Perioperative Multicomponent Interdisciplinary Program Reduces Delirium Incidence in Elderly Patients With Hip Fracture.
Xin ZhaoWei YuanPublished in: Journal of the American Psychiatric Nurses Association (2020)
BACKGROUND: Delirium is common in elderly patients with hip fracture. Although several multicomponent care pathways have been developed, few nurse-led perioperative multicomponent programs have been evaluated. OBJECTIVES: The current study aimed to evaluate the effect of a nurse-led perioperative multicomponent interdisciplinary program in preventing postoperative delirium in elderly patients with hip fracture. METHOD: The participants in the usual care group were recruited from March 2012 to February 2013, and these in the experimental group were recruited from May 2013 to June 2014. The participants in the usual care group (n = 174) received usual medical and nursing care from admission to hospital discharge and the participants in the experimental group (n = 192) received the nurse-led perioperative multicomponent interdisciplinary intervention. The STROBE checklist was used to report this study. RESULTS: There were no statistical differences between the two cohorts in terms of the baseline data such as gender, age, fracture type, and so on. The experimental group had a lower incidence of delirium and postoperative hypoxia than the usual care group. No statistical differences in terms of delirium severity, delirium duration, and mean hospitalization length were observed. CONCLUSIONS: The nurse-led perioperative multicomponent interdisciplinary program described in the current study is feasible and effective in reducing the incidence of postoperative delirium in elderly patients with hip fracture.
Keyphrases
- hip fracture
- patients undergoing
- quality improvement
- healthcare
- cardiac surgery
- primary care
- palliative care
- risk factors
- randomized controlled trial
- emergency department
- public health
- pain management
- acute kidney injury
- machine learning
- middle aged
- affordable care act
- electronic health record
- mental health
- artificial intelligence
- big data
- data analysis