Background: Targeting systemic hemodynamic parameters in critical care settings does not always improve patient outcomes. The cellular oxygen metabolism (COMET) monitor noninvasively measures mean mitochondrial oxygen tension (mitoPO₂) and its variance via delayed fluorescence of protoporphyrin IX. However, microvascular oxygenation is often heterogeneous, allowing hypoxic and normoxic tissue to coexist, potentially leading to organ dysfunction. 

Methods: A new algorithm was developed to study underlying mitoPO₂ distributions. To evaluate performance, the algorithm was first tested on simulated unimodal and bimodal mitoPO₂ distributions across varying signal-to-noise ratios (SNRs). It was then applied to clinical data from cardiac surgery patients.

Simulation results: The algorithm accurately recovered the mean mitoPO₂  and underlying distributions across varying SNRs.

Clinical results: A total of 47 patients were included in the analysis, with 28 developing cardiac surgery-associated acute kidney injury (CSA-AKI) and 19 not. The mitoPO₂ calculated by the developed algorithm generally followed the results from the COMET closely but showed overestimation in the lower oxygen range. The CSA-AKI group spent significantly more intraoperative time with mitoPO₂ <25mmHg consistent with previous research.

Conclusion: The developed algorithm proved to be reliable in estimating the mean mitoPO₂. Larger patient cohorts are needed to validate the role of the algorithm in CSA-AKI risk assessment.

Note: title and abstract differ from original due to confidentiality.
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Cardiothoracic surgery is a common treatment for cardiovascular diseases. Patients are admitted to the ICU after cardiothoracic surgery for continuous monitoring to prevent or treat postoperative complications as much and as soon as possible. One of these complications is the development of circulatory shock. It is likely caused by one or a combination of several factors, leading to increased morbidity and mortality in the ICU. The main purpose of the circulation is to transport O2 and nutrients to the tissues and remove waste products of the tissues via the tissue’s microcirculation. Under normal conditions, O2 supply exceeds O2 demand. However, during circulatory shock, the circulation cannot meet the perfusion demands of the organs, leading to organ dysfunction and organ failure. Resuscitation procedures for patients with circulatory shock focus on normalizing macrocirculatory parameters, such as CO and SvO2, by administering fluids and vasopressors to support tissue perfusion. Improvement in macrocirculatory parameters is expected to be paralleled by improvement in microcirculatory perfusion and tissue oxygenation (i.e., hemodynamic coherence), but it appears that these do not always improve simultaneously. Loss of this coherence has been associated with adverse outcomes. 

The microcirculation can be imaged sublingually with an HVM. Studies in patients with septic shock have shown that hemodynamic coherence is often lacking. Therefore, it could be valuable to monitor the microcirculation of cardiothoracic surgery patients. 

This thesis aimed to investigate the postoperative time course of microcirculatory parameters in patients admitted to the ICU after cardiothoracic surgery with and without circulatory shock,  the relationship between macro- and microcirculation, and the usage of leukocyte detection in understanding patient’s systemic inflammation.  

Chapter 2 provides general background information on cardiothoracic surgery, CPB, the physiology of the microcirculation, the latest generation of HVM, pathophysiological changes in the microcirculation after cardiothoracic surgery, leukocyte-endothelium interactions, the macrocirculation, and hemodynamic coherence. A retrospective study of cardiothoracic surgical patients with shock is described in Chapter 3 of this thesis. The results showed that the microcirculation might adapt to compensate for the circulatory shock state by decreasing RBCv and increasing FCD, TVD, and cHct compared with normal values of healthy volunteers while maintaining tRBCp. Chapter 4 describes a prospective study comparing cardiothoracic surgical patients with shock from Chapter 3 with cardiothoracic surgical patients without shock. The comparison between these two groups showed that both groups exhibited different behavior of the microcirculation. However, the underlying mechanism is not understood and requires further research. Chapter 5 contains an explanatory review of the use of STDs for leukocyte detection. Chapter 6 provides a general discussion and reviews the future prospects of microcirculation measurements as a tool in the management of critically ill patients. Our findings should be examined in more extensive clinical trials to determine whether microcirculatory changes contribute to the development of shock.

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Intensive Care Unit (ICU) readmission is a serious adverse event associated with high mortality rates and costs. Prediction of ICU readmission could support physicians in their decision to discharge patients from the ICU to lower care wards. Due to increasing ICU data availability, Artificial Intelligence (AI) models in the form of machine learning (ML) algorithms can be used to build high-performing decision support tools. To have impact on patient outcomes, these decision support tools should have high discriminative performance and should be explainable to the ICU physician. The goal of this thesis was to compare several types of ML models on predictive performance and explainability for the prediction of ICU readmission for discharge decision support. The scientific paper that aims to answer this question can be found in Part III of this thesis. In a broader perspective, we proposed a framework for the development and implementation of clinically valuable AI-based decision support.
First, a systematic review was conducted to examine current literature on ML prediction models for ICU readmission (Part I). We concluded that previously developed models reported inappropriate performance metrics and were not implemented in clinical practice. Furthermore, previous work did not compare explainable outcomes in terms of patient factors contributing to the risk of readmission between models. Secondly, we conducted a questionnaire among ICU physicians to investigate current discharge practices and their attitude towards the use of AI tools in their work processes (Part II). Although not all physicians agreed that the decision to discharge ICU patients is complex, most of them do believe in the clinical value of an AI-based discharge decision support tool. Thirdly, we developed several prediction models for ICU readmission and compared them on discriminative performance, calibration properties, and explainability (Part III). We concluded that advanced ML models did not outperform logistic regression in terms of discriminative performance and calibration properties. However, the explanations of XGBoost, a state-of-the-art ML algorithm, were more in line with the ICU physician’s clinical reasoning compared to logistic regression and neural networks. Lastly, we designed a study protocol to prospectively evaluate the predictive performance of Pacmed Critical, a CE-certified AI-based discharge decision support tool, and that of the ICU physician (Part IV).
This thesis contributed to making the step from developing high-performing prediction models to clinical adoption of an ICU discharge decision support system. Due to small differences in discriminative power and calibration properties between models, the model best explainable to the physician and most in line with clinical reasoning should be chosen for decision support. Before final implementation, impact on patient outcomes and costs will need to be studied in prospective trials.
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