Daily Anesthesiology Research Analysis

BACKGROUND: Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. The authors sought to determine whether internal electroencephalogram (EEG) subparameters extracted by the BIS monitor (Medtronic, USA), a device commonly used to estimate depth of anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest. METHODS: In this retrospective cohort study, a three-layer neural network was trained to predict recovery of consciousness to the point of command following versus not based on 48 h of continuous EEG recordings in 315 comatose patients admitted to a single U.S. academic medical center after cardiac arrest (derivation cohort, n = 181; validation cohort, n = 134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine ( i.e. , the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. The model was trained on hourly averaged measurements of these internal subparameters. This model's performance was compared to the modified Westhall qualitative EEG scoring framework. RESULTS: Maximum prognostic accuracy in the derivation cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, the model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristics curve, 0.86; accuracy, 0.87; sensitivity, 0.83; specificity, 0.88; positive predictive value, 0.71; negative predictive value, 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest. CONCLUSIONS: In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative accepted standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

BACKGROUND: According to the model of the glymphatic system, the directed flow of cerebrospinal fluid (CSF) is a driver of waste clearance from the brain. In sleep, glymphatic transport is enhanced, but it is unclear how it is affected by anesthesia. Animal research indicates partially opposing effects of distinct anesthetics, but corresponding results in humans are lacking. Thus, this study aims to investigate the effect of sevoflurane anesthesia on CSF flow in humans, both during and after anesthesia. METHODS: Using data from a functional magnetic resonance imaging experiment in 16 healthy human subjects before, during, and 45 min after sevoflurane monoanesthesia of 2 volume percent (vol%), the authors related gray matter blood oxygenation level-dependent signals to CSF flow, indexed by functional magnetic resonance imaging signal fluctuations, across the basal cisternae. Specifically, CSF flow was measured by CSF functional magnetic resonance imaging signal amplitudes, global gray matter functional connectivity by the median of interregional gray matter functional magnetic resonance imaging Spearman rank correlations, and global gray matter-CSF basal cisternae coupling by Spearman rank correlations of functional magnetic resonance imaging signals. RESULTS: Anesthesia decreased cisternal CSF peak-to-trough amplitude (median difference, 1.00; 95% CI, 0.17 to 1.83; P = .013) and disrupted the global cortical blood oxygenation level-dependent and functional magnetic resonance imaging-based connectivity (median difference, 1.5; 95% CI, 0.67 to 2.33; P < 0.001) and global gray matter-CSF coupling (median difference, 1.19; 95% CI, 0.36 to 2.02; P = 0.002). Remarkably, the impairments of global connectivity (median difference, 0.94; 95% CI, 0.11 to 1.77; P = 0.022) and global gray matter-CSF coupling (median difference, 1.06; 95% CI, 0.23 to 1.89; P = 0.008) persisted after re-emergence from anesthesia. CONCLUSIONS: Collectively, the authors' data show that sevoflurane impairs macroscopic CSF flow via a disruption of coherent global gray matter activity. This effect persists, at least for a short time, after regaining consciousness. Future studies need to elucidate whether this contributes to the emergence of postoperative neurocognitive symptoms, especially in older patients or those with dementia.

BACKGROUND: Delirium is an acute state of confusion associated with adverse postoperative outcomes. Delirium is diagnosed clinically using screening tools; most cases go undetected. Identifying a delirium biomarker would allow for accurate diagnosis, application of therapies, and insight into causal pathways. To agnostically discover novel biomarkers of delirium, a case-control substudy was conducted using the Vascular Events in Surgery Patients Cohort Evaluation (VISION) Cardiac Surgery Biobank. The objective was to identify candidate biomarkers to investigate in future studies. METHODS: The study gathered a convenience sample of 30 patients with delirium on postoperative day 1 matched to 30 controls by age, sex, ethnicity, center, and cardiopulmonary bypass time. The Olink Explore 3K platform was used to identify blood protein alterations on postoperative day 3. Protein concentrations were expressed as normalized protein expression units (log 2 fold scale). Protein expression was compared between cases and controls using a paired t test and identified significantly different biomarkers based on a false discovery rate-adjusted P value of less than 0.05. RESULTS: Of 2,865 unique serum proteins, 26 (0.9%) were significantly associated with delirium status; all were elevated in cases versus controls at a false discovery rate of less than 0.05. Pathway analysis identified calcium-release channel activity ( Padj = 0.02) and GTP-binding ( Padj = 0.005) functions as characteristic of proteins associated with delirium. The top three differentially expressed biomarkers were FKBP1B ( Padj = 0.003), C2CD2L ( Padj = 0.004), and RAB6B ( Padj = 0.004). The inflammatory biomarker interleukin-8 (CXCL8; mean difference = 2.36; P = 3.6 × 10- 4 ) was also associated with delirium. CONCLUSIONS: The study identified 26 biomarkers significantly associated with delirium; all are novel except for interleukin-8. An association between delirium and recognized neuroinflammatory proteins or markers of brain injury was not identifed, which supports using biomarkers to differentiate between delirium and other neurologic conditions. While exploratory, the study's findings support using biomarkers to diagnose postoperative delirium and validate using agnostic screens to identify potential delirium biomarkers.