Xi Yong, Jin Chenghua, Wang Lijie, Shen Weiyu
Department of Thoracic Surgery, Ningbo Medical Center Lihuili Eastern Hospital, Ningbo, Zhejiang, China.
Department of Thoracic Surgery, Taipei Medical University Ningbo Medical Center, Ningbo, Zhejiang, China.
Interact Cardiovasc Thorac Surg. 2019 Oct 1;29(4):525-531. doi: 10.1093/icvts/ivz150.
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.
恶性肿瘤的食管切除术是一项高度复杂且困难的手术，术后会出现相当多的并发症。在本研究中，我们旨在确定基于术中因素（IPFs）的分类器预测食管切除术后并发症的能力。
这项回顾性研究纳入了2015年10月至2017年12月期间接受根治性食管切除术的251例患者。使用最小绝对收缩和选择算子回归模型，我们提取了与术后发病率相关的IPFs，然后构建了一个分类器。术前变量和基于IPF的分类器采用单变量和多变量逻辑回归分析。构建了一个预测术后发病风险的列线图，并使用自助重采样进行验证。
经过最小绝对收缩和选择算子回归分析，我们发现这4个IPF（手术方式、最低心率、最低平均动脉血压和估计失血量）与术后发病率相关。使用基于IPF的分类器将患者分为低风险和高风险组后，发现30天发病率（分别为7.2%和70.1%，P < 0.001）和死亡率（分别为0%和4.7%，P = 0.029）的差异具有统计学意义。多变量分析表明，基于IPF的分类器是预测食管癌患者术后发病的独立危险因素。对列线图的性能进行了评估，并证明在临床上有用。
我们证明了基于IPF的列线图可以可靠地预测术后发病风险。它有可能促进食管癌患者的个体化围手术期管理。
