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The overall goal of this research project is to elucidate underlying pathophysiological mechanisms of postoperative delirium (POD) and to specifically validate perioperative predictive factors that will help in indentifying patients at higher risk of developing POD.
POD is defined as a "fluctuating disturbance in attention that represents an acute change from baseline, accompanied by disturbed cognition or perception, and not due to a pre-existing neurocognitive disorder or occurring in the context of a severely reduced arousal level". Depending on the type of surgery and the studied population, it can occur in 20 to 45% of the older patients. POD is a burden to the health care providers. Indeed, it is strongly associated with increased morbidity and mortality. The pathophysiology of POD is multifactorial and not yet completely elucidated. The aging brain is more vulnerable to the development of POD. However, more than the chronological age, the patient's overall vulnerability and their preexisting cognitive status are indicators of their ability to cope with these perioperative stressors. Indeed, the patient's cognitive status is a leading cause of POD and models predicting POD show poor accuracy because they do not take into account the patient's preoperative cognitive status. Preoperative neurocognitive assessment could be performed but these tests are time-consuming and subject to various influencing factors. Hence, objective tools are required to distinguish patients with preoperative cognitive impairment.
First hypothesis: The presence of a specific intraoperative EEG signal pattern may provide a tool for such identification of patients with underlying preoperative cognitive frailty. Commonly used anesthestic agents for induction and maintaining general anesthesia (e.g. sevoflurane or propofol) provide a typical electroencephalographic pattern with slow/delta and alpha oscillations, predominantly in the frontal cerebral cortex. More specifically, alpha oscillations actually originate from parieto-occipital sites in awake patients and migrate towards frontal regions after the induction of anesthesia. This phenomenon is called the "anteriorization" of the alpha frequency band. Besides, amongst all EEG frequencies, the contribution of alpha oscillations to the global tracing evolve throughout adulthood : the alpha power tends to decrease with age, and this decrease is more pronounced in the presence of underlying cognitive disorder (e.g. mild cognitive impairment, Alzheimer's disease). More importantly, it has been demonstrated that a lower frontal alpha band anteriorization during general anesthesia is associated with lower preoperative cognitive scores. Moreover, these patients might be at higher risk of intraoperative EEG suppression in case of an overdose of anesthetics or, even often, despite a lower dose of anesthetics. In this regard, the presence and the power of frontal alpha oscillations under general anesthesia may be indicators of the patient's preoperative cognitive status and may therefore predict the risk of developing POD.
Second hypothesis: Genetic studies have demonstrated a correlation between specific genotypes and the risk of cognitive decline. APOEe4 genotype is a known risk factor for Alzheimer's disease (AD), and has been shown to be also a risk factor of POD. However, APOEe4 allele is neither necessary nor exclusive to develop AD, and this may also hold true for POD, as this late hypothesis has been rejected in other previous studies. Otherwise, few studies have looked at some EEG particularities according to APOEe4 genotyping, in AD patients and control subjects. Unfortunately, their results regarding the presence of the e4 allele and associated EEG abnormalities are conflicting. To date, no study has related APOE genotyping and intraoperative EEG patterns under general anesthesia.
Third hypothesis: In addition to perioperative episodes of cerebral hypoxia and/or hypoperfusion and neuroinflammation, pathophysiological mechanisms of POD also include a potential direct insult to the brain, induced by both anesthesia and surgery. Yet, the ideal biomarker, highly sensitive for brain injury, as well as highly specific for neuronal tissue remains to be identified. Indeed, the release of such proteins after a neuronal injury can ensue from many levels and some of them have extracranial sources. These sources may therefore influence the observed results. As a conseuquence, in clinical practice, none of the currently evaluated neurobiomarkers (e.g. interleukins, Neuron Specific Enolase, S100 calcium-binding protein B) has emerged as a reliable diagnostic and/or prognostic tool for assessing postoperative neurological complications. Recently, much focus has been given to neurofilaments, as this group of proteins is part of the scaffolding of axons and is exclusively expressed in neuronal tissue. As a consequence, abnormally high levels of neurofilaments in extracellular fluids, such as cerebrospinal fluids (CSF) or serum, correspond specifically to neuronal cell damage, which represents a significant advantage compared to other biomarkers previously tested. Among the three subunits of neurofilament, neurofilament light (NfL) subunit has been shown to be promising. High levels of NfL have been found in a large range of neurodegenerative disorders, but also in acute events such as traumatic brain injury and stroke. Since it has been possible to measure NfL in serum, obviating the need for cerebrospinal fluid samples, their analysis in the perioperative period has been facilitated. Indeed, serum NfL levels have been recently investigated in the perioperative period in various surgical patient populations. These studies provide us information about the kinetics of perioperative NfL release but they show conflicting results regarding a potential correlation between high perioperative NfL levels and the occurrence of POD.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| EEG for cardiac surgery patients | Experimental | Patients who undergo elective cardiac surgery with cardiopulmonary bypass, from 18 to >75 years old. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| EEG | Procedure | EEG samples will be recorded before and during the cardiac surgery in order to perform spectral and coherence analyses |
|
| Measure | Description | Time Frame |
|---|---|---|
| Postoperative delirium (POD) | Development of POD after cardiac surgery (using CAM, CAM-ICU, flow chart review) | from postoperative awakening in ICU until discharge from the hospital (assessed up to 7 days postoperatively) |
| Measure | Description | Time Frame |
|---|---|---|
| Length of stay | Length of stay in the Intensive Care Unit and in hospital | Up to one month |
| Postoperative cognitive dysfunction | Brief cognitive evaluation by phone (TICS) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Mona Momeni, MD, PhD | Cliniques universitaires Saint-Luc, UCL | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Cliniques universitaires Saint-Luc | Brussels | 1200 | Belgium |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28533746 | Result | Giattino CM, Gardner JE, Sbahi FM, Roberts KC, Cooter M, Moretti E, Browndyke JN, Mathew JP, Woldorff MG, Berger M; MADCO-PC Investigators. Intraoperative Frontal Alpha-Band Power Correlates with Preoperative Neurocognitive Function in Older Adults. Front Syst Neurosci. 2017 May 8;11:24. doi: 10.3389/fnsys.2017.00024. eCollection 2017. | |
| 23820102 |
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Only one group of cardiac patients divided in sub-groups according to their age.
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| Apolipoprotein genotype | Genetic | APOE genotyping will be performed for each patient |
|
| Serum NfL measurements | Biological | 5 perioperative blood samples will be taken to measure the evolution of NfL in the serum (before and until postoperative day 5) |
|
| Preoperative neurocognitive evaluation | Other | Each patient will benefit from a complete neurocognitive evaluation before surgery (battery of validated cognitive tests) |
|
| 6 months after surgery |
| Brown EN, Purdon PL. The aging brain and anesthesia. Curr Opin Anaesthesiol. 2013 Aug;26(4):414-9. doi: 10.1097/ACO.0b013e328362d183. |
| 23587637 | Result | de Waal H, Stam CJ, de Haan W, van Straaten EC, Blankenstein MA, Scheltens P, van der Flier WM. Alzheimer's disease patients not carrying the apolipoprotein E epsilon4 allele show more severe slowing of oscillatory brain activity. Neurobiol Aging. 2013 Sep;34(9):2158-63. doi: 10.1016/j.neurobiolaging.2013.03.007. Epub 2013 Apr 12. |
| 26238230 | Result | Vasunilashorn S, Ngo L, Kosar CM, Fong TG, Jones RN, Inouye SK, Marcantonio ER. Does Apolipoprotein E Genotype Increase Risk of Postoperative Delirium? Am J Geriatr Psychiatry. 2015 Oct;23(10):1029-1037. doi: 10.1016/j.jagp.2014.12.192. Epub 2015 May 21. |
| 29459944 | Result | Evered L, Silbert B, Scott DA, Zetterberg H, Blennow K. Association of Changes in Plasma Neurofilament Light and Tau Levels With Anesthesia and Surgery: Results From the CAPACITY and ARCADIAN Studies. JAMA Neurol. 2018 May 1;75(5):542-547. doi: 10.1001/jamaneurol.2017.4913. |
| 30522125 | Result | Halaas NB, Blennow K, Idland AV, Wyller TB, Raeder J, Frihagen F, Staff AC, Zetterberg H, Watne LO. Neurofilament Light in Serum and Cerebrospinal Fluid of Hip Fracture Patients with Delirium. Dement Geriatr Cogn Disord. 2018;46(5-6):346-357. doi: 10.1159/000494754. Epub 2018 Dec 6. |
| 40131805 | Derived | Lagios MH, Bidoul T, Momeni M, Khalifa C. Is There a Better Timing for Frontal Electroencephalogram Alpha Band Power Quantification to Predict Delirium After Cardiac Surgery? Anesth Analg. 2025 Sep 1;141(3):671-673. doi: 10.1213/ANE.0000000000007492. Epub 2025 Mar 25. No abstract available. |
| 37551153 | Derived | Khalifa C, Lenoir C, Robert A, Watremez C, Kahn D, Mastrobuoni S, Aphram G, Ivanoiu A, Bonhomme V, Mouraux A, Momeni M. Intra-operative electroencephalogram frontal alpha-band spectral analysis and postoperative delirium in cardiac surgery: A prospective cohort study. Eur J Anaesthesiol. 2023 Oct 1;40(10):777-787. doi: 10.1097/EJA.0000000000001895. Epub 2023 Aug 8. |
| ID | Term |
|---|---|
| D000071257 | Emergence Delirium |
| ID | Term |
|---|---|
| D003693 | Delirium |
| D003221 | Confusion |
| D019954 | Neurobehavioral Manifestations |
| D009461 | Neurologic Manifestations |
| D009422 | Nervous System Diseases |
| D011183 | Postoperative Complications |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D012816 | Signs and Symptoms |
| D019965 | Neurocognitive Disorders |
| D001523 | Mental Disorders |
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