医学
列线图
腹部外科
外科
结直肠癌
放化疗
术前护理
血管外科
逻辑回归
癌症
心脏外科
内科学
放射治疗
作者
Yanwu Sun,Jianhua Chen,Chengwei Ye,Huiming Lin,Xingrong Lu,Ying Huang,Pan Chi
标识
DOI:10.1007/s00268-021-06080-w
摘要
Abstract Aim Laparoscopic total mesorectal excision (LaTME) following preoperative chemoradiotherapy (PCRT) in locally advanced rectal cancer (LARC) is technically demanding. The present study is intended to evaluate predictive factors of surgical difficulty of LaTME following PCRT by using pelvimetric and nutritional factors. Method Consecutive LARC patients receiving LaTME after PCRT were included. Surgical difficulty was classified based upon intraoperative (operation time, blood loss, and conversion) and postoperative outcomes (postoperative hospital stay and morbidities). Pelvimetry was performed using preoperative T2‐weighted MRI. Nutritional factors such as albumin‐to‐globulin ratio (AGR) and prognostic nutritional index (PNI) were calculated. Multivariable logistic analysis was used to identify predictors of high surgical difficulty. A predictive nomogram was developed and validated internally. Results Among 294 patients included, 36 (12.4%) patients were graded as high surgical difficulty. Logistic regression analysis demonstrated that previous abdominal surgery (OR = 6.080, P = 0.001), tumor diameter (OR = 1.732, P = 0.003), intersphincteric resection (vs. low anterior resection, OR = 13.241, P < 0.001), interspinous distance (OR = 0.505, P = 0.009), and preoperative AGR (OR = 0.041, P = 0.024) were independently predictive of high surgical difficulty of LaTME after PCRT. Then, a predictive nomogram was built (C‐index = 0.867). Conclusion Besides previous abdominal surgery, type of surgery (intersphincteric resection), tumor diameter, and interspinous distance, we found that preoperative AGR could be useful for the prediction of surgical difficulty of LaTME after PCRT. A predictive nomogram for surgical difficulty may aid in planning an appropriate approach for rectal cancer surgery after PCRT.
科研通智能强力驱动
Strongly Powered by AbleSci AI