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PhD in Smart Neuromonitoring to Support Precision Medicine in Acute Central Nervous Injury

Katholieke Universiteit Leuven - SOPRANI network

The SOPRANI network consists of 7 European research institutions and 1 non-academic partner and has recently been granted a European MSCA Doctoral Training Network grant to train and guide 10 doctoral candidates in a 10 individual research projects that are intended to substantially advance the field of neuromonitoring in patients with acute brain injury in neuro-intensive care. Current measurable physiological signals only roughly represent ongoing pathophysiological processes. As a result, no therapeutic action has been shown to be beneficial in randomized patient trials in this patient group. The project goal is to prepare novel dynamic insult monitoring technologies and to develop improved decision support by integrating disease models and insult/treatment ontologies into smart multimodality monitor software. A parallel goal is to unite high level expertise in clinical, biomedical, statistical and engineering sciences into one network to boost the next generation of researchers to substantially advance the field of neuromonitoring. The network includes 3 relevant animal models and access to large (multi)center patient databases with injury, treatment & outcome data. Smart monitor platforms that aid precision medicine in acute central nervous system injury close to trials and future innovation leaders are expected results. The institutions involved in the network are: - KU Leuven, Belgium (Bart Depreitere, Geert Meyfroidt) - Leiden University Medical Center, the Netherlands (Ewout Steyenbergh, Wilco Peul) - Charité Berlin, Germany (Jens Dreier) - Joseph Kepler University Linz, Austria (Raimund Helbok) - University of Cambridge, UK (Peter Smielewski) - Saint-George University London, UK (Samira Saadoun, Marios Papadopoulos) - VIB (Leuven), Belgium (Alan Urban) - Moberg Analytics, USA (Dick Moberg) The network includes leading researchers and clinicians in the field of central nervous system injury, biotechnology, biostatistics and data sciences, who have decided to join forces by composing a multidisciplinary team to teach young researchers in multiple competences and apply these in their research.

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Project

The brain and spinal cord too often still act as a black box, even with current multimodality monitoring in 2023, still leaving clinicians unsure of what is really happening inside her/his sedated patient, how to make sense of the different signals displayed on the monitor and what therapeutic action to take or not to take. The underpinning of monitored signal changes by pathophysiological event knowledge  obtained in the lab and carefully compared with insights from modelling big data from patient repositories is a first and indispensable step towards next generation monitoring. The combination of such an integrative monitoring platform with smart visualization concepts – that associate events with probabilistic outcome prediction – and event/treatment ontology architectures, has a strong potential for truly beneficial decision support, and will make the black box more transparent. The main and ultimate impact is expected in improved patient outcomes following acute central nervous system injury. Based on incidences of the involved pathologies in Europe and the proportion requiring ICU care, it is roughly estimated that this matter concerns. 320,000 patients per year in Europe. Every neuron saved from ischemic or apoptotic death can make a difference for the individual patient: the difference between either or not regaining consciousness, either or not having sufficient strength and coordination to walk, either or not avoiding serious cognitive decline.

The project consists of the following PhD projects:

  • Unveiling and improving data on patients with acute CNS injury (VIB): 1.To make an inventory of existing data repositories; 2. To harmonize and standardize clinical, imaging and monitoring data originating from different devices, platforms and databases; 3. To develop and test a federated analysis network strategy that reconciles multicenter research with privacy legislation
  • Establishing insult burden curves for different types of secondary insults in acute brain injury (KU Leuven): 1. To establish insult burden curves for different types of secondary insults in acute brain injury; 2. To establish categorized insult burden curves for different types of patient and injury categories; 3 To develop smart visualization concepts of established monitoring signals and secondary insults
  • Multimodality monitoring in acute brain injury: what treatments have what effects? (JK University Linz): 1. To investigate effect of treatments on signals, insults and outcomes; 2. To capture and categorize domain knowledge on multimodality monitoring; 3. To develop a theoretical framework of current knowledge on multimodality monitoring and treatment algorithms; 4. To develop an ontology of pathophysiological events and proposed decisions
  • Associations between physiological and metabolic parameters and neurological outcome in SCI (Saint-George University London): 1. To associate multimodality monitoring signals and metabolics in SCI with short and long term outcomes; 2. To develop secondary insult burden curves in SCI
  • Multidimensional statistical disease model of traumatic brain injury(Leiden University Medical Center): 1. To build a statistical model using all available patient-, injury-, treatment- and outcome data from the different accessible datasets on traumatic brain injury and train the model to predict secondary insults and outcomes; 2. To explore and investigate hypotheses on treatment effects (in collaboration with ESR3); 3. To integrate relevant disease model concepts in a decision support platform
  • Multidimensional statistical disease model of acute stroke and subarachnoid haemorrhage (Leiden University Medical Center): 1. To build a statistical model using all available patient-, injury-, treatment- and outcome data from the different accessible datasets on stroke and subarachnoid haemorrhage and train the model to predict secondary insults and outcomes; 2. To explore and investigate hypotheses on treatment effects (in collaboration with ESR3); 3. To investigate collision opportunities for the statistical disease models on traumatic and non-traumatic acute brain injury; 4. To integrate relevant disease model concepts in a decision support platform
  • A dynamic cerebral autoregulation status monitor in a piglet cranial window model of severe TBI (KU Leuven): 1. To develop a dynamic monitor of CAin the piglet cranial window model; 2. To validate the dynamic CA monitor in a piglet model of severe TBI equipped with a cranial window; 3. To investigate (molecular) drivers of impaired CA
  • Clues for the monitoring of NV unit dysfunction in a translational setting (Charité Berlin): 1. To explicit relations between CA, hemodynamic responses to SD and blood-brain barrier function in an animal model of SD-induced spreading ischemia; 2. To explicit these relations in an animal model of primary vasospasm-induced ischemia; 3. To explicit these relations in a patient dataset of aneurysmal SAH documented with longitudinal MRI, subduralelectrocorticography and neurovascular monitoring; 4. To investigate NV unit dysfunction in conjunction with CA dysfunction in a piglet cranial window model of severe TBI
  • Neuromonitoring derived metrics for the assessment of pathophysiological hemodynamics (University of Cambridge): 1. To integrate experimental animal data on cerebral hemodynamics into the ODE cerebral circulation model; 2. To use advanced signal processing methods to extract relevant hemodynamic parameters of the circulation model from high resolution monitoring signals obtained in the animal models; 3. To extract similar hemodynamic parameters from high resolution patient data from the accessible clinical data repositories; 4. To investigate relations between the statistical disease model (IRP 5 & 6) and the ODE cerebral circulation model
  • Effectiveness, cost-effectiveness and shortcomings of current neuromonitor technology and barriers to new technology (Leiden University Medical Center): 1. To investigate effectiveness of current neuromonitor technology in the management of acute brain injury by a systematic review; 2.To investigate cost-effectiveness of current neuromonitor technology based on 2tertiary hospitals’ databases; 3. To adopt qualitative research methods to document shortcomings of current neuromonitor technology as well as practice variations and expert wishes; 4. To develop strategies for the testing of new neuromonitor concepts in prospective patient trials.

Profile

The candidates should have a strong academic record and a Masters diploma in the fields of Bio-informatics, Computer Science, Mathematics, Engineering, Medicine or Biomedical Sciences. Previous research experience is a plus. The candidates should not have a PhD. Depending on the project type, knowledge of data analysis, (bio)statistics, machine learning and associated programming skills or physiology, biology, medicine, and lab sciences are essential.

The candidates should be able to work independently, take initiative, adopt critical judgment and demonstrate ability to work in team. The candidates should be motivated to work with and listen to experts with a clinical background, with a biomedical sciences background and with biostatistical and engineering backgrounds. The project will include several network wide educational events and a secondment, for which travel, communication and social skills are required.

Proficiency in written and spoken English is crucial.

The candidates can be of any nationality, but must not have resided or carried out his/her main activity in the country of the recruiting beneficiary for more than 12 months in the 3 years immediately prior to his/her recruitment.

The selected candidates are expected to write a doctoral thesis on their research after 4 years (when relevant, a 4th year of research will be funded by the host institution).

Offer

The selected candidates are offered:

  • A full time PhD position in one of the 7 world-renowned research institutions and in the dynamic, multidisciplinary and intellectually challenging environment of the SOPRANI network, under supervision of and in close collaboration with experts from a wide variety of domains.
  • A thorough scientific education and training in all relevant competences to advance the field of neuromonitoring, enabling the possibility to become a world-class researcher in this field.
  • The possibility to actively participate in the network’s organizational structure and in international conferences and collaborations.
  • A predetermined living allowance, and when appropriate a mobility allowance, family allowance, long-term leave allowance or special needs allowance according to EU standards corrected for the country of employment and integrally transferred to the researchers.

Interested?

You can apply through the webportal. Application deadline is November 30 2023.

The actual start of the research will be situated around Q2 2024.

For more information please contact: Prof. Dr. Bart Depreitere, Project Coordinator

Neurosurgery University Hospitals Leuven

bart.depreitere@uzleuven.be

You can apply for this job no later than December 31, 2023

KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be

Qualification Type: PhD
Location: Leuven - Belgium
Funding for: UK Students, EU Students, International Students
Funding amount: Not Specified
Hours: Full Time
Placed On: 27th October 2023
Closes: 31st December 2023
Reference: BAP-2023-437
   
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