Anesthesiology最新文献

Background: Effective pain recognition and treatment in perioperative environments reduce length of stay and decrease risk of delirium and chronic pain. We sought to develop and validate preliminary computer vision-based approaches for nociception detection in hospitalized patients.

Methods: Prospective observational cohort study using red-green-blue camera detection of perioperative patients. Adults (≥18 years) admitted for surgical procedures to the San Francisco Veterans Affairs Medical Center (SFVAMC) were included across 2 study phases: (1) algorithm development phase and (2) internal validation phase. Continuous recordings occurred perioperatively across any postoperative setting. We inputted facial images into convolutional neural networks using a pretrained backbone, to detect (1) critical care pain observation tool (CPOT) and (2) numerical rating scale (NRS). Outcomes were binary pain/no-pain. We performed external validation for CPOT and NRS classification on data from University of Northern British Columbia-McMaster University (UNBC) and Delaware Pain Database. Perturbation models were used for explainability.

Results: We included 130 patients for development, 77 patients for validation cohort and 25 patients from UNBC and 229 patients from Delaware datasets for external validation. Model area under the curve of the receiver operating characteristic for CPOT models were 0.71 (95% confidence interval [CI] 0.70, 0.74) on the development cohort, 0.91 (95% CI 0.90, 0.92) on the SFVAMC validation cohort, 0.91 (0.89, 0.93) on UNBC and 0.80 (95% CI 0.75, 0.85) on Delaware. NRS model had lower performance (AUC 0.58 [95% CI 0.55, 0.61]). Brier scores improved following calibration across multiple different techniques. Perturbation models for CPOT models revealed eyebrows, nose, lips, and foreheads were most important for model prediction.

Conclusions: Automated nociception detection using computer vision alone is feasible but requires additional testing and validation given small datasets used. Future multicenter observational studies are required to better understand the potential for automated continuous assessments for nociception detection in hospitalized patients.

Background: Orexin neuropeptides help regulate sleep/wake states, respiration, and pain. However, their potential role in regulating breathing, particularly in perioperative settings, is not well understood. TAK-925 (danavorexton), a novel, orexin receptor 2-selective agonist, directly activates neurons associated with respiratory control in the brain and improves respiratory parameters in rodents undergoing fentanyl-induced sedation. This study assessed the safety and effect of danavorexton on ventilation in healthy men in an established remifentanil-induced respiratory depression model.

Methods: This single-center, double-blind, placebo-controlled, two-way crossover, phase 1 trial randomized (1:1) 13 healthy men to danavorexton (11mg [low-dose] then 19mg [high-dose]) or placebo, under remifentanil infusion, on two occasions separated by a ≥36-hour washout period. Remifentanil infusion was titrated under isohypercapnic conditions to achieve ~30% to 40% decrease in minute ventilation (from ~20 to ~14 L/minute) before danavorexton/placebo administration. Assessments included safety, ventilation measurements, sedation, and pain tolerance.

Results: 4 (30.8%) danavorexton-treated participants and 1 (8.3%) placebo-treated participant experienced treatment-emergent adverse events (all mild in severity). Insomnia, lasting 1 day, occurred in 1 participant, and was considered related to danavorexton. Compared with placebo, low- and high-dose danavorexton significantly increased ventilation variables (observed mean [95% confidence interval] change, sensitivity analysis model-based p-values) including minute volume (8.2[5.0, 11.4] and 13.0[9.4, 16.5] L/min), tidal volume (312[180, 443] and 483[309, 657] mL), and respiratory rate (3.8[1.9, 5.7] and 5.2[2.7, 7.7] breaths/min) (all P<0.001). High-dose danavorexton significantly decreased sedation on visual analog scale (-29.7[-54.1, -5.3] mm, P<0.001) and Richmond Agitation Sedation Scale (0.4[0.0, 0.7], P<0.001), compared with placebo. Improvements in respiratory variables continued beyond completion of danavorexton infusion. No significant differences in pain tolerance were observed between danavorexton doses or between danavorexton and placebo (~13% increase from baseline; low-dose:P=0.491; high-dose:P=0.140).

Conclusions: Danavorexton has effects on respiration and wakefulness in an opioid-induced respiratory depression setting without reversing opioid analgesia.

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, we conducted a case-control sub-study using the VISION-Cardiac Surgery biobank. Our objective was to identify candidate biomarkers to investigate in future studies.

Methods: We obtained a convenience sample of 30 patients with delirium on postoperative day 1 matched to 30 matched 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 (NPx) units (log2 fold scale). We compared protein expression between cases and controls using a paired t-test and reported significantly different biomarkers based on a False Discovery Rate (FDR)-adjusted p-value<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 an FDR<0.05. Pathway analysis identified "calcium-release channel activity" (Padj=0.02) and "Guanosine 5' triphosphate (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 IL-8 (CXCL8) (mean difference = 2.36; P=3.6x10-4) was also associated with delirium.

Discussion: We identified 26 biomarkers significantly associated with delirium; all are novel except for IL-8. We did not identify an association between delirium and recognized neuro-inflammatory proteins and markers of brain injury, which supports using biomarkers to differentiate between delirium and other neurological conditions. While exploratory, our findings support using biomarkers to diagnose postoperative delirium and validate using agnostic screens to identify potential delirium biomarkers.

Introduction: Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. We sought to determine whether internal EEG subparameters extracted by the Bispectral Index (BIS) monitor, 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, we trained a 3-layer neural network to predict recovery of consciousness to the point of command following versus not based on 48 hours of continuous EEG recordings in 315 comatose patients admitted to a single US 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. Our model was trained on hourly-averaged measurements of these internal subparameters. We compared this model's performance 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, our model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristic 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 gold-standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

Background: Disparities in postoperative nausea and vomiting (PONV) and its prophylaxis may exist based on race, ethnicity, and socioeconomic status (SES). Our objective was to evaluate whether patients from racial and ethnic minority groups and patients from lower SES backgrounds received less appropriate PONV prophylaxis and experienced higher rates of PONV and post-discharge nausea and vomiting (PDNV).

Methods: This retrospective cohort study included 23,333 adults who underwent major surgeries (total knee arthroplasty, cholecystectomy, hysterectomy, and prostatectomy) from 2017-2022 in a single, multi-state hospital system. Outcomes included prophylactic antiemetic administration according to consensus guidelines as well as occurrence of PONV and PDNV, with predictors being patient race and ethnicity, payor type, and community-level SES.

Results: About 45% (n=10,407) of patients received guideline-recommended PONV prophylaxis. Regression models showed statistically significant differences in appropriate PONV prophylaxis by race and ethnicity as well as community-level SES, with Black (OR=0.76; 95% CI: 0.63, 0.92) and Hispanic (OR=0.82; 95% CI: 0.70, 0.96) patients having lower odds of receiving appropriate antiemetic prophylaxis compared to non-Hispanic White patients. Approximately 11% of patients (n=2522) experienced PONV in the Post-Anesthesia Care Unit (PACU), and about 19.5% of patients (n=4540) experienced PDNV. No significant differences in PONV were observed in the PACU among different groups, however, Black, Hispanic, Other races and ethnicities, and patients with Medicaid had higher odds of PDNV.

Conclusion: The study identified differences in appropriate PONV prophylaxis by race and ethnicity as well as community-level SES. There were no differences in PONV by our predictors, but higher odds of PDNV by race and ethnicity and payor. This study underscores the importance of data stratification in quality measures to identify disparities in perioperative care; it can lead to changes in perioperative anesthetic management. Further research should explore these associations in a broader cohort and address potential confounding sources.

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 (fMRI) experiment in 16 healthy human subjects before, during, and 45 minutes after sevoflurane mono-anesthesia of 2vol%, we related grey matter blood-oxygenation-level dependent (BOLD) signals to CSF flow, indexed by fMRI signal fluctuations, across the basal cisternae. Specifically, CSF flow was measured by CSF fMRI signal amplitudes, global grey matter (gGM) functional connectivity by the median of inter-regional GM fMRI Spearman rank correlations, and gGM-CSF basal cisternae coupling by Spearman rank correlations of fMRI signals.

Results: Anesthesia decreased cisternal CSF peak-to-trough amplitude (median difference Mdn-diff = 1.00, 95% CI [0.17 1.83], p = .013), disrupted the global, cortical BOLD-fMRI-based connectivity (Mdn-diff = 1.5, 95% CI [0.67, 2.33], p < 0.001) and, global grey matter (gGM)-CSF coupling (Mdn-diff = 1.19, 95% CI [0.36, 2.02], p = 0.002). Remarkably, the impairments of global connectivity (Mdn-diff = 0.94, 95% CI [0.11, 1.77], p = 0.022) and gGM-CSF coupling (Mdn-diff = 1.06, 95% CI [0.23, 1.89], p = 0.008) persisted after re-emergence from anesthesia.

Conclusions: Collectively, our data show that sevoflurane impairs macroscopic CSF flow via a disruption of coherent gGM 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.