Factors Predicting Difficulty of Laparoscopic Low Anterior Resection for Rectal Cancer with Total Mesorectal Excision and Double Stapling Technique |
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Authors: | Weiping Chen Qiken Li Yongtian Fan Dechuan Li Lai Jiang Pengnian Qiu Lilong Tang |
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Institution: | 1. Department of Colorectal Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China;2. Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China;The Chinese University of Hong Kong, HONG KONG |
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Abstract: | BackgroundLaparoscopic sphincter-preserving low anterior resection for rectal cancer is a surgery demanding great skill. Immense efforts have been devoted to identifying factors that can predict operative difficulty, but the results are inconsistent.ObjectiveOur study was conducted to screen patients’ factors to build models for predicting the operative difficulty using well controlled data.MethodWe retrospectively reviewed records of 199 consecutive patients who had rectal cancers 5–8 cm from the anal verge. All underwent laparoscopic sphincter-preserving low anterior resections with total mesorectal excision (TME) and double stapling technique (DST). Data of 155 patients from one surgeon were utilized to build models to predict standardized endpoints (operative time, blood loss) and postoperative morbidity. Data of 44 patients from other surgeons were used to test the predictability of the built models.ResultsOur results showed prior abdominal surgery, preoperative chemoradiotherapy, tumor distance to anal verge, interspinous distance, and BMI were predictors for the standardized operative times. Gender and tumor maximum diameter were related to the standardized blood loss. Temporary diversion and tumor diameter were predictors for postoperative morbidity. The model constructed for the operative time demonstrated excellent predictability for patients from different surgeons.ConclusionsWith a well-controlled patient population, we have built a predictable model to estimate operative difficulty. The standardized operative time will make it possible to significantly increase sample size and build more reliable models to predict operative difficulty for clinical use. |
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