PhD defense by Isabel Martinez Tejada

The PhD investigated long-term intracranial pressure following craniectomy and subsequent cranioplasty.

The research was conducted under the supervision of

• Professor Marianne Juhler
• Professor Jens E Wilhjelm
• Morten Andresen, MD, PhD

Opponents on the thesis were

• Associate Professor Sadasivan Puthusserypady,
  Technical University of Denmark
• Professor Anders Eklund,
  Umeå University, Sweden
• Associate Professor Anders Rosendal Korshøj,
  Aarhus University, Denmark

Thesis summary

With the development of telemetric monitoring devices in the last decade, ICP monitoring has become feasible in a broader clinical setting, with patients undergoing ICP monitoring with mobile equipment either in-hospital or in the home setting, where a larger variety of ICP waveforms exist. Currently, the identification of these waveforms, the so-called macro-patterns lasting seconds to minutes, is primarily based on visual inspection. The need for objective and more automated identification of these variations emerges as a potential tool for better understanding the physiological underpinnings of the patient’s clinical state.

During my PhD thesis, we investigated a new methodology that could serve as a foundation for future objective and reproducible macro-pattern identification in the ICP signal with the hope to better understand the morphological characteristics and distribution of these macro-patterns in the ICP signal and their clin ical significance.

First, we showed that the current terminology and descriptions of B-waves no longer adequately address the ICP waveforms found in the clinical practice today. Their origin is also unclear. Our results found that ICP B-waves, also observed in healthy subjects during sleep, are associated both with respiratory disturbances and vascular contribution of flow velocity in a limited frequency range.

Second, a new data quality pipeline was designed to integrate all data validation checks to ensure high data quality. The method was applied to ICP signals before macro-pattern identification, to mitigate the possibility of artefacts masking the true signal.

Finally, a method based on k-Shape clustering was developed to identify the most encountered macro-patterns in ICP signals. We found a total of seven macro-patterns—with varying occurrence and distribution—that described our ICP signals.

In conclusion, we proposed a new, objective, and more automated method to identify macro-patterns in ICP signals. Thanks to this method, disease entities are likely to be identifiable based on the internal distribution and weighting of specific ICP macro-patterns. This information aims at optimizing both disease and treatment identification.