Predictive Value Of Intraoperative Factors For Complications After Oesophagectomy
INTERACTIVE CARDIOVASCULAR AND THORACIC SURGERY(2019)
摘要
OBJECTIVES: Oesophagectomy for malignancy is a highly complex and difficult procedure associated with considerable postoperative complications. In this study, we aimed to identify the ability of an intraoperative factor (IPFs)-based classifier to predict complications after oesophagectomy.METHODS: This retrospective review included 251 patients who underwent radical oesophagectomy from October 2015 to December 2017. Using the least absolute shrinkage and selection operator regression model, we extracted IPFs that were associated with postoperative morbidity and then built a classifier. Preoperative variables and the IPF-based classifier were analysed using univariable and multivariable logistic regression analysis. A nomogram to predict the risk of postoperative morbidity was constructed and validated using bootstrap resampling.RESULTS: Following the least absolute shrinkage and selection operator regression analysis, we discovered that those 4 IPF (surgical approach, lowest heart rate, lowest mean arterial blood pressure and estimated blood loss) were associated with postoperative morbidity. After stratification into low-and high-risk groups with the IPF-based classifier, the differences in 30-day morbidity (7.2% vs 70.1%, P < 0.001, respectively) and mortality (0% vs 4.7%, P= 0.029, respectively) were found to be statistically significant. The multivariable analysis demonstrated that the IPF-based classifier was an independent risk factor for predicting postoperative morbidity for patients with oesophageal cancer. The performance of the nomogram was evaluated and proven to be clinically useful.CONCLUSIONS: We demonstrated that an IPF-based nomogram could reliably predict the risk of postoperative morbidity. It has the potential to facilitate the individual perioperative management of patients with oesophageal cancer.
更多查看译文
关键词
Oesophagectomy, Intraoperative factors, Complication, Least absolute shrinkage and selection operator, Nomogram
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