British journal of anaesthesia最新文献

This narrative review examines artificial intelligence (AI) applications in airway management through a structured literature search completed in July 2025. AI shows promise in predicting difficult airways through facial recognition, voice analysis, and multiparametric assessment. AI models demonstrate improved positive predictive values compared with conventional bedside tests, though prediction remains imperfect. The fundamental challenge of low positive predictive values persists, albeit at reduced levels. For videolaryngoscopy, AI-powered systems provide real-time structure identification, procedural guidance, tracheal tube placement verification, and potentially fewer complications. AI also offers cognitive support during critical scenarios by mitigating decision biases, providing intelligent alarms, and ensuring guideline adherence when human performance might be compromised by stress. In education, AI-enhanced virtual reality simulations create realistic practice environments with personalised feedback tailored to learners' needs. Experimental robotic intubation systems guided by AI algorithms demonstrate performance comparable with experts in controlled settings, though primarily for training and remote applications. Despite these advances, AI should complement rather than replace human expertise. Important challenges remain, including the risk of clinician deskilling, the 'black box' nature of some algorithms limiting transparency, and the need for robust validation before widespread implementation. The ideal approach involves human-machine collaboration where AI compensates for cognitive limitations while physicians contribute clinical judgement, context awareness, and empathy. As AI continues evolving, appropriate ethical frameworks and regulatory oversight must be developed in parallel to ensure these technologies enhance patient safety while preserving the essential human element of healthcare.

Perioperative neurocognitive disorders are common complications after anaesthesia and surgery, particularly among older adults. A growing body of preclinical evidence implicates dysregulation of extrasynaptic γ-aminobutyric acid type A (GABA_A) receptors as a key contributor to these disorders. Unlike synaptic GABA_A receptors that mediate synaptic or phasic inhibition, extrasynaptic GABA_A receptors, that often contain α5 and δ receptor subunits, generate a tonic inhibitory conductance that regulates neuronal excitability and network dynamics. Most anaesthetic drugs are well known to potentiate fast GABAergic inhibitory neurotransmission, but these agents also trigger persistent increases in the cell-surface expression of extrasynaptic GABA_A receptors, especially in brain regions that are critical for cognition such as the hippocampus and prefrontal cortex. Surgery and inflammation similarly cause excess cell-surface expression of extrasynaptic GABA_A receptors. The increased number of receptors enhances the amplitude of a tonic inhibitory current, which disrupts network plasticity and impairs learning, memory, and executive function. This narrative review explores the mechanistic links between perioperative care and alterations in the cell-surface expression of extrasynaptic GABA_A receptors, and the implications of such mechanisms in perioperative neurocognitive disorders. On the basis of these insights, we propose several potential prevention and treatment strategies.

Background: Protamine is administered to reverse unfractionated heparin (UFH) after cardiopulmonary bypass (CPB), but dosing strategies-typically based on protamine-to-heparin (P:H) ratios-vary, and the minimal effective dose remains unclear. Reversal is commonly assessed using activated clotting time (ACT), which may not reliably reflect residual heparin activity. We used pharmacometric modeling to determine a minimal effective P:H ratio and to characterise the anti-factor Xa (anti-Xa) activity-ACT relationship.

Methods: In this prospective, single-centre study, 68 adults undergoing CPB-assisted cardiac surgery were enrolled. A total of 757 blood samples were collected intraoperatively and after UFH reversal to measure anti-Xa activity and ACT. A population pharmacokinetic-pharmacodynamic model was developed using a nonlinear mixed-effects approach to describe UFH neutralisation by protamine. This model was then used to perform Monte Carlo simulations estimating the probability of complete reversal (anti-Xa <0.10 IU ml^-1) at various P:H ratios, based on cumulative intraoperative UFH dose.

Results: Patients received a mean total dose of 30 250 IU UFH and 200 mg protamine i.v. Measured anti-Xa activity decreased to <0.10 IU ml^-1 in all patients within 10 min of protamine initiation, indicating rapid reversal. Model-based simulations predicted that a P:H ratio of 0.625:1 would achieve complete reversal in 95% of patients. Although ACT and anti-Xa activity were positively associated, ACT values varied widely at low anti-Xa concentrations.

Conclusions: A P:H ratio of 0.625:1 provided adequate UFH reversal. Given the imprecision of ACT, fixed low-ratio dosing without routine monitoring could be a practical alternative but requires prospective validation.

Clinical trial registration: EudraCT (2019-000859-14); www.

Clinicaltrials: gov (NCT04092868).