Syuhada Khreshna, Wanda Dessie, Nur'aini Risti, Ardiantari Chairun, Susilo Ayu
Statistics Research Division, Institut Teknologi Bandung, Bandung, Indonesia.
Faculty of Nursing, Universitas Indonesia, Depok, Indonesia.
J Nutr Metab. 2020 Jun 9;2020:4305487. doi: 10.1155/2020/4305487. eCollection 2020.
Malnutrition is a global health problem and challenge for every country. It may occur in any form and affect all levels of age including children. We pay particular attention to the so-called hospital-acquired malnutrition (HaM) for pediatric patients. Our aim was to explore statistical risk factors or characteristics as well as to forecast risk scoring for such malnutrition.
This study employed a cross-sectional design involving children from 1 month to 18 years of age who were hospitalized for at least 72 hours. We used secondary data from 308 medical records of pediatric patients who were admitted to the hospital in 2017. We excluded the data if the patient had tumors or organomegaly, fluid retention, and dehydration. HaM was determined based on a weight loss each day during hospitalization until the day of discharge. Statistical data analysis is carried out for both descriptive and inferential statistics. Our predictive model is yielded by linear regression, and risk scoring is obtained through logistic regression.
The findings showed several risk factors or characteristics for HaM prevalence: sex, age, medical diagnosis, diet, nutrition route, and NEWS score. The early warning system to pediatric patients is conducted by calculating malnutrition-at-risk in which a value beyond 100.5 is considered as having high potential risk for HaM.
Nurses are expected to monitor pediatric patients' condition, including measuring the anthropometry regularly, in order to identify the initial signs of HaM.
营养不良是一个全球性的健康问题，对每个国家都是一项挑战。它可能以任何形式出现，并影响包括儿童在内的所有年龄段人群。我们特别关注儿科患者中所谓的医院获得性营养不良（HaM）。我们的目的是探索此类营养不良的统计风险因素或特征，并预测风险评分。
本研究采用横断面设计，纳入1个月至18岁住院至少72小时的儿童。我们使用了2017年入院的308例儿科患者病历中的二手数据。如果患者患有肿瘤或器官肿大、液体潴留和脱水，则排除其数据。HaM根据住院期间直至出院当天每天的体重减轻情况来确定。对描述性统计和推断性统计都进行了数据分析。我们的预测模型通过线性回归得出，风险评分通过逻辑回归获得。
研究结果显示了HaM患病率的几个风险因素或特征：性别、年龄、医学诊断、饮食、营养途径和NEWS评分。通过计算有营养不良风险来对儿科患者进行预警系统，其中超过100.5的值被认为具有发生HaM的高潜在风险。
期望护士监测儿科患者的状况，包括定期测量人体测量数据，以便识别HaM的初始迹象。
