PR of war-related colon injuries can be performed safely in selected circumstances in the absence of concomitant organ injury. Delayed anastomosis can often be performed after damage control operations once the patient stabilizes. Ostomy closure complications are more likely after anastomotic failure.

Parastomal hernia is a prevalent problem and treatment can pose difficulties due to significant rates of recurrence and morbidities of the repair. The current standard of care is to perform parastomal hernia repair with mesh whenever possible. There exist multiple options for mesh reinforcement (biologic and synthetic) as well as surgical techniques, to include type of repair (keyhole and Sugarbaker) and position of mesh placement (onlay, sublay, or intraperitoneal). The sublay and intraperitoneal positions have been shown to be superior with a lower incidence of recurrence. This procedure may be performed open or laparoscopically, both having similar recurrence and morbidity results. Prophylactic mesh placement at the time of stoma formation has been shown to significantly decrease the rates of parastomal hernia formation.

Introduction: Machine learning can enable the development of predictive models that incorporate multiple variables for a systems approach to organ allocation. We explored the principle of Bayesian Belief Network (BBN) to determine whether a predictive model of graft survival can be derived using pretransplant variables. Our hypothesis was that pretransplant donor and recipient variables, when considered together as a network, add incremental value to the classification of graft survival. Methods: We performed a retrospective analysis of 5,144 randomly selected patients (age ≥18, deceased donor kidney only, first-time recipients) from the United States Renal Data System database between 2000 and 2001. Using this dataset, we developed a machine-learned BBN that functions as a pretransplant organ-matching tool. Results: A network of 48 clinical variables was constructed and externally validated using an additional 2,204 patients of matching demographic characteristics. This model was able to predict graft failure within the first year or within 3 years (sensitivity 40%; specificity 80%; area under the curve, AUC, 0.63). Recipient BMI, gender, race, and donor age were amongst the pretransplant variables with strongest association to outcome. A 10-fold internal cross-validation showed similar results for 1-year (sensitivity 24%; specificity 80%; AUC 0.59) and 3-year (sensitivity 31%; specificity 80%; AUC 0.60) graft failure. Conclusion: We found recipient BMI, gender, race, and donor age to be influential predictors of outcome, while wait time and human leukocyte antigen matching were much less associated with outcome. BBN enabled us to examine variables from a large database to develop a robust predictive model.

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The cumulative incidence of PE was 5.7%. The incidence of PE is significantly higher with trauma-associated amputation than with extremity long-bone fracture without amputation. Bilateral amputations, multiple long-bone fractures, and pelvic fractures are independent risk factors for the development of PE. The use of aggressive prophylaxis, deep venous thrombosis screening with ultrasound, and use of prophylactic inferior vena cava filters should be considered in this patient population.