Lv Jingfang, Guan Xu, Wei Ran, Yin Yefeng, Liu Enrui, Zhao Zhixun, Chen Haipeng, Liu Zheng, Jiang Zheng, Wang Xishan
Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Oncol. 2023 Mar 7;13:1067414. doi: 10.3389/fonc.2023.1067414. eCollection 2023.
Total laparoscopic anterior resection (tLAR) has been gradually applied in the treatment of rectal cancer (RC). This study aims to develop a scoring system to predict the surgical difficulty of tLAR.
RC patients treated with tLAR were collected. The blood loss and duration of excision (BLADE) scoring system was built to assess the surgical difficulty by using restricted cubic spline regression. Multivariate logistic regression was used to evaluate the effect of the BLADE score on postoperative complications. The random forest (RF) algorithm was used to establish a preoperative predictive model for the BLADE score.
A total of 1,994 RC patients were randomly selected for the training set and the test set, and 325 RC patients were identified as the external validation set. The BLADE score, which was built based on the thresholds of blood loss (60 ml) and duration of surgical excision (165 min), was the most important risk factor for postoperative complications. The areas under the curve of the predictive RF model were 0.786 in the training set, 0.640 in the test set, and 0.665 in the external validation set.
This preoperative predictive model for the BLADE score presents clinical feasibility and reliability in identifying the candidates to receive tLAR and in making surgical plans for RC patients.
全腹腔镜下前切除术(tLAR)已逐渐应用于直肠癌(RC)的治疗。本研究旨在开发一种评分系统以预测tLAR的手术难度。
收集接受tLAR治疗的RC患者。通过使用受限立方样条回归建立失血与切除时间(BLADE)评分系统来评估手术难度。采用多因素逻辑回归评估BLADE评分对术后并发症的影响。使用随机森林(RF)算法建立BLADE评分的术前预测模型。
共随机选取1994例RC患者作为训练集和测试集,并确定325例RC患者作为外部验证集。基于失血阈值(60 ml)和手术切除时间阈值(165分钟)构建的BLADE评分是术后并发症的最重要危险因素。预测性RF模型在训练集、测试集和外部验证集中的曲线下面积分别为0.786、0.640和0.665。
这种BLADE评分的术前预测模型在识别接受tLAR手术的患者以及为RC患者制定手术计划方面具有临床可行性和可靠性。