A National Survey of National Emergency Laparotomy Audit (NELA) Leads to Identify Current Nutrition Practices and Pathways in NELA-Eligible Emergency General Surgery Patients
BRITISH JOURNAL OF SURGERY(2023)
摘要
Aim No nutritional data are collected by the National Emergency Laparotomy Audit (NELA), despite the association of malnutrition with worse outcomes. This study aimed to describe the current practices, pathways and barriers encountered in identifying malnutrition in NELA-eligible patients. Method Following piloting and validity assessment, anaesthetic and surgical NELA Leads at hospitals across England and Wales that submitted data to NELA’s 7th report were emailed a survey link. Responses were gathered using Qualtrics. Descriptive analysis and correlation with laparotomy volume and professional subtype was performed in SPSSv26. University of Sheffield ethical approval was obtained. Results There were 166/289 NELA Leads and 117/167 hospitals that completed the survey (57.4% and 70.1% response rates, respectively). Respondents reported low rates of nutritional screening (39.2%) and assessment (45.8%) for malnutrition pre-operatively with no awareness of local screening tools (41.6%) despite 68% of units having a Nutrition Support Team. Contrary to guidelines, NELA Leads report albumin levels continue to be used to determine malnutrition risk (73.5%). Post-operative pathways were common (71.7%). Reported barriers to nutritional screening and assessment included a lack of time, training and education, organisational support, and responsibility. NELA Leads indicated nutrition risk is inadequately identified and is an important missing data item from NELA (71.2%). Responses were not correlated with hospital laparotomy volume or professional subtype. Conclusions Identifying malnutrition risk in NELA patients requires attention. Barriers are wide reaching and include a lack of time, knowledge, and ownership. Nutrition pathways that encompass the pre-operative phase and incorporation of malnutrition data in NELA may support improvements.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要