多学科协作护理模式在肝硬化营养不良高风险患者中的应用
Application of Multidisciplinary Collaborative Nursing Model in High-Risk Patients with Malnutrition in Liver Cirrhosis
申丽香 1王晶晶1
作者信息
- 1. 河南省人民医院消化内科,郑州大学人民医院,河南郑州 450001
- 折叠
摘要
目的 探讨多学科协作护理模式对肝硬化营养不良高风险患者营养状况及衰弱的效果.方法 选取2022年5月至2023年6月就诊于河南省人民医院的72例肝硬化患者为研究对象,随机分为对照组(36例)和干预组(36例),对照组接受常规护理,干预组在对照组基础上接受基于多学科协作的护理模式.干预前及干预8周后,采用英国皇家自由医院营养优先工具(RFH-NPT)、主观全面营养评定量表(SGA)、肝脏衰弱指数(LFI)调查工具比较两组患者健康状况及衰弱水平.结果 干预后,两组患者RFH-NPT评分、SGA评分及LFI差异有统计学意义(P＜0.05),干预组RFH-NPT评分、LFI评分均低于对照组(P＜0.05).结论 基于多学科协作的护理模式可提升肝硬化患者的营养状况,降低患者发生营养不良的风险,增强个体的体能素质,改善患者的衰弱状态.
Abstract
Objective To explore the effect of multidisciplinary collaborative nursing model on improving the nutritional status and frailty of high-risk patients with malnutrition in liver cirrhosis.Methods A total of 72 patients with liver cirrhosis who visited Henan Provincial People's Hospital from May 2022 to June 2023 were selected as the research subjects.They were randomly divided into a control group(36 cases)and an intervention group(36 cases).The control group received routine care,while the intervention group received a nursing model based on multidisciplinary cooperation on the basis of the control group.Before and 8 weeks after the intervention,the health status and frailty level of the two groups of patients were compared using the Royal Free Hospital nutrition prioritizing tool(RFH-NPT),subjective global assessment(SGA),and liver frailty index(LFI)survey tools.Results After the intervention,there was a statistically difference in RFH-NPT score,SGA score and LFI between the two groups of patients(P＜0.05).The intervention group had lower RFH-NPT score and LFI score than the control group(P＜0.05).Conclusion A multidisciplinary collaborative nursing model can improve the nutritional status of patients with liver cirrhosis,reduce the risk of malnutrition,enhance individual physical fitness,and improve their frailty.
关键词
肝硬化/多学科协作/护理/营养风险/衰弱Key words
liver cirrhosis/multidisciplinary collaboration/nursing/nutritional risks/weak引用本文复制引用
出版年
2024
NETL
NSTL
万方数据