Mortality remains high and is associated with sepsis. Fistulas proximal to the duodeno-jejunal flexure are more likely to close spontaneously. If the fistula fails to close spontaneously care is often prolonged and complex, requiring a dedicated nutrition team. In this series, spontaneous closure was more common in upper GI fistulas. Patients who are not able to be discharged in the interval between fistula formation and definitive surgery have a higher mortality risk.

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The use of routine blood group and save is not justified. A targeted approach will reduce the demand on blood transfusion service without detriment to those undergoing cholecystectomy. There is no substitute for adequate vigilance for bleeding as a complication with any surgical procedure.

Aim There is current debate about the optimal management of lateral pelvic lymph nodes (LPLNs) in rectal cancer between Western and Eastern centres. This paper aims to report the rate of histologically proven positive LPLNs in a group of patients undergoing the conventional Western approach to primary and recurrent rectal cancer. Method A retrospective cohort review of all patients who underwent LPLN dissection at Royal Prince Alfred Hospital in Sydney, Australia. This included patients who underwent pelvic exenteration who had LPLNs excised either en bloc for laterally invasive or recurrent tumours or as part of selective node dissection for suspicious lymph nodes on preoperative imaging. Histopathological results for these patients were compared with node status at preoperative imaging. Results Seventy‐one patients satisfied the inclusion criteria. Of those patients with positive nodes on histology, 27% (9/33) with radiologically positive LPLNs were treated with preoperative radiotherapy and 75% (9/12) with radiologically positive LPLNs were not treated with preoperative radiotherapy (P = 0.004). None of the 12 patients with radiologically negative nodes treated with radiotherapy had positive nodes; 25% (3/12) of the patients with radiologically negative nodes who were not treated with radiotherapy had positive nodes. Fifty‐three per cent of patients developed postoperative complications. Conclusion Our study suggests that in patients with radiologically positive LPLNs chemoradiotherapy may not be enough to sterilize these extra‐mesorectal lymph nodes as a large proportion (27%) will have residual viable adenocarcinoma cells. In patients with radiologically negative LPLNs, however, the addition of chemoradiotherapy may serve to adequately sterilize these lymph nodes without the need for prophylactic LPLN dissection.

Aim We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods.