Daily Anesthesiology Research Analysis

Opioids are essential analgesics for managing severe pain but can paradoxically increase pain sensitivity (hyperalgesia) and diminish analgesic efficacy (tolerance). Hyperactivity of NMDA-type glutamate receptors (NMDARs) at primary afferent terminals in the spinal cord contributes to both phenomena; however, the underlying signaling mechanisms remain unclear. Here, we report that morphine administration in rats promoted the translocation of monomeric BRAF, an oncogenic kinase, from the dorsal root ganglion (DRG) to spinal cord synaptosomes, leading to increased MEK-ERK phosphorylation at nociceptor central terminals. BRAF physically interacted with NMDARs in both rat and human spinal cords. Inhibition of BRAF activity with vemurafenib reversed morphine-induced NMDAR phosphorylation and synaptic localization of α2δ-1-bound NMDARs. Vemurafenib also abolished morphine-induced presynaptic NMDAR hyperactivity in spinal dorsal horn neurons. Correspondingly, conditional Braf knockout in DRG neurons normalized morphine-enhanced NMDAR phosphorylation, synaptic trafficking of α2δ-1-bound NMDARs, and NMDAR hyperactivity in the spinal cord. Furthermore, pharmacological inhibition of BRAF or MEK, or Braf deletion in DRG neurons, enhanced morphine analgesia while mitigated morphine-induced hyperalgesia and tolerance. These findings identify BRAF overactivity at nociceptor central terminals as a key mediator of opioid-induced NMDAR hyperactivity. Clinically approved BRAF inhibitors could be repurposed to enhance opioid analgesia while minimizing adverse effects.

BACKGROUND: Postoperative delirium is a common and serious complication after general anesthesia; its accurate prediction remains a substantial challenge in perioperative medicine. Existing models primarily rely on clinical variables and may have limited predictive accuracy. This study aimed to evaluate the added value of heart rate variability parameters in predicting postoperative delirium and construct an interpretable multimodal predictive model. METHODS: In this prospective observational study, 1418 patients undergoing general anesthesia were included. Seventy-three features, including electrocardiogram abnormalities and heart rate variability time-, frequency-, and nonlinear-domain indicators, were extracted from electrocardiogram data. Postoperative delirium was assessed using the Chinese version of the 3-Minute Diagnostic Interview for Delirium within 3 days postoperatively. Feature selection was conducted by combining least absolute shrinkage and selection operator (LASSO) regression, the Boruta algorithm, and random forests, and 10 machine learning models were developed. Model performance was evaluated through receiver operating characteristic curves and decision curve analysis, with interpretability assessed via Shapley additive explanations. Clinical prediction tools were derived from key features. We used an external validation set to further evaluate the generalization ability of the models. RESULTS: Postoperative delirium occurred in 255 (18%) patients. Seventeen key predictors were identified in total. The combined clinical-electrocardiogram-heart rate variability model demonstrated the highest predictive performance (area under the curve = 0.728), outperforming clinical-only (area under the curve = 0.673) and electrocardiogram-only models (area under the curve = 0.679). Logistic regression showed the highest discrimination. In the external validation set, the model maintained robust performance with an area under the curve value of 0.836. Shapley additive explanations highlighted seven core predictors: atrial or ventricular arrhythmia, operative time, ST-segment abnormalities, age, American Society of Anesthesiologists classification, heart rate variability entropy, and overall electrocardiogram abnormalities. A nomogram and online platform enabled personalized risk assessment. CONCLUSIONS: Our results indicate that integrating heart rate variability with clinical and electrocardiogram features significantly enhances the personalized predictive efficacy of postoperative delirium.

Opioids, most intravenous anesthetics and various illicit substances can cause fatal respiratory depression by depressing central respiratory networks. ENA-001, a ventilatory modulator targeting the carotid bodies, has emerged as a potential countermeasure without impairing analgesia or hypnosis. We conducted a literature review to evaluate the efficacy and safety of ENA-001. This scoping review summarizes the current evidence base for ENA-001 from in vitro, animal experiments, and human volunteer studies. A comprehensive search was conducted across several electronic databases to identify all available literature describing its effects as a respiratory stimulant and as a reversal agent of drug-induced respiratory depression. We identified eight relevant publications, four describing data in humans and four using animal models (mice, rats, and non-human primates). ENA-001 increased ventilation and attenuated respiratory depression induced by morphine and the combination of xylazine and fentanyl. The primary site of action was localized to the carotid bodies, specifically the pore-forming α-subunit of the KCa1.1 (BK) channel. In humans, ENA-001 increased minute ventilation and reduced end-tidal carbon dioxide under poikilocapnic conditions. In experimental human models of alfentanil- and propofol-induced respiratory depression, ENA-001 significantly improved isohypercapnic minute ventilation and fully restored the hypoxic ventilatory response, respectively, without impairing sedation or analgesia. Across all studies, the safety profile was favorable, with no serious adverse events reported. In conclusion, ENA-001 is a first-in-class, peripherally acting respiratory stimulant that effectively reverses drug-induced respiratory depression. These findings support its continued clinical development for opioid- and anesthetic-induced respiratory compromise.