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基于机器学习的分析,用于预测使用围产因子在出生时体重非常低的婴儿中手术性死性肠球炎的预测:全国范围的队列研究

Seung Hyun Kim^1,2, Yoon Ju Oh³, Joonhyuk Son⁴

¹Department of Pediatrics, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.

European journal of pediatrics

|March 30, 2024

概括

这项研究开发了一种机器学习模型,用于预测使用早期围产因子在非常低出生体重 (VLBW) 婴儿中的手术性死性肠球炎 (sNEC). 早期预测可以改善患sNEC高风险的婴儿的结果.

科学领域:

新生儿医学新生儿医学
计算生物学计算生物学
儿科手术儿科手术

背景情况:

在早产婴儿中早期预测手术性死性肠球炎 (sNEC) 是至关重要的,但由于疾病的复杂性而具有挑战性.
很难识别患有手术干预高风险的非常低出生体重 (VLBW) 婴儿.
现有的机器学习 (ML) 研究通常依赖于放射图或临床症状,这可能会限制早期预测能力.

研究的目的:

开发和验证一种ML算法,用于使用围产因子预测VLBW婴儿的sNEC.
为了识别患有NEC的VLBW婴儿,这些婴儿容易发生临床恶化并需要手术干预.
改善sNEC的早期检测和管理,以改善患者的预后.

主要方法:

利用来自韩国新生儿网络 (KNN) 的16,385名VLBW婴儿的国家队列登记.
在出生后的第一周内收集了38个产周变量 (母体,产前,产后) 用于模型培训.
开发了一个集体ML模型来预测sNEC,将其与医疗治疗的NEC (mNEC) 区分开来.

主要成果:

总共有1085名婴儿患有NEC (654个sNEC,431个mNEC).
患有sNEC的VLBW婴儿在较低的妊娠年龄有更高的发病率.
拟议的整体模型实现了0.721的接收器操作特征曲线下的面积,用于sNEC预测.

结论:

开发的整体模型显示了预测哪些NEC婴儿可能会发展sNEC的潜力.
早期预测sNEC可以促进及时干预,可能改善受影响婴儿的预后.
早期围产因素的ML分析提供了一种新的方法来识别高风险VLBW婴儿的sNEC.

关键词:

机器学习机器学习死性肠道结肠炎 (NECO) 是一种新生儿重症监护病房新生儿重症监护病房非常低的出生体重非常低的出生体重.

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