Cerebral Neuromonitoring During Cardiac Surgery: A Critical Appraisal With an Emphasis on Near-Infrared Spectroscopy

Lewis, Choy; Parulkar, Suraj; Bebawy, John Patrick F; Sherwani, Saadia S.; Hogue, Charles W.

doi:10.1053/j.jvca.2018.03.032

Cited by 58 publications

(64 citation statements)

References 80 publications

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“…There are several limitations associated with NIRS, including that NIRS measurements are affected by mean arterial blood pressure, hemoglobin concentration, anatomy (eg, skull thickness and area of cerebrospinal fluid layer), and differential path length factors 31 . Another disadvantage of NIRS is an extensive intra‐patient and interpatient variability, indicating that NIRS is mostly suitable for measuring variations in an individual patient 12,14,32,33 …”

Section: Methods and Designmentioning

confidence: 99%

Cerebral ischemia detection using artificial intelligence (CIDAI)—A study protocol

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El-Merhi

Liljencrantz

et al. 2020

Acta Anaesthesiol Scand

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Background The onset of cerebral ischemia is difficult to predict in patients with altered consciousness using the methods available. We hypothesize that changes in Heart Rate Variability (HRV), Near‐Infrared Spectroscopy (NIRS), and Electroencephalography (EEG) correlated with clinical data and processed by artificial intelligence (AI) can indicate the development of imminent cerebral ischemia and reperfusion, respectively. This study aimed to develop a method that enables detection of imminent cerebral ischemia in unconscious patients, noninvasively and with the support of AI. Methods This prospective observational study will include patients undergoing elective surgery for carotid endarterectomy and patients undergoing acute endovascular embolectomy for cerebral arterial embolism. HRV, NIRS, and EEG measurements and clinical information on patient status will be collected and processed using machine learning. The study will take place at Sahlgrenska University Hospital, Gothenburg, Sweden. Inclusion will start in September 2020, and patients will be included until a robust model can be constructed. By analyzing changes in HRV, EEG, and NIRS measurements in conjunction with cerebral ischemia or cerebral reperfusion, it should be possible to train artificial neural networks to detect patterns of impending cerebral ischemia. The analysis will be performed using machine learning with long short‐term memory artificial neural networks combined with convolutional layers to identify patterns consistent with cerebral ischemia and reperfusion. Discussion Early signs of cerebral ischemia could be detected more rapidly by identifying patterns in integrated, continuously collected physiological data processed by AI. Clinicians could then be alerted, and appropriate actions could be taken to improve patient outcomes.

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Section: Methods and Designmentioning

confidence: 99%

Cerebral ischemia detection using artificial intelligence (CIDAI)—A study protocol

Block

El-Merhi

Liljencrantz

et al. 2020

Acta Anaesthesiol Scand

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“…Many patients in the aging surgical population may be predisposed to experiencing cerebral ischemia due to preexisting cerebral vascular disease. In these patients, a higher mean arterial blood pressure (MAP) is required to perfuse narrowed arteries and arterioles in the brain (78). Other common causes of perioperative cerebral ischemia include hypoperfusion during cross-clamping, impaired cerebral autoregulation, embolism, hypoxia, and anemia (79,80).…”

Section: Eeg and Detection Of Cerebral Ischemia During Surgical Procementioning

confidence: 99%

Electroencephalography: Clinical Applications During the Perioperative Period

et al. 2020

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Electroencephalography (EEG) monitoring has become technically feasible in daily clinical anesthesia practice. EEG is a sensitive method for detecting neurophysiological changes in the brain and represents an important frontier in the monitoring and treatment of patients in the perioperative period. In this review, we briefly introduce the essential principles of EEG. We review EEG application during anesthesia practice in the operating room, including the use of processed EEG in depth of anesthesia assessment, raw EEG monitoring in recognizing brain states under different anesthetic agents, the use of EEG in the prevention of perioperative neurocognitive disorders and detection of cerebral ischemia. We then discuss EEG utilization in the intensive care units, including the use of EEG in sedative level titration and prognostication of clinical outcomes. Existing literature provides insight into both the advances and challenges of the clinical applications of EEG. Future study is clearly needed to elucidate the precise EEG features that can reliably optimize perioperative care for individual patients.

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“…Further the NIRS signal can be contaminated by peripheral perfusion of the scalp and scull. More studies are needed before the usefulness of NIRS can be clearly determined …”

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confidence: 99%

Are available methods for assessment of cerebral perfusion sufficient for scientific studies or not?

Naredi

2020

Acta Anaesthesiol Scand

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Cerebral Neuromonitoring During Cardiac Surgery: A Critical Appraisal With an Emphasis on Near-Infrared Spectroscopy

Cited by 58 publications

References 80 publications

Cerebral ischemia detection using artificial intelligence (CIDAI)—A study protocol

Cerebral ischemia detection using artificial intelligence (CIDAI)—A study protocol

Electroencephalography: Clinical Applications During the Perioperative Period

Are available methods for assessment of cerebral perfusion sufficient for scientific studies or not?

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