Abstract
After major surgery many patients develop signs and symptoms of generalised inflammation, which is defined as Systemic Inflammatory Response Syndrome (SIRS). To examine the systemic inflammatory response syndrome in the intensive care unit (ICU) after cardiac and thoracic surgery a retrospective study was performed on 1674 selected patients admitted in the Cardiothoracic ICU of the University Hospital of Vienna. SIRS was defined according to the American College of Chest Physicians / Society of Critical Care Medicine (ACCP / SCCM) Consensus Conference. In the first phase the moment of the first occurrence of SIRS, and of severe SIRS was determined. An SIRS episode was defined as a time interval from the beginning of SIRS until the receding of the symptoms for more than 24 hours. Based on this information, an artificial neural network (ANN) was constructed to predict severe SIRS. SIRS was present in 1544 patients (92.2%), SIRS with additional signs of organ dysfunction evolving in 76.1% of the cases; the progression took less than 24 hours in 87.9% of the cases. The presence of signs of the SIRS on the first operative day is hardly suitable for the risk prediction. A significant correlation between the number of SIRS episodes and the outcome for each individual patient was found. The number of SIRS episodes could be an accurate parameter for estimating the outcome and the treatment outlay. Keywords: data mining, knowledge acquisition, neural networks, medical application.
