The aim of this project is to develop a machine-learning model for calculating the risk of postoperative complications. In addition to the data collected during the premedication, the model will include all intraoperative values recorded in the Patient Data Management System (PDMS), which include not only vital and respiratory parameters, but also medication and doses, intraoperative events and times. Postoperative complications are defined according to their severity according to the Clavien-Dindo score (Dindo, Demartines et al., 2004) and are collected from the data available in the health information system (HIS).

The machine-learning model is created using an extreme-gradient boosting algorithm which has been updated with new data from the year 2021 to ensure accuracy of the model.

Inclusion Criteria:

• all patients who underwent surgery with anesthesia

Exclusion Criteria:

• none

Patients who underwent surgical interventions with general or regional anesthesia at Klinikum rechts der Isar, Munich after the implementation of an electronic patient data management system in May 2014