Predictive value of intraoperative factors for complications after oesophagectomy

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.

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.

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.

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.