8-10 岁儿童蛋白质-能量营养不良的分析和预测。

IF 1.7 4区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL American journal of translational research Pub Date : 2024-10-15 eCollection Date: 2024-01-01 DOI:10.62347/QIFY1619
Yunyan Deng, Yanmei Ye, Sisi Chen, Yawen Liang, Xiaoyan Chen
{"title":"8-10 岁儿童蛋白质-能量营养不良的分析和预测。","authors":"Yunyan Deng, Yanmei Ye, Sisi Chen, Yawen Liang, Xiaoyan Chen","doi":"10.62347/QIFY1619","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To identify independent risk factors for protein-energy malnutrition (PEM) in children aged 8-10 years and to develop and validate a nomogram model for estimating PEM risk.</p><p><strong>Methods: </strong>In this retrospective study, a cohort of 1,412 children from The Fifth Affiliated Hospital of Guangzhou Medical University, spanning January 2022 to December 2023, was identified. Participants were randomly classified into a training set (n=988) and a validation set (n=424). Patients in the training set were divided into normal (n=667) and PEM (n=321) groups. Data collection involved demographic, sociological, physical, and biochemical assessments. Independent risk factors for PEM were identified using univariate and multivariate logistic regression. A nomogram risk model was constructed from significant predictors, and its performance was assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). An independent dataset further validated the nomogram model.</p><p><strong>Results: </strong>Among the 1,412 children, 497 (35.2%) had PEM, which included stunting (11.83%), underweight (11.61%), and wasting (11.76%). Multivariate analysis identified six independent risk factors for PEM: gestational age (OR (95% CI)=5.830 (3.604-9.431), P<0.001), household income (OR (95% CI)=0.383 (0.281-0.523), P<0.001), sleep duration (OR (95% CI)=1.800 (1.319-2.457), P<0.001), mood disorders (OR (95% CI)=6.924 (4.437-10.805), P<0.001), and physical activity time (OR (95% CI)=3.210 (2.342-4.400), P<0.001). The nomogram model demonstrated good predictive performance (AUC=0.803 (0.773-0.832)) and was validated well on an independent dataset (AUC=0.783 (0.739-0.828)).</p><p><strong>Conclusion: </strong>The study identified key independent risk factors for PEM in children and established a robust nomogram model for clinical risk assessment. The model's high predictive accuracy and clinical applicability suggest it may be a valuable tool for the early identification and intervention strategies for PEM in clinical practice.</p>","PeriodicalId":7731,"journal":{"name":"American journal of translational research","volume":"16 10","pages":"5564-5574"},"PeriodicalIF":1.7000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558431/pdf/","citationCount":"0","resultStr":"{\"title\":\"Analysis and prediction of protein-energy malnutrition in children aged 8-10 years.\",\"authors\":\"Yunyan Deng, Yanmei Ye, Sisi Chen, Yawen Liang, Xiaoyan Chen\",\"doi\":\"10.62347/QIFY1619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To identify independent risk factors for protein-energy malnutrition (PEM) in children aged 8-10 years and to develop and validate a nomogram model for estimating PEM risk.</p><p><strong>Methods: </strong>In this retrospective study, a cohort of 1,412 children from The Fifth Affiliated Hospital of Guangzhou Medical University, spanning January 2022 to December 2023, was identified. Participants were randomly classified into a training set (n=988) and a validation set (n=424). Patients in the training set were divided into normal (n=667) and PEM (n=321) groups. Data collection involved demographic, sociological, physical, and biochemical assessments. Independent risk factors for PEM were identified using univariate and multivariate logistic regression. A nomogram risk model was constructed from significant predictors, and its performance was assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). An independent dataset further validated the nomogram model.</p><p><strong>Results: </strong>Among the 1,412 children, 497 (35.2%) had PEM, which included stunting (11.83%), underweight (11.61%), and wasting (11.76%). Multivariate analysis identified six independent risk factors for PEM: gestational age (OR (95% CI)=5.830 (3.604-9.431), P<0.001), household income (OR (95% CI)=0.383 (0.281-0.523), P<0.001), sleep duration (OR (95% CI)=1.800 (1.319-2.457), P<0.001), mood disorders (OR (95% CI)=6.924 (4.437-10.805), P<0.001), and physical activity time (OR (95% CI)=3.210 (2.342-4.400), P<0.001). The nomogram model demonstrated good predictive performance (AUC=0.803 (0.773-0.832)) and was validated well on an independent dataset (AUC=0.783 (0.739-0.828)).</p><p><strong>Conclusion: </strong>The study identified key independent risk factors for PEM in children and established a robust nomogram model for clinical risk assessment. The model's high predictive accuracy and clinical applicability suggest it may be a valuable tool for the early identification and intervention strategies for PEM in clinical practice.</p>\",\"PeriodicalId\":7731,\"journal\":{\"name\":\"American journal of translational research\",\"volume\":\"16 10\",\"pages\":\"5564-5574\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558431/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American journal of translational research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.62347/QIFY1619\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of translational research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.62347/QIFY1619","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

摘要

目的:确定 8-10 岁儿童蛋白质-能量营养不良(PEM)的独立风险因素,并开发和验证用于估计 PEM 风险的提名图模型:确定8-10岁儿童蛋白质能量营养不良(PEM)的独立风险因素,并开发和验证用于估计PEM风险的提名图模型:在这项回顾性研究中,确定了广州医科大学附属第五医院2022年1月至2023年12月期间的1412名儿童。参与者被随机分为训练集(988 人)和验证集(424 人)。训练集中的患者分为正常组(667 人)和 PEM 组(321 人)。数据收集包括人口学、社会学、物理学和生化评估。通过单变量和多变量逻辑回归确定了 PEM 的独立风险因素。根据重要的预测因素构建了一个提名图风险模型,并使用接收器操作特征曲线(ROC)、校准曲线和决策曲线分析(DCA)对其性能进行了评估。一个独立的数据集进一步验证了提名图模型:在 1,412 名儿童中,497 人(35.2%)患有 PEM,其中包括发育迟缓(11.83%)、体重不足(11.61%)和消瘦(11.76%)。多变量分析确定了 PEM 的六个独立风险因素:胎龄(OR (95% CI)=5.830 (3.604-9.431))、PC 结论:该研究确定了儿童 PEM 的主要独立风险因素,并建立了用于临床风险评估的可靠提名图模型。该模型具有很高的预测准确性和临床适用性,可作为临床实践中早期识别和干预 PEM 的重要工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Analysis and prediction of protein-energy malnutrition in children aged 8-10 years.

Objective: To identify independent risk factors for protein-energy malnutrition (PEM) in children aged 8-10 years and to develop and validate a nomogram model for estimating PEM risk.

Methods: In this retrospective study, a cohort of 1,412 children from The Fifth Affiliated Hospital of Guangzhou Medical University, spanning January 2022 to December 2023, was identified. Participants were randomly classified into a training set (n=988) and a validation set (n=424). Patients in the training set were divided into normal (n=667) and PEM (n=321) groups. Data collection involved demographic, sociological, physical, and biochemical assessments. Independent risk factors for PEM were identified using univariate and multivariate logistic regression. A nomogram risk model was constructed from significant predictors, and its performance was assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). An independent dataset further validated the nomogram model.

Results: Among the 1,412 children, 497 (35.2%) had PEM, which included stunting (11.83%), underweight (11.61%), and wasting (11.76%). Multivariate analysis identified six independent risk factors for PEM: gestational age (OR (95% CI)=5.830 (3.604-9.431), P<0.001), household income (OR (95% CI)=0.383 (0.281-0.523), P<0.001), sleep duration (OR (95% CI)=1.800 (1.319-2.457), P<0.001), mood disorders (OR (95% CI)=6.924 (4.437-10.805), P<0.001), and physical activity time (OR (95% CI)=3.210 (2.342-4.400), P<0.001). The nomogram model demonstrated good predictive performance (AUC=0.803 (0.773-0.832)) and was validated well on an independent dataset (AUC=0.783 (0.739-0.828)).

Conclusion: The study identified key independent risk factors for PEM in children and established a robust nomogram model for clinical risk assessment. The model's high predictive accuracy and clinical applicability suggest it may be a valuable tool for the early identification and intervention strategies for PEM in clinical practice.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
American journal of translational research
American journal of translational research ONCOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
自引率
0.00%
发文量
552
期刊介绍: Information not localized
期刊最新文献
The antimicrobial effect of Curcuma longa and Allium sativum decoction in rats explains its utility in wound care. Assessment of body composition-related imaging parameters indicative of sarcopenia in Chinese patients with Crohn's disease: correlation with disease severity and biologic efficacy. Role of coagulation indices in assessinghypertensive disorders in pregnancy and predicting delivery outcomes. Assessment of frailty status in patients with acute cerebral infarction and their relationship with serum markers. Serum bisphenol S levels are associated with decreased ovarian reserve function: a single-center study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1