Multimodal monitoring guided management of blood and cerebral perfusion pressure in aneurysmal subarachnoid hemorrhage – a prospective multicenter observational cohort study.

• Applicant: Privatdozent Michael Veldeman, Ph.D.
• Subject Area: Clinical Neurology; Neurosurgery and Neuroradiology
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 546970303

This project aims to analyze a prospectively collected dataset of physiological information gathered during the intensive care treatment of patients suffering from aneurysmal subarachnoid hemorrhage (SAH). Data collection was performed at RWTH Aachen University Hospital, Universitätsspital Zürich, and Addenbrooke's Hospital in Cambridge. Based on metabolic and cerebrovascular measurements in patients' brain tissue, the goal is to better understand the pathophysiology behind delayed cerebral ischemia (DCI) following SAH. To date, physiological measurements, such as the partial pressure of brain tissue oxygen or interstitial concentrations of glucose and lactate, have been interpreted based on fixed thresholds. However, the slope of change over time in an unfavorable direction might serve as a faster and more reliable predictor of delayed cerebral ischemia. For the cohort of patients being analyzed, data is available on cerebral autoregulatory indices (PRx, CPPopt), microdialysis-measured metabolite concentrations, and brain tissue oxygen levels. The intricate interplay between autoregulatory dysfunction and metabolic derangement in relation to DCI remains poorly understood. The analysis of longitudinal or time-series data is challenging because conventional statistical regression models do not adequately account for time as a factor or changes over specific time intervals. To appropriately analyze these data, the applicant needs to acquire a new skill set in data analytical techniques. These skills primarily involve statistical programming in R, enabling the application of mixed-effects models, and programming in Python, facilitating the use of autoregressive integrated moving average (ARIMA) models. This expertise is available at the facility where the analyses are being conducted. However, to master these techniques and apply them autonomously, additional time is required.

• DFG Programme: WBP Fellowship
• International Connection: United Kingdom
• Host: Professor Dr. Peter Smielewski, Ph.D.