Dataset Information

The Predictive Power of Near-Infrared Spectroscopy in Improving Cognitive Problems in Patients Undergoing Brain Surgeries: A Systematic Review.

ABSTRACT: One of the main objectives in neurosurgical procedures is the prevention of cerebral ischemia and hypoxia leading to secondary brain injury. Different methods for early detection of intraoperative cerebral ischemia and hypoxia have been used. Near-infrared spectroscopy (NIRS) is a simple, non-invasive method for monitoring cerebral oxygenation increasingly used today. The aim of this study was to systematically review the brain monitoring with NIRS in neurosurgery. The search process resulted in the detection of 324 articles using valid keywords on the electronic databases, including Embase, PubMed, Scopus, Web of Science, and Cochrane Library. Subsequently, the full texts of 34 studies were reviewed, and finally 11 articles (seven prospective studies, three retrospective studies, and one randomized controlled trial) published from 2005 to 2020 were identified as eligible for systematic review. Meta-analysis was not possible due to high heterogeneity in neurological and neurosurgical conditions of patients, expression of different clinical outcomes, and different standard reference tests in the studies reviewed. The results showed that NIRS is a non-invasive cerebral oximetry that provides continuous and measurable cerebral oxygenation information and can be used in a variety of clinical settings.

SUBMITTER: Mokhtari M

PROVIDER: S-EPMC8995777 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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The Predictive Power of Near-Infrared Spectroscopy in Improving Cognitive Problems in Patients Undergoing Brain Surgeries: A Systematic Review.

Mokhtari Majid M Amirdosara Mahdi M Goharani Reza R Zangi Masood M Tafrishinejad Arash A Nashibi Masoud M Dabbagh Ali A Sadeghi Hassan H Nateghinia Saeedeh S Hajiesmaeili Mohammadreza M Yousefi-Banaem Hossein H Sayehmiri Fatemeh F

Anesthesiology and pain medicine 20220201 1

One of the main objectives in neurosurgical procedures is the prevention of cerebral ischemia and hypoxia leading to secondary brain injury. Different methods for early detection of intraoperative cerebral ischemia and hypoxia have been used. Near-infrared spectroscopy (NIRS) is a simple, non-invasive method for monitoring cerebral oxygenation increasingly used today. The aim of this study was to systematically review the brain monitoring with NIRS in neurosurgery. The search process resulted in ...[more]

PMID: 35433374

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Project description:Background and Objectives: Postoperative cognitive disturbances (POCD) can significantly alter postoperative recovery. Inadequate intraoperative cerebral oxygen supply is one of the inciting causes of POCD. Near-infrared spectroscopy (NIRS) devices monitor cerebral oxygen saturation continuously and can help to guide intraoperative patient management. The aim of the study was to evaluate the applicability of the NIRS-based clinical algorithm during spinal neurosurgery and to find out whether it can influence postoperative cognitive performance. Materials and Methods: Thirty four patients scheduled for spinal neurosurgery were randomized into a study group (n = 23) and a control group (n = 11). We monitored regional cerebral oxygen saturation (rScO2) throughout surgery, using a NIRS device (INVOS 4100). If rScO2 dropped bilaterally or unilaterally by more than 20% from baseline values, or under an absolute value of 50%, the NIRS-based algorithm was initiated in the study group. In the control group, rScO2 was monitored blindly. To evaluate cognitive function, Montreal-Cognitive Assessment (MoCA) scale was used in both groups before and after the surgery. Results: In the study group, rScO2 dropped below the threshold in three patients and the NIRS-based algorithm was activated. Firstly, we verified correct positioning of the head; secondly, we increased mean systemic arterial pressure in the three patients by injecting repeated intravenous bolus doses of Ephedrine, ultimately resulting in an rScO2 increase above the approved threshold level. None of the three patients showed POCD. In the control group, one patient showed a drop in rScO2 of 34% from baseline and presented with a POCD. RScO2 drop occurred with other stable intraoperative measurements. Conclusions: A significant rScO2 drop may occur during spinal surgery in prone position despite other intraoperative measurements remaining stable, allowing it to stay otherwise unrecognized. Use of the NIRS-based clinical algorithm can help to avoid POCD in patients after spinal surgery.

Project description:Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, particularly important in preterm infants who are at an increased risk of immature brain development. An accurate classification of neonatal sleep states can contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) and heart rate (HR) serving as key components in sleep assessment systems for neonates. Recent studies have demonstrated the feasibility of extracting both RR and HR using near-infrared spectroscopy (NIRS) in neonates. This study introduces a comprehensive sleep classification approach leveraging high-frequency NIRS signals recorded at a sampling rate of 100 Hz from a cohort of nine preterm infants admitted to a neonatal intensive care unit. Eight distinct features were extracted from the raw NIRS signals, including HR, RR, motion-related parameters, and proxies for neural activity. These features served as inputs for a deep convolutional neural network (CNN) model designed for the classification of AS and QS sleep states. The performance of the proposed CNN model was evaluated using two cross-validation approaches: ten-fold cross-validation of data pooling and five-fold cross-validation, where each fold contains two independently recorded NIRS data. The accuracy, balanced accuracy, F1-score, Kappa, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) were employed to assess the classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, and XGBoost (XGB), were conducted. Our results reveal the CNN model's superior performance, achieving an average accuracy of 88%, a balanced accuracy of 94%, an F1-score of 91%, Kappa of 95%, and an AUC-ROC of 96% in data pooling cross-validation. Furthermore, in both cross-validation methods, RF and XGB demonstrated accuracy levels closely comparable to the CNN classifier. These findings underscore the feasibility of leveraging high-frequency NIRS data, coupled with NIRS-based HR and RR extraction, for assessing sleep states in neonates, even in an intensive care setting. The user-friendliness, portability, and reduced sensor complexity of the approach suggest its potential applications in various less-demanding settings. This research thus presents a promising avenue for advancing neonatal sleep assessment and its implications for infant health and development.

Dataset Information

The Predictive Power of Near-Infrared Spectroscopy in Improving Cognitive Problems in Patients Undergoing Brain Surgeries: A Systematic Review.

Publications

The Predictive Power of Near-Infrared Spectroscopy in Improving Cognitive Problems in Patients Undergoing Brain Surgeries: A Systematic Review.

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