Daily Anesthesiology Research Analysis

BACKGROUND: Accurate prognostication following cardiac arrest (CA) is crucial for informing clinical decisions. Current guidelines do not recommend a specific time point for recording somatosensory evoked potentials (SSEPs) after CA. We evaluated the ability of ultra-early short- and middle-latency SSEPs to predict good an poor neurological outcome and compared its accuracy with that of other predictors recorded early after CA. METHODS: Prospective single-centre study. Sixty-five comatose adults underwent a multimodal prognostic assessment, including neurophysiological (SSEPs and electroencephalogram [EEG]), clinical (pupillary reflexes and myoclonus), and imaging indices (brain computed tomography [CT]) within 6 h post-CA. Serum neuron-specific enolase (NSE) was sampled 12 h post-CA. We analysed the SSEPs N20 wave amplitude and duration, and the presence of the middle-latency N70 wave. Poor outcome was defined as a Cerebral Performance Category (CPC) of 3-5 at hospital discharge. RESULTS: A bilaterally absent N20 wave predicted poor outcome with 100[89-100]% specificity and 67[48-82]% sensitivity. Adding low-amplitude (<1.2 µV), prolonged (>10 ms) N20 waves without N70 increased sensitivity to 93[79-99]% without compromising specificity. Conversely, a high-amplitude (>3 µV) N20 wave with normal duration with preserved N70 predicted good outcome with 94[79-99]% sensitivity and 100[89-100]% specificity. SSEPs outperformed all other early prognostic indices for both good and poor outcome prediction. All poor outcome patients had at least two concordant unfavourable predictors. CONCLUSIONS: Ultra-early quantitative assessment of short- and middle-latency SSEPs provides highly accurate prediction of both good and poor neurological outcomes after CA. This approach may enhance early clinical decision-making and warrants validation in larger cohorts.

BACKGROUND: Obstructive sleep apnoea (OSA) has been thought to increase the risk of respiratory depression from opioids. The primary aim of this study was to assess whether preoperative hypoxaemia by sleep study pulse oximetry imparts greater opioid sensitivity. METHODS: A multicentre observational cohort study with in-cohort dose randomisation was performed in children 2-8 yr of age with OSA undergoing adenotonsillectomy. Ninety patients were assigned to one of two Spo RESULTS: Ninety patients underwent in-cohort randomisation (Spo CONCLUSIONS: Single-dose fentanyl ventilatory effects in paediatric OSA patients during sevoflurane anaesthesia were not associated with preoperative nocturnal hypoxaemia nadir. Fentanyl dosing in children with OSA should not be determined by sleep study Spo CLINICAL TRIAL REGISTRATION: NCT05051189.

BACKGROUND: Protein lactylation has been implicated in stress-responsive cellular mechanisms, yet its role in lung transplantation-associated ischemia-reperfusion injury (IRI) remains undefined. METHODS: Transcriptomic profiles from GSE145989 were analyzed through differential expression analysis (limma) and weighted gene co-expression network analysis (WGCNA). Integrating the identified genes with lactylation-related signatures uncovered key lactylation-related genes (LRGs) as potential targets. Consensus clustering stratified post-reperfusion samples into molecular subtypes with distinct lactylation dynamics. Machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) and random forest, were employed to refine diagnostic biomarkers, which were subsequently incorporated into a nomogram model. External validation was performed using GSE18995 dataset, while single-cell RNA sequencing (GSE220797) was used to map cellular distributions. Lactate levels, global protein lactylation levels, and candidate gene expression were experimentally validated in a murine lung IRI model through lactic acid assay kit, western blotting, immunohistochemistry, immunofluorescence and RT-qPCR. RESULTS: Six LRGs were identified through differential expression patterns, co-expression networks, and lactylation signatures. Consensus clustering revealed two distinct molecular subtypes with differential IRI progression patterns. Four machine learning-optimized biomarkers (SLC2A3, MYC, NLRP3, PIGA) demonstrated robust diagnostic performance. Their differential expression was confirmed in GSE18995. Single-cell data analysis revealed their predominant expression in various cell types. Murine experiments confirmed elevated lactate concentrations in bronchoalveolar lavage fluid and plasma, accompanied by enhanced global protein lactylation and consistent hub gene expression alterations. CONCLUSIONS: This integrative transcriptomic analysis identifies four lactylation-associated regulators of pulmonary IRI, proposing novel therapeutic targets for improving graft survival in lung transplantation.