycemia, while an extended gestational week reduces the probability of developing neonatal hypoglycemia.

摘要

背景

由于新生儿低血糖的快速进展和缺乏先兆症状,医护人员难以发现新生儿低血糖。这可能导致新生儿脑功能受损,给家庭带来沉重的护理负担,并给社会造成经济负担。目前缺乏早期诊断新生儿低血糖的工具。本研究旨在通过建立风险预测模型,尽早发现有发生新生儿低血糖风险的新生儿。

方法

采用回顾性设计,纳入 2021 年 12 月至 2022 年 9 月在一家三级医院分娩的 470 对母婴。收集母亲和新生儿的社会人口学数据和临床数据。采用单因素和多因素逻辑回归筛选优化因素。使用 R 软件构建新生儿低血糖风险预测列线图,并利用校准曲线和受试者工作特征曲线(ROC)评估模型性能。

结果

纳入预测风险列线图的因素包括母亲年龄(比值比[OR] =1.10,95%可信区间:1.04,1.17)、空腹时间(OR=1.07,95%可信区间:1.03,1.12)、利托君使用(OR=2.00,95%可信区间:1.05,3.88)、妊娠期糖尿病(OR=2.13,95%可信区间:1.30,3.50)、妊娠周数(OR=0.80,95%可信区间:0.66,0.96)、胎儿窘迫(OR=1.76,95%可信区间:1.11,2.79)和新生儿体重指数(OR=1.50,95%可信区间:1.24,1.84)。曲线下面积(AUC)为 0.79(95%置信区间[CI]:0.75,0.82),特异性为 0.82,敏感性为 0.62。

结论

本研究的预测模型具有良好的预测性能。该模型的建立表明,母亲年龄增加、分娩前空腹时间延长、利托君使用、妊娠期糖尿病诊断、胎儿窘迫诊断以及新生儿体重指数增加会增加新生儿低血糖发生的概率,而妊娠周数延长会降低新生儿低血糖发生的概率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc28/10389046/677e2131465c/fendo-14-1199628-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验