Abstract
Effective haemodynamics require sufficient myocardial contractility, and adequate filling and tonus of the circulatory system. Perioperatively, in the operating theatre, at the postoperative anaesthesia care unit and at the intensive care unit (ICU), patients often need support of their circulatory system. Maintaining stable haemodynamics is challenging and at present our treatments are mostly reactive. Furthermore, selecting the correct treatment modality can be difficult as a proper pathophysiological diagnosis of the cause of the haemodynamic instability might be inapparent.
In Part I of this thesis we primarily focus on patients admitted to the ICU and aim to implement a physiological theory on venous return in clinical practice. In Part II we focus on prediction and prevention of haemodynamic instability (defined as intraoperative hypotension) with the use of machine learning.
In Part I of this thesis we primarily focus on patients admitted to the ICU and aim to implement a physiological theory on venous return in clinical practice. In Part II we focus on prediction and prevention of haemodynamic instability (defined as intraoperative hypotension) with the use of machine learning.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 18 Jan 2023 |
Print ISBNs | 9789463617796 |
Publication status | Published - 2023 |