The first international Delphi consensus statement on Laparoscopic Gastrointestinal surgery.

BACKGROUND: Laparoscopic surgery has almost replaced open surgery in many areas of Gastro-Intestinal (GI) surgery. There is currently no published expert consensus statement on the principles of laparoscopic GI surgery. This may have affected the training of new surgeons. This exercise aimed to achieve an expert consensus on important principles of laparoscopic GI surgery. METHODS: A committee of 38 international experts in laparoscopic GI surgery proposed and voted on 149 statements in two rounds following a strict modified Delphi protocol. RESULTS: A consensus was achieved on 133 statements after two rounds of voting. All experts agreed on tailoring the first port site to the patient, whereas 84.2% advised avoiding the umbilical area for pneumoperitoneum in patients who had a prior midline laparotomy. Moreover, 86.8% agreed on closing all 15 mm ports irrespective of the patient's body mass index. There was a 100% consensus on using cartridges of appropriate height for stapling, checking the doughnuts after using circular staplers, and keeping the vibrating blade of the ultrasonic energy device in view and away from vascular structures. An 84.2% advised avoiding drain insertion through a ≥10 mm port site as it increases the risk of port-site hernia. There was 94.7% consensus on adding laparoscopic retrieval bags to the operating count and ensuring any surgical specimen left inside for later removal is added to the operating count. CONCLUSION: Thirty-eight experts achieved a consensus on 133 statements concerning various aspects of laparoscopic GI Surgery. Increased awareness of these could facilitate training and improve patient outcomes.

Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction.

Importance: Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes. Objective: To apply image-based deep learning to predict complexity, defined as need for component separation, and pulmonary and wound complications after abdominal wall reconstruction (AWR). Design, Setting, and Participants: This quality improvement study was performed at an 874-bed hospital and tertiary hernia referral center from September 2019 to January 2020. A prospective database was queried for patients with ventral hernias who underwent open AWR by experienced surgeons and had preoperative computed tomography images containing the entire hernia defect. An 8-layer convolutional neural network was generated to analyze image characteristics. Images were batched into training (approximately 80%) or test sets (approximately 20%) to analyze model output. Test sets were blinded from the convolutional neural network until training was completed. For the surgical complexity model, a separate validation set of computed tomography images was evaluated by a blinded panel of 6 expert AWR surgeons and the surgical complexity DLM. Analysis started February 2020. Exposures: Image-based DLM. Main Outcomes and Measures: The primary outcome was model performance as measured by area under the curve in the receiver operating curve (ROC) calculated for each model; accuracy with accompanying sensitivity and specificity were also calculated. Measures were DLM prediction of surgical complexity using need for component separation techniques as a surrogate and prediction of postoperative surgical site infection and pulmonary failure. The DLM for predicting surgical complexity was compared against the prediction of 6 expert AWR surgeons. Results: A total of 369 patients and 9303 computed tomography images were used. The mean (SD) age of patients was 57.9 (12.6) years, 232 (62.9%) were female, and 323 (87.5%) were White. The surgical complexity DLM performed well (ROC = 0.744; P < .001) and, when compared with surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared with 65.0% (P < .001). Surgical site infection was predicted successfully with an ROC of 0.898 (P < .001). However, the DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (P = .03). Conclusions and Relevance: Image-based DLM using routine, preoperative computed tomography images was successful in predicting surgical complexity and more accurate than expert surgeon judgment. An additional DLM accurately predicted the development of surgical site infection.

No evidence of Permacol rejection presented by Wotton and Akoh.

Wotton and Akoh in their previously reported case in this journal postulate that Permacol rejected. This letter provides a detailed critique of that claim and provides an alternative explanation for the histological data provided by the authors. It is also argued that Wotton and Akoh have misrepresented one of the papers in the discussion in their article and a clarification of that referenced paper is given.