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利用儿童发育记录的力量,通过贝叶斯分析来检测早期神经发育障碍。

Harnessing the power of child development records to detect early neurodevelopmental disorders using Bayesian analysis.

作者信息

Hatakenaka Yuhei, Hachiya Koutaro, Åsberg Johnels Jakob, Gillberg Christopher

机构信息

Faculty of Humanities and Sociologies, University of the Ryukyus, Okinawa, Japan.

Kochi Gillberg Neuropsychiatry Centre, Kochi Prefectural Medical and Welfare Centre, Kochi, Japan.

出版信息

Acta Paediatr. 2025 Jan;114(1):180-195. doi: 10.1111/apa.17424. Epub 2024 Sep 12.

DOI:10.1111/apa.17424
PMID:39264286
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11627451/
Abstract

AIM

This study aims to analyse the developmental data from public health nurses (PHNs) to identify early indicators of neurodevelopmental disorders (NDDs) in young children using Bayesian network (BN) analysis to determine factor combinations that improve diagnosis accuracy.

METHODS

The study cohort was 501 children who underwent health checkups at 18 and 36-month. Data included demographics, pregnancy, delivery, neonatal factors, maternal interviews, and physical and neurological findings. Diagnoses were made by paediatricians and child psychiatrists using standardised tools. Predictive accuracy was assessed by the receiver operating characteristic (ROC) curve analysis.

RESULTS

We identified several infant/toddler factors significantly associated with NDD diagnoses. Predictive factors included meconium-stained amniotic fluid, 1 min Apgar score, and early developmental milestones. ROC curve analysis showed varying predictive accuracies based on evaluation timing. The 10-month checkup was valid for screening but less reliable for excluding low-risk cases. The 18-month evaluation accurately identified children at NDD risk.

CONCLUSION

The study demonstrates the potential of using developmental records for early NDD detection, emphasising early monitoring and intervention for at-risk children. These findings could guide future infant mental health initiatives in the community.

摘要

目的

本研究旨在分析公共卫生护士(PHN)的发育数据,以确定幼儿神经发育障碍(NDD)的早期指标,使用贝叶斯网络(BN)分析来确定可提高诊断准确性的因素组合。

方法

研究队列包括501名在18个月和36个月时接受健康检查的儿童。数据包括人口统计学、妊娠、分娩、新生儿因素、母亲访谈以及体格和神经学检查结果。诊断由儿科医生和儿童精神科医生使用标准化工具进行。通过受试者操作特征(ROC)曲线分析评估预测准确性。

结果

我们确定了几个与NDD诊断显著相关的婴幼儿因素。预测因素包括羊水胎粪污染、1分钟阿氏评分和早期发育里程碑。ROC曲线分析显示,根据评估时间的不同,预测准确性有所差异。10个月的检查对于筛查有效,但在排除低风险病例方面可靠性较低。18个月的评估能够准确识别有NDD风险的儿童。

结论

该研究证明了利用发育记录进行早期NDD检测的潜力,强调了对高危儿童的早期监测和干预。这些发现可为社区未来的婴幼儿心理健康倡议提供指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/167f/11627451/39a51c2e649f/APA-114-180-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/167f/11627451/888df4182cb1/APA-114-180-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/167f/11627451/0a1e6dd6fa95/APA-114-180-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/167f/11627451/39a51c2e649f/APA-114-180-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/167f/11627451/888df4182cb1/APA-114-180-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/167f/11627451/0a1e6dd6fa95/APA-114-180-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/167f/11627451/39a51c2e649f/APA-114-180-g001.jpg

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本文引用的文献

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Neuropsychiatr Dis Treat. 2022 Oct 19;18:2405-2420. doi: 10.2147/NDT.S377534. eCollection 2022.
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Association between autism spectrum disorder and peripartum events: a case-control study.自闭症谱系障碍与围产期事件的关联:病例对照研究。
Rev Paul Pediatr. 2022 Jul 6;41:e2021220. doi: 10.1590/1984-0462/2023/41/2021220. eCollection 2022.
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Pediatr Res. 2022 Jun;91(7):1906-1912. doi: 10.1038/s41390-021-01710-4. Epub 2021 Aug 21.
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Early Diagnostics and Early Intervention in Neurodevelopmental Disorders-Age-Dependent Challenges and Opportunities.神经发育障碍的早期诊断与早期干预——年龄相关的挑战与机遇
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Assessment of Cesarean Delivery and Neurodevelopmental and Psychiatric Disorders in the Children of a Population-Based Swedish Birth Cohort.基于人群的瑞典出生队列中剖宫产分娩与儿童神经发育和精神障碍的评估。
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