Critical illnesses and surgical procedures can impede the continuous blood flow to vital organs, potentially resulting in inadequate oxygen supply to meet their metabolic demands. Monitoring tools to assess organ perfusion are not readily available at the patient's bedside. Therefore, blood pressure measurements currently serve as a surrogate to estimate a patient's circulatory state. The first part of this thesis offers an overview of how critically low arterial blood pressure, also known as hypotension, is defined, monitored, and treated in intensive care units (ICUs) across various countries worldwide. Additionally, it provides insight into the effects of hypotension exposure during an ICU stay on patient outcomes. To prevent hypotension-related organ injury, blood pressure is often continuously monitored in critically ill patients, allowing for immediate alarms and prompt treatment if hypotension occurs. However, hypotension, and therefore organ injury, already occurred at that point. Moreover, selecting the appropriate treatment can be challenging as the cause of the blood pressure drop may be unclear. The final part of this thesis focuses on the prediction and prevention of hypotensive events during cardiac surgery and postoperative intensive care unit stay using continuous blood pressure monitoring and machine learning
