Background: Post-COVID (PC) is characterized by prolonged symptoms following a COVID-19 infection, with Post-Exertional Malaise (PEM) being a feature with a large negative impact on quality of life. Mitochondrial dysfunction has previously been related to PEM-related symptoms. This study non-invasively investigates the in-vivo mitochondrial oxygenation (mitoPO2) and mitochondrial oxygen consumption (mitoVO2) in PC patients experiencing PEM compared to convalescent controls using the COMET device.

Methods: We conducted a comparative, non-randomized, observational study with 24 PC patients and 20 convalescent controls. MitoPO2 and mitoVO2 were measured at baseline, after short exercise, and after 10 minutes of recovery. Linear mixed models assessed the effects of exercise and recovery on mitoPO2 and mitoVO2. PROMs related to tiredness, PEM, and function were evaluated for explainability of mitoPO2 heterogeneity in PC participants using Spearman correlations.

Results: No significant differences were found between the PC and control groups in mitoPO2 and mitoVO2 (p > 0.05). Exercise led to a significant decrease in mitoPO2 (p < 0.001), which recovered after 10 minutes. No significant differences were observed between groups regarding the effects of exercise and recovery (p > 0.05). A large variance in mitoPO2 was noted in the PC group, suggesting heterogeneity within this population. Additionally, mitoVO2 increased significantly after recovery in both groups, with no significant group differences.

Conclusion: The study implies that while mitoPO2 decreases significantly after exercise and mitoVO2 increases significantly after recovery, both do not differ significantly between PC patients and controls. However, the observed variance in mitoPO2 after exercise and recovery among PC patients suggests varying disease mechanisms related to oxygen supply. Future research should focus on longitudinal assessments, home-based measurements, and exploring alternative methods to assess mitochondrial and microcircular function to better understand these findings and address the limitations of the current study.

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.

Background and objectives: Mitochondria require oxygen to generate energy for essential cellular processes. An in-vivo non-invasive technique to monitor mitochondrial oxygen tension (mitoPO2) is the protoporphyrin IX-triplet state lifetime technique. Protoporphyrin IX (PpIX), one of the key components of heme, is naturally present in the mitochondria. Topical administration of 5-aminolevulinic acid hydrochloride (ALA) increases PpIX concentration in the skin. After excitation with a laser pulse, PpIX emits red light with an oxygen-dependent delayed fluorescence lifetime. This signal is detected by a photomultiplier and the mitoPO2 can be derived from this. A clinical device has been developed using this technique. Although this device has been proven feasible, improvements in reliability and practicality are needed. This work aims to investigate possible improvements to the components of this device for better application in a clinical setting. The main areas of improvement are the excitation wavelength, the detection of the signal, and the light source.
Excitation wavelength: First, wavelengths from 415 nm to 695 nm are explored as possible excitation wavelengths. Second, four excitation wavelengths that exhibit low noise are investigated in more detail. For a better comparison, the pulse energy per wavelength is equated. The excitation wavelengths 415 nm and 515 nm showed low noise, no oversaturation, and accurate fits. These two excitation wavelengths are then validated on three test subjects with skin types II, IV, and V on the Fitzpatrick scale. Oxygen-dependent lifetimes are obtained in all test subjects with an excitation wavelength of 415 nm.
Detection: A new detector is explored to replace the large and expensive photomultiplier. The detector is tested in a fluorescent solution and on human skin. The first experiments resulted in oversaturation of the detector. To prevent this, several combinations of band-pass, long-pass, and absorption filters are tested. Even with these filters, the detector was oversaturated by the light pulse from the laser.
Light source: Two light sources are investigated as possible replacements for the currently used laser. These were first tested in a 10 μM PpIX solution. No delayed fluorescence was observed because the light sources and detector could not be placed close enough to each other. Therefore, for the measurements in human skin, the light source was coupled into a fiber. Both light sources showed oxygen-dependent lifetimes, suggesting that they are suitable for this application. As a final step, related parameters were optimized to reduce oversaturation and noise. These are described in the Confidential Appendix.
Conclusion: The potential of 415 nm as an excitation wavelength is demonstrated, but these results should be validated in more test subjects. The light sources showed the potential to replace the current laser, which could reduce maintenance and increase reliability. The next step should be to combine the 415 nm excitation wavelength, light source, and detector into a prototype to explore their combined potential.

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.