Background: Pendelluft results from differences in time constants, airway opening pressures and heterogenous pressure distributions. It has been associated with increased lung strain, and worse clinical outcomes (4). Although scientific interest in pendelluft has grown, no consensus exists on the optimal method for its detection and quantification. This thesis aimed to compare five EIT-based pendelluft quantification methods regarding pendelluft prevalence across different datasets of mechanically ventilated ICU patients.

Methods: The five methods (5-9) were implemented as described in their original publications and applied to three patient groups: those with healthy lungs, those with hypoxemic failure, and those on non-invasive ventilation. To compare prevalence results, thresholds were derived from measurements in patients on pressure-controlled ventilation. Each method was evaluated using both its original preprocessing, and a standardized preprocessing approach to assess the impact of preprocessing choices.

Results: Pendelluft prevalence was highest among hypoxemic failure patients (up to 100%), and lowest in patients with healthy lungs (as low as 20%), with agreement across methods ranging from 62-83%. Several methods showed significant positive correlations. Pendelluft intensity was significantly higher during pressure-support ventilation compared to pressure-controlled ventilation (n=15, p = 0.03).

Discussion: While agreement on pendelluft prevalence was relatively high, differences in intensity reflected underlying methodological variations in signal interpretation. Standardizing preprocessing improved comparability and reduced variability. A significant increase in pendelluft during pressure-support ventilation supports its link to patient effort. These findings highlight the need for standardized quantification protocols and emphasize the impact of preprocessing choices in advancing pendelluft research and clinical applications.

Introduction
Hemodynamic instability in patients is caused by changes in preload, afterload, or contractility of the heart. The gold standard measurement for assessing these cardiac parameters is through pressure-volume loop (PV-loop) measurements. However, due to their invasive nature, PV-loop measurements are not performed at the patient's bedside. Instead, clinicians often rely on continuous monitoring of the arterial blood pressure curve to detect changes in cardiac loading conditions and contractility. A recent simulation study suggested that specific, hidden features within the peripheral arterial blood pressure waveform could predict changes in cardiac preload, afterload, and contractility. The aim of this study was to validate the predictive value of these features using clinical patient data. We hypothesized the following:
1. PV-loops measured before and after Transcatheter Aortic Valve Implantation (TAVI) procedure could serve as a model for alterations in left ventricular afterload.
2. PV-loops measured before and after Transcatheter Edge-to-Edge Repair (TEER) procedure of the tricuspid valve can function as a model for changes in left ventricular preload.
3. The arterial blood pressure waveform features found in the simulation study are associated with changes in preload, afterload, and contractility measured with PV-loops before and after TAVI and TEER procedures.

Methods
This study utilized left ventricular PV loops measured during the TAVI and TEER procedures from the PLUTO-II study, along with simultaneously recorded arterial blood pressure curves. Left ventricular preload, afterload, and contractility were extracted from the PV-loops. The dicrotic notch, diastolic peak pressure, systolic downstroke slope, and anacrotic notch type, which were the features predictive for changes in preload, afterload, and contractility, were extracted from the arterial blood pressure curves. The change in the cardiac parameters and arterial blood pressure waveform features was assessed and the correlation between the cardiac parameter and corresponding arterial blood pressure curve feature were determined. A p-value of ≤0.05 was considered statistically significant.

Results
The data of 35 TAVI patients could be included in the analyses. Both afterload and preload decreased statistically significantly following the TAVI procedure (mean difference = -1.14, p = <0.001; mean difference = -8.94, p = 0.01, respectively). The correlation value for the comparisons between the differences in afterload and systolic downstroke slope was -0.07 (p = 0.69). The correlation values for the comparison between the differences in preload and dicrotic notch pressure, and preload and diastolic peak pressure were 0.21 (p = 0.22) and 0.15 (p = 0.38), respectively. The correlation between the difference in contractility and the anacrotic notch type was -0.16 (p = 0.4).
PV-loops of the left ventricle were measured in 16 tricuspid valve TEER patients. The preload increased statistically significantly after the TEER procedure (mean di\erence = 5 mL, p = 0.03). However, the arterial blood pressure curves in these patients were not suitable for analysis, preventing further investigation into the relationship between cardiac parameters and blood pressure curve features.

Conclusion
The PV-loop measurements measured before and after TAVI and tricuspid valve TEER provide suitable models for studying the effect of changes in afterload and preload, respectively, on the arterial blood pressure waveform. However, this study did not find strong correlations between the cardiac parameters and the arterial blood pressure waveform features identified in the simulation study.