Journal of neurosurgical anesthesiology
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J Neurosurg Anesthesiol · Apr 2023
Meta AnalysisInhalational Versus Propofol-based Intravenous Maintenance of Anesthesia for Emergence Delirium in Adults: A Meta-analysis and Trial Sequential Analysis.
Emergence delirium (ED) is a severe postoperative complication that increases the risk for injury, self-extubation, and hemorrhage. Inhalational maintenance of anesthesia is a risk factor for ED in pediatric patients, but its impact in adults is undefined. This meta-analysis compares the incidence of ED between inhalational and propofol-based intravenous maintenance of anesthesia. ⋯ Compared with propofol-based intravenous maintenance of anesthesia, inhalational maintenance increased the incidence of ED in adults (risk ratio [RR], 2.02; 95% confidence interval [CI]: 1.30-3.14; P =0.002). This was confirmed by sensitivity analysis, trial sequential analysis, and subgroup analyses of studies that assessed ED via Aono's four-point scale (RR, 3.72; 95% CI: 1.48-9.31; P =0.005) and the Ricker Sedation Agitation Scale (RR, 3.48; 95% CI: 1.66-7.32; P =0.001), studies that included sevoflurane for maintenance of anesthesia (RR, 1.87; 95% CI: 1.13-3.09; P =0.02), studies that reported ED as the primary outcome (RR, 2.73; 95% CI: 1.53-4.86; P =0.0007), and studies that investigated ocular (RR, 2.98; 95% CI: 1.10-8.10; P =0.03), nasal (RR; 95% CI: 1.27-6.50; P =0.01), and abdominal (RR, 3.25; 95% CI: 1.12-9.40; P =0.03) surgeries, but not intracranial surgery (RR, 0.72; 95% CI: 0.34-1.54; P =0.40). In summary, inhalational maintenance of sevoflurane was a risk factor for ED compared with propofol-based intravenous maintenance in adults who underwent ocular, nasal, and abdominal surgeries but not intracranial surgery.
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Intraoperative neuromonitoring has been a valuable tool for ensuring the functional integrity of vital neural structures by providing real-time feedback to the operative team during procedures where neurological structures are at risk. Commonly used intravenous and inhaled anesthetic drugs are known to affect waveform parameters measured with various intraoperative neuromonitoring modalities. While the concept of opioid-sparing multimodal analgesia has gained popularity in recent years, the impact of such a strategy on intraoperative neuromonitoring remains poorly characterized, in contrast to the more well-established concepts and literature regarding the effects of other hypnotic agents on neuromonitoring quality. The purpose of this focused review is to provide an overview of the clinical evidence pertaining to the pharmacological interaction of certain multimodal analgesics with routine intraoperative neuromonitoring modalities.
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J Neurosurg Anesthesiol · Apr 2023
Incremental Cost-effectiveness Analysis on Length of Stay of an Enhanced Recovery After Spine Surgery Program: A Single-center, Retrospective Cohort Study.
Enhanced recovery after spine surgery (ERAS) is increasingly utilized to improve postoperative outcomes and reduce cost. There are limited data on the monetary benefits of ERAS when incorporating the costs of developing, operationalizing, and maintaining ERAS programs. The objective of this study was to calculate the incremental cost-effectiveness of a spine surgery ERAS program, modeling hospital and operational cost and length of stay (LOS). ⋯ We report a real-world, cost-effectiveness analysis following implementation of an ERAS program for spine surgery at a quaternary medical center. Our study demonstrated that considering LOS as the sole determinant, standard care is the dominant cost-effective strategy compared with the ERAS protocol.
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J Neurosurg Anesthesiol · Apr 2023
A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury.
Traumatic brain injury (TBI) is a major cause of death and disability. Episodes of hypotension are associated with worse TBI outcomes. Our aim was to model the real-time risk of intraoperative hypotension in TBI patients, compare machine learning and traditional modeling techniques, and identify key contributory features from the patient monitor and medical record for the prediction of intraoperative hypotension. ⋯ This study developed a model for real-time prediction of intraoperative hypotension in TBI patients, which can use computationally efficient machine learning techniques and a streamlined feature-set derived from patient monitor data.