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Aim: Predicting intra-abdominal infections (IAI) after colorectal surgery by means of clinical signs is challenging. A naïve logistic regression modeling approach has some limitations, for which reason we study two potential alternatives: the use of Bayesian networks, and that of logistic regression model., Methods: Data from patients that had undergone colorectal procedures between 2010 and 2017 were used. The dataset was split into two subsets: (i) that for training the models and (ii) that for testing them. The predictive ability of the models proposed was tested (i) by comparing the ROC curves from days 1 and 3 with all the subjects in the test set and (ii) by studying the evolution of the abovementioned predictive ability from day 1 to day 5., Results: In day 3, the predictive ability of the logistic regression model achieved an AUC of 0.812, 95% CI = (0.746, 0.877), whereas that of the Bayesian network was 0.768, 95% CI = (0.695, 0.840), with a p-value for their comparison of 0.097. The ability of the Bayesian network model to predict IAI does present significant difference in predictive ability from days 3 to 5: AUC(Day 3) = 0.761, 95% CI = (0.680, 0.841) and AUC(Day 5) = 0.837, 95% CI = (0.769, 0.904), with a p-value for their comparison of 0.006., Conclusions: Whereas at postoperative day 3, a logistic regression model with imputed data should be used to predict IAI; at day 5, when the predictive ability is almost identical, the Bayesian network model should be used., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Minimally invasive surgery has demonstrated many benefits in general surgery, particularly in colon and rectal procedures. On the other hand, it has some limitations that must be taken into account, especially technical drawback. Robotic surgery has incorporated many improvements to overcome this disadvantage, such as 3D visualization, articulating instruments assisting complex and precise movements. As a result, robotic colorectal surgery shows less intraoperative blood loss, shorter time to oral tolerance and initial flatus (particularly associated with "Enhanced Recovery After Surgery" protocol), less conversion rate to open surgery, shortened hospital stay, and longer distal margins compared to laparoscopic and open surgery. This approach also shows a shorter learning curve. Some studies suggest that it could decrease perioperatively or 30 days after the intervention's mortality, raise overall survival, reduce wound infection, and improve functional results, while others show no significant difference. However, it lengthens surgical time. Otherwise, the studies included do not show statistically significant changes in the number of resected lymph nodes and anastomotic leaks. Economic costs remain one of the major concerns, although to date there are no large‐scale studies that have evaluated this aspect from a global point of view. Robotic surgery represents a qualitative leap in surgical instruments and, although there is no strong evidence in favor of the use of robotic surgery over laparoscopic or open surgery, there is enough evidence to support its use in colorectal surgery, with potential advantages for patients. [ABSTRACT FROM AUTHOR]