Prof. Dr.-Ing. Bert Arnrich


Clinical ASSist AND aleRt Algorithms in visceral surgery

Funded by the Innovationsfond des Gemeinsamen Bundesausschusses.

Robin van de Water, Bjarne Pfitzner

Among the 7.1 million surgical procedures performed annually in Germany, approximately 110,000 operations are related to the four major abdominal organ systems: liver, pancreas, upper gastrointestinal tract (esophagus and stomach) and intestine (small bowl and colon). These rather complex operations are associated with high rates of postoperative complications such as serious bleeding, acute kidney failure, intraabdominal infections and sepsis. In fact, at least one out of four surgical patients experiences at least one severe complication after major abdominal surgery with mortality rates being subsequently as high as 12% according to recent studies. Of particular note, these complications often occur multiple days after surgery - hence, when the patient has already been transferred from an intensive care unit (ICU) to a regular ward with reduced monitoring capacities only. However, early detection of patient deterioration is of key importance to prevent beginning complications from aggravating and finally becoming life-threatening. In septic patients, for instance, every hour of delayed antibiotic therapy induction increases patient mortality by 2%. 

Given the crucial need to improve patient monitoring after surgery, the Clinical Assist AND Alert Algorithm (CASSANDRA)-Project aims at developing and evaluating the use of machine learning (ML) algorithms in detecting and predicting postoperative intra-abdominal infections and other complication entities.  The computational challenge in this field emerges from combining static preoperative risk parameters (eg. patient age, medical history, etc.) and static intraoperative data (e.g. duration, blood loss, etc.) with dynamic real time parameters on the ICU as well as on regular wards – the latter using a telemetric continuous vital parameter monitoring device. Taken together, the results may pave the way for an autonomous real-time monitoring system on surgical wards in the long term. The CASSANDRA-project will be conducted in close collaboration with the Surgical Department of the Charité - Universitätsmedizin Berlin and is funded by the Innovation Fonds of the Federal Joint Committee (Gemeinsamer Bundesausschuss G-BA) from May 2021 until April 2024.

About the project partner Charité 

The Surgical Clinic, Campus Charité Mitte|Campus Virchow Klinikum of the Charité - Universitätsmedizin Berlin is one of the largest surgical departments in Europe with over 5000 cases annually. Conducting more than 170 pancreatic, 350 liver, 500 intestinal and 170 esophageal and gastric surgical procedures per year, it is one of only a few surgical single centres providing a number of operations high enough for the development and evaluation of various machine learning algorithms. The challenging process of systematic data collection and digital storage throughout inpatient stays has already been introduced and continuously been optimized at the Campus Virchow Klinikum as part of the Enhanced Recovery After Surgery (ERAS ©)-program that works an holistic perioperative documentation concept. Our collaboratiion partners are Axel Winter, Dr. Max Maurer and Prof. Dr. Igor M. Sauer.


  • Perioperative Risk Assessment in Pancreatic Surgery Using Machine Learning. Pfitzner, Bjarne; Chromik, Jonas; Brabender, Rachel; Fischer, Eric; Kromer, Alexander; Winter, Axel; Moosburner, Simon; Sauer, Igor M.; Malinka, Thomas; Pratschke, Johann; Arnrich, Bert; Maurer, Max M. (2021). 2211–2214.