相关实验视频

Updated: May 7, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

建立和验证一种预测模型,用于单独怀孕妇女的自发早产

Lv Zimeng^1,2, Hu Jingyuan³, Zhang Naiwen¹

¹Department of Medical Ultrasound, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, Shandong, China.

BMC pregnancy and childbirth

|September 11, 2024

概括

这项研究开发了一种简单的预测模型和评分系统,以识别高风险自发早产 (sPTB) 的孕妇. 该模型使用诸如流产史和子宫长度等因素进行早期检测和管理.

科学领域:

产科和妇科产科和妇科
生殖医学生殖医学
孕产妇和胎儿医学孕产妇和胎儿医学

背景情况:

自发早产 (sPTB) 对母亲和婴儿的健康构成重大风险.
需要一个敏感,具体,易于应用的sPTB预测模型,特别为中国人群量身定制.
建立一个评分系统有助于早期,直观和有效地评估早产风险.

研究的目的:

查自发早产 (sPTB) 的敏感和特定预测因素.
为中国妇女开发一个可操作和广泛适用的sPTB预测模型和评分系统.
促进早期,直观和有效地评估早产风险.

主要方法:

一组685名怀孕妇女在他们的第二个三个月被分为早产和非早产分组.
收集了临床和超声波数据,以确定sPTB的危险因素.
开发了一种基于名图的预测模型和评分系统,并使用ROC和C校准曲线进行外部验证.

主要成果:

多变量分析确定了流产史,先前的早产和子宫长度 (CL) 作为sPTB的显著预测因素 (P <0.05).
创建了一个基于名录的模型和评分系统,将风险分为低,中和高.
该模型表现出良好的适应性和一致性,AUC为0.850 (建模) 和0.881 (验证).

结论:

开发的预测模型适合中国的国家背景,直观,易于操作,广泛适用.
这种工具有助于在临床实践中早期检测sPTB.
它通过提供低,中和高风险的有效评估来帮助临床管理.

关键词:

预测模型的预测模型.预测因素是指预测因素.自发的早产,即早产的自发性.超声波超声波是指超声波的使用.

更多相关视频

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Published on: January 12, 2018

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

相关概念视频

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...