Analysis and prediction of protein-energy malnutrition in children aged 8-10 years.

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
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Abstract

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.

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8-10 岁儿童蛋白质-能量营养不良的分析和预测。
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。
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来源期刊
American journal of translational research
American journal of translational research ONCOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
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