Development of artificial blood loss and duration of excision score to evaluate surgical difficulty of total laparoscopic anterior resection in rectal cancer

Jingfang Lv; Xu Guan; Ran Wei; Yefeng Yin; Enrui Liu; Zhixun Zhao; Haipeng Chen; Zheng Liu; Zheng Jiang; Xishan Wang

doi:10.3389/fonc.2023.1067414

Development of artificial blood loss and duration of excision score to evaluate surgical difficulty of total laparoscopic anterior resection in rectal cancer

Front Oncol. 2023 Mar 7:13:1067414. doi: 10.3389/fonc.2023.1067414. eCollection 2023.

Authors

Jingfang Lv¹, Xu Guan¹, Ran Wei¹, Yefeng Yin¹, Enrui Liu¹, Zhixun Zhao¹, Haipeng Chen¹, Zheng Liu¹, Zheng Jiang¹, Xishan Wang¹

Affiliation

¹ 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.

Abstract

Purpose: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: BLADE score system; random forest algorithm; rectal cancer; surgical difficulty; totally laparoscopic anterior resection.

Grants and funding

This work was supported by the National Natural Science Foundation of China [grant number 82100598], the Sanming Project of Medicine in Shenzhen [grant number SZSM201911012], and the National Key R&D Program for Young Scientists [grant number 2022YFC2505700].