Factors Influencing Difficulty of Laparoscopic Abdominoperineal Resection for Ultra-Low Rectal Cancer

医学 腹会阴切除术 逻辑回归 体质指数 肛缘 结直肠癌 放化疗 单变量分析 癌症 外科 内科学 多元分析
作者
Qiken Li,Dechuan Li,Lai Jiang,Pengnian Qiu,Zhixuan Fu,Lilong Tang,Weiping Chen
出处
期刊:Surgical laparoscopy, endoscopy & percutaneous techniques [Lippincott Williams & Wilkins]
卷期号:27 (2): 104-109 被引量:11
标识
DOI:10.1097/sle.0000000000000378
摘要

Our current study was conducted to identify patients' anatomic, pathologic, and clinical factors to predict difficulty of performing laparoscopic abdominoperineal resection for ultra-low rectal cancer.Records of 117 consecutive patients with rectal cancer 2 to 5 cm from the anal verge were retrospectively reviewed. Using univariate and multivariate linear or logistic regression models, standardized operative time and blood loss, as well as postoperative morbidity were utilized as endpoints to screen patients' multiple variables to predict operative difficulty.Multivariate linear regression analysis showed body mass index (BMI) (estimate=0.07, P=0.0056), interspinous distance (estimate=-0.02, P=0.0011), tumor distance from anal verge (estimate=-0.17, P=0.0355), prior abdominal surgery (estimate=0.51, P=0.0180), preoperative chemoradiotherapy (estimate=0.67, P=0.0146), and concurrent diseases (hypertension and/or diabetes mellitus) (estimate=0.49, P=0.0122) are predictors for standardized operative time. Age (estimate=0.02, P=0.0208) and concurrent diseases (estimate=0.43, P=0.0476) were factors related to standardized blood loss. BMI (estimate=0.15, P=0.0472) was the only predictor for postoperative morbidity based on logistic regression analysis.Age, BMI, interspinous distance, tumor distance from anal verge, prior abdominal surgery, preoperative chemoradiotherapy, and concurrent diseases influence the difficulty of performing laparoscopic abdominoperineal resection for ultra-low rectal cancer. Standardized operative time allows researchers to amass samples by pooling data from all published studies, thus building reliable models to predict operative difficulty for clinical use.
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