Continuous and non-invasive assessment of respiratory drive and effort in mechanically ventilated patients
Rob Warnaar is a PhD student in the Department of Cardiovascular and Respiratory Physiology. (Co)Promotors are prof.dr. D.W. Donker and dr. E. Mos-Oppersma from the Faculty of Science & Technology.
Mechanical ventilation provides lifesaving support for critically ill patients with respiratory failure, but may contribute to lung and diaphragm injury when poorly tailored. A critical challenge is achieving lung‑ and diaphragm‑protective ventilation by matching ventilatory support to the patient’s needs. This requires insight into respiratory drive and effort, which are difficult to measure continuously at the bedside. This PhD thesis addresses this challenge by advancing respiratory surface electromyography (sEMG), a non‑invasive technique for continuous monitoring of respiratory muscle activity. In addition, computational physiological models (CPMs) are studied as a complementary approach to provide insight into physiological processes that are not observable in routine clinical care.
Part I focuses on the methodological foundations of respiratory sEMG. Substantial variability in acquisition strategies and reporting quality currently limit comparability across studies. Signal processing strongly influences sEMG‑derived outcomes. To promote transparency and reproducibility, this work introduces ReSurfEMG, an open‑source Python package providing standardised workflows for sEMG processing and quality assessment. Part II investigates the physiological interpretation of sEMG‑derived metrics in intensive care patients. Novel indices are presented to quantify respiratory effort and the patient’s contribution to tidal breathing. These measures respond to changes in ventilator support and during weaning, highlighting their potential to support individualised patient management. Part III evaluates the clinical readiness of CPMs. A systematic review and simulation study identify design principles and limitations defining when such models can be reliably applied at the bedside. Together, this thesis advances non‑invasive respiratory monitoring, laying the groundwork for safer, more individualised mechanical ventilation in critically ill patients.
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