Anesthesiology and Perioperative Science最新文献

Focus on the implications of common perioperative drugs for mitochondrial quality control and their subsequent impact on the overall physiological condition has been increasing. This review discusses the effects of perioperative drugs, such as intravenous and inhaled anesthetics, analgesics, local anesthetics on mitochondrial quality and their underlying mechanisms. These drugs influence mitochondrial properties, including morphology, dynamics, energy metabolism, and protein expression, thereby affecting the clinical outcomes of patients undergoing surgery. Such effects can be either protective or detrimental and are contingent upon multiple variables such as the specific drug used, dosage, application timing, and the patient's overall health status. Recognizing the effects of these perioperative drugs on mitochondrial quality control is crucial to selecting safer anesthetic protocols, reducing postoperative complications, enhancing postoperative recovery, and gaining insights into the development of innovative treatment methodologies and optimization of perioperative care.

Perioperative anesthetic management entails a multitude of decision-making processes within complex medical scenarios. These demand the continuous and dynamic execution of precise decisions which poses significant challenges. In the age of big data, the exponential growth in data volume from diverse sources has revolutionized many fields, including healthcare, finance, and marketing. Machine learning has emerged as a powerful tool for analyzing big data, enabling the handling of large datasets and uncovering intricate patterns and relationships. The application of big data and artificial intelligence algorithms is gradually being integrated, enabling effective task completion in various stages of perioperative management, including risk prediction, decision support, and auxiliary examination. Through in-depth analysis of big data, healthcare professionals can gain insights into patient prognoses. This review provides a comprehensive overview of the distinctive features of perioperative big data and its applications in anesthesia management during the perioperative period.

Purpose

Delirium has a significant impact on millions of people globally and is closely linked to an unfavorable prognosis for complications. Observational studies suggest that plasma metabolites may play significant roles as markers and effectors of delirium, but causal relationships have not yet been elucidated. Based on the most recent genome-wide association study (GWAS) data, our study aims to present novel insights into the genetic relationship between delirium and plasma metabolites. This investigation offers potential clues for utilizing plasma metabolites as predictors of delirium development.

Methods

We performed a thorough Mendelian randomization (MR) analysis to investigate the causal relationship between 1,091 individual metabolites and 309 metabolite ratios in plasma with respect to delirium. Inverse-variance weighting (IVW) was employed as the primary estimation method, while MR-Egger and weighed median methods were utilized to assess the robustness of the results. Sensitivity analyses encompassing the MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test, Cochran Q test, leave-one-out analysis and MR Egger intercept analysis were also undertaken. Additionally, the MR Steiger test was performed to explore any potential reverse causal effect of metabolites on delirium.

Results

A total of 63 types of plasma metabolites associated with delirium were detected using the IVW method (p < 0.05). Among the known metabolites, our analysis revealed that two specific metabolites (1-palmitoyl-2-palmitoleoyl-gpc (16:0/16:1) and homovanillate) and a metabolite ratio (phosphate to oleoyl-linoleoyl-glycerol (18:1 to 18:2)) demonstrated a consistent and significant causal relationship with delirium across all analysis methods. Finally, no evidence of pleiotropy was detected in our analysis.

Conclusions

Our study has revealed a causal association between blood metabolites and the risk of delirium. homovanillate is known to be associated with immunity and redox, 1-palmitoyl-2-palmitoleoyl-gpc (16:0/16:1) and metabolite ratio (phosphate to oleoyl-linoleoyl-glycerol (18:1 to 18:2)) may play a role in lipid regulation. These findings may provide fresh insights into the identification of suitable diagnostic markers and potential treatment strategies focused on specific plasma metabolites in patients with delirium. However, further experiments are required to gain a comprehensive understanding of the underlying biological mechanisms involved.

Sleep is an essential physiological process that promotes physical recovery and helps consolidate learning and memory. Common manifestations of sleep disturbances include insomnia, hypersomnia, circadian rhythm disorders, and parasomnias, all of which impair cognitive function, particularly in the elderly. Cognitive impairment is a significant factor that threatens the quality of life in the elderly, and there is currently no effective treatment for conditions such as dementia. The relationship between sleep and cognition is complex. Studies have shown that sleep disorders adversely affect cognitive function and increase the incidence of cognitive decline. This article focuses on sleep disturbances and their effects on the cognition of the elderly by reviewing research conducted over the past 20 years and describing potential mechanisms. Additionally, we explore the relationship between sleep and cognition during the perioperative period, aiming to identify strategies for optimizing perioperative sleep quality. We believe this review provides a deeper understanding of the association between sleep and cognition and offers a new perspective for perioperative management.

Purpose

Low back pain (LBP) is a common and recurring public health problem that affects sufferers both physically and mentally and warrants further research. A succession of studies have suggested a plausible role for inflammatory cytokines in the pathogenesis of LBP. To date, there is no conclusive mechanism explaining how inflammatory cytokines affects LBP.

Methods

A bidirectional two-sample Mendelian randomization (MR) investigation was undertaken in two stages. The initial phase encompassed 41 inflammatory cytokines as the exposure, with LBP as the outcome, and the subsequent phase adopted the inverse approach. A total of 41 blood inflammatory cytokines were extracted from the genome-wide association study meta-analysis database, encompassing 8,293 individuals. Data pertaining to LBP were acquired from the Finnish biobank. Primary findings were computed using inverse-variance weighting (IVW), while sensitivity analyses accounting for pleiotropy and invalid instruments were conducted utilizing the weighted-median estimator, MR-Egger, and MR Pleiotropy RESidual Sum and Outlier.

Results

Our results suggest that higher levels of Macrophage migration inhibitory factor (MIF) as well as lower levels of C-C motif chemokine ligand 3 (CCL3) are associated with an increased risk of LBP (odds ratio [OR] = 1.134, 95% confidence interval [CI ]= 1.032–1.245, P = 0.009; OR = 0.887, 95% CI = 0.803–0.980, P = 0.018). Moreover, there was no heterogeneity and horizontal pleiotropy observed in the sensitivity analysis. In contrast, in studies of the effect of LBP on inflammatory cytokines, genetically determined LBP had no causal effect on 41 inflammatory cytokines (IVW P > 0.05).

Conclusions

Our study confirms that the levels of circulating MIF and CCL3 may be regarded as valuable circulating inflammatory biomarkers for the management of LBP in clinical practice and as potential molecules for future mechanistic investigation and drug target identification.

High-flow nasal cannula (HFNC) has been widely promoted during the COVID-19 pandemic to circumvent invasive mechanical ventilation. While there are several reported benefits, randomized trials demonstrate inconsistent variable success. We hypothesize that this is due to variable stopping criteria. This systematic review’s purpose is to review these criteria and investigate any associations with HFNC outcomes. We searched PubMed and EMBASE for all English-language randomized controlled trials (RCTs) published from January 1, 2007, to December 31, 2022, focusing on respiratory rate as a threshold for escalation of respiratory support. Subgroup analysis was conducted based on trial failure criteria, and intubation and mortality benefits were studied. Fisher’s exact test was performed following a 5% level of significance. Of the 22 RCTs included, 4 (18.2%) reported significant intubation benefits and 1 (0.05%) reported significant mortality benefit. The presence of objective failure criteria with a prespecified high respiratory rate threshold (35 breaths per minute or higher) had a significant effect on intubation rate reduction (P = 0.02). However, this result might be limited by the heterogeneity of the included studies. Further RCTs are required to confirm this conclusion. Given that a high respiratory rate threshold was associated with a reduction of intubation without increasing mortality, we hypothesize that among patients receiving HFNC who were eventually not intubated, the avoidance of intubation led to better clinical outcomes, while among eventually intubated patients, delays led to poorer outcomes.