With the ever-increasing popularity of robotic-assisted laparoscopic surgery over the past decades, the literature reporting complications distant from the surgical site involving the use of this technology has also grown. The goal of this non-systematic review is to summarise these reports with a systems-based presentation of these complications. The most commonly observed complications were related to the peripheral nervous system and the most devastating occurring in cardiac and ophthalmic systems. There were no reports of patient complications directly related to the robot itself. While several of the reported complications are not unique to robotic surgery, they are included to maintain awareness of their possibility. The limitation of surgical time, judicious fluid administration, and constant vigilance of patient positioning are all recommended as possible preventative measures.

BackgroundAdvanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics.MethodsThirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual.ResultsFactors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001).ConclusionsANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities.

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Thoracic organ transplantation constitutes a significant proportion of all transplant procedures. Thoracic solid organ transplantation continues to be a burgeoning field of research. This article presents a review of remarkable literature published in 2017 regarding perioperative issues pertinent to the thoracic transplant anesthesiologists.