Anesthesiology and Perioperative Science最新文献

The National Health Service (NHS) in England has set hospitals a target of achieving > 85% in a metric called ‘capped theatre utilisation’ (CTU), as central to its post-pandemic surgical waiting list recovery planning. This could serve as a model internationally, as other countries seek to improve operating theatre efficiency. Our review presents an analysis of what CTU means in the context of other measures of theatre ‘utilisation’, serving as a tutorial for clinical leaders, managers and all staff involved in theatres better to understand the metrics sometimes being used to assess their performance. We present results of a theoretical sensitivity analysis to assess how CTU values vary for hypothetical operating lists of three different structures (number of cases, their duration, and intercase gap times), as the stated start and finish times are shifted backwards and forwards in time. We then present results of our interrogation of the NHS Model Health Operating Theatres database to assess hospitals' CTU performance over three years. We discovered that in theory, CTU was especially sensitive to both stated list start times and list structure. The relationship to start time was asymmetric: early starts in one list did not compensate for loss of CTU value with late start in another list, when values were averaged across lists. This sensitivity analysis also predicted wide weekly CTU fluctuations, with values < 85% more likely than > 85%, especially for tertiary referral centres predominantly undertaking long, complex procedures. Our interrogation of the database confirmed these predictions. Moreover, we discovered many instances of implausible CTU values and underlying patterns indicating fundamental flaws in the CTU algorithm, rather than data entry errors. We conclude that CTU, and the NHS target of 85%, is not a suitable metric for operating theatre performance. It has proved unachievable in any sustainable way, and its underlying algorithm produces aberrant values. We discuss the serious consequences of basing other national policies or funding models on a fundamentally flawed metric. These results have lessons for international healthcare systems.

Purpose

A bench study to assess the elimination of sevoflurane from an anesthetic workstation using three different processing methods.

Methods

Sevoflurane concentrations from samples of the inspiratory breathing hose and air samples from within the investigation room were assessed during predetermined flush out intervals using Gas Chromatography-Ion Mobility Spectrometry. The primary objective was to determine the time to reach concentrations below 5 ppm_v.

Results

Reduction of sevoflurane volume concentrations below a threshold of 5 ppm_v was achieved within the first minute after removal of the vaporizer and the complete exchange of the soda and the breathing system and within the 15 min measurement interval after inclusion of two activated charcoal filters without a 90 s of flushing and without changing of the breathing hoses as required by the manufacturer.

Conclusions

Complete removal of the vaporizer and an exchange of the soda and the ventilation unit most quickly reduced sevoflurane concentrations, but the total processing interval may exceed 30 min. Inserted activated charcoal filters without a previous 90 s of flushing and without changing the breathing hoses followed by flushing only with raised fresh gas flow allow ventilation below trigger threshold to be reached within due time.

Purpose

Postoperative pain management is a critical aspect of surgical care, significantly influencing patient recovery and outcomes. This study aims to evaluate the impact of opioid use on hospital stay and mortality rates among patients undergoing abdominal surgeries in Brazil, where healthcare disparities may affect postoperative outcomes.

Methods

This observational cross-sectional study was conducted at the University Hospital Onofre Lopes in Natal, Brazil. We included 385 patients aged 18 years or older who underwent abdominal surgery in 2023. Data were collected from medical records, focusing on analgesic therapy, opioid types, dosages, and patient demographics. Statistical analyses were performed using JAMOVI software.

Results

Opioid consumption was prevalent among the cohort, with 89.6% of patients receiving opioids postoperatively. Tramadol was the most frequently administered opioid, followed by nalbuphine and morphine. Patients treated with morphine had a significantly extended median hospital stay (15.5 days) compared to those receiving tramadol or nalbuphine (2 days). Morphine use was also associated with a higher mortality rate, with these patients being 16 times more likely to die compared to those on weaker opioids. Older age and extended hospital stays were additional risk factors for increased mortality.

Conclusion

Opioids were used extensively in this cohort as the main analgesic drug for postoperative pain management after abdominal surgeries. Use was associated with prolonged hospital stay and higher probability of death.

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.