Predicting length of stay after proximal femoral endoprosthetic replacement for oncological conditions

SURGEON-JOURNAL OF THE ROYAL COLLEGES OF SURGEONS OF EDINBURGH AND IRELAND(2022)

引用 0|浏览11
暂无评分
摘要
Background: Endoprosthetic replacement of the proximal femur plays a vital role in managing metastatic and primary bone tumours1. Length of stay (LOS) has important resource implications but is driven by patient and disease factors over and above the procedure itself. The aim of this project was to identify factors that drive LOS in patients undergoing Methods: This was a retrospective analysis of clinical records from a single centre (RNOH). 144 cases were identified over a 4 year-period. These were divided into 3 diagnostic categories: primary bone tumour with chemotherapy, primary bone tumour without chemotherapy and metastatic bone disease. Several factors were considered that could influence the length of stay including age, ASA grade, gender, admission to the high dependency unit (HDU), diagnosis, saving the greater trochanter, pre-operative radiotherapy, admission with a fracture and return to theatre. Results: The median LOS for PFR was 15 days, with 79% admitted to HDU. LOS was almost doubled for patients returning to theatre (P 1/4 0.04). Patients with ASA grades of 3 and 4 had a 75% longer LOS compared to those with grade 1. Additionally, a 10-year increase in age was associated with a 6-8% increase in LOS. Incorporating these factors produced a model which explained 27% of the variability of LOS. Conclusion: Majority of the variables which were tested were significantly associated with LOS. However, factors other than those in our model drive length of stay. This analysis can support conversations with patients and service planning around LOS. (c) 2021 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.
更多
查看译文
关键词
Endoprosthetic replacements,Length of stay,Metastatic bone disease,Primary bone tumours
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要