Emergence form general anesthesia is a critical period in perioperative care where serious complications may arise. This article reviews the factors that affect normal emergence from anesthesia and common causes and consequences of delayed emergence. The review also focuses on strategies that can be implemented before the conclusion of surgery to optimize emergence from general anesthesia. It examines approaches to optimize return of consciousness, ventilation and oxygenation, as well as measures to prevent coughing, agitation, and delayed emergence. Recent evidence indicates that emergence from anesthesia is an active neurologic process driven by a wide network of arousal circuits and not a pharmacokinetic mirror image of induction. Emergence is influenced by neural inertia, which makes the brain resistant to shifting states between consciousness and unconsciousness. Some drugs including doxapram and danavorexton have been found to decrease emergence time through a variety of pharmacological mechanisms. However, these drugs are in an experimental phase of development. Optimizing emergence from general anesthesia includes careful planning of strategies to return the patient to pre-anesthetic mental, physiological, and comfort baselines while preventing acute postoperative pain, coughing, and agitation. Optimization of oxygenation and ventilation, appropriate reversal of neuromuscular blockade, and prevention of agitation and delayed emergence are key components of a well-designed emergence plan.
This narrative review examines the human factors challenges to trauma anesthesia, with particular focus on how excessive environmental noise and operational chaos degrade clinician performance. We explore the ways in which acoustic interference, team composition, flow disruptions and cognitive overload compromise team performance during trauma resuscitation and review potential mitigation strategies. Sound levels in trauma bays frequently exceed thresholds known to impair cognitive function and short-term memory. Communication failures and coordination breakdowns, rather than individual technical errors, account for most observed safety threats during resuscitation. Multiple behavioral assessment frameworks have linked stronger non-technical skills to faster crisis resolution and reduced adverse events. Technical proficiency alone cannot overcome chaotic environments or flawed systems. Improving trauma anesthesia outcomes requires incorporating human factors principles into training curricula and system design. Comparative research examining team models, composition and anesthesia-specific trauma education represents a critical next step.
Acute kidney injury (AKI) is a common postoperative complication with no established pharmacologic prevention. Sodium bicarbonate (NaHCO₃) has been studied extensively for its renoprotective potential, but evidence is conflicting. Its influence depends on the patient’s prior renal function and the type of renal injury. We performed an updated meta-analysis to evaluate the efficacy and safety of perioperative intravenous NaHCO₃ for preventing renal injury after major surgery. We searched PubMed, Embase, Web of Science, and Cochrane Library for randomized controlled trials comparing perioperative NaHCO₃ with control in adult patients undergoing major surgery (9 cardiac surgery trials and 1 orthotopic liver transplantation trial). Primary outcome was AKI incidence; secondary outcomes included serum creatinine change at 48 h, need for renal replacement therapy (RRT), and all-cause mortality. Random-effects models were used. Ten randomized controlled trials involving 1,544 patients were included. Perioperative NaHCO₃ did not reduce AKI incidence compared with control therapy (RR = 1.02, 95
Ambulatory surgery is an increasingly dominant mode of elective surgical care worldwide, yet the post-discharge period remains a potentially critical phase of the patient’s journey. This review examines current evidence and emerging practices in post-discharge monitoring, complication management, and digital follow-up tools for patients after day-case surgery. Structured nurse-led telephone follow-up within 24–48 h, app-based or phone-text monitoring systems have demonstrated improved detection of complications and enhanced patient engagement after ambulatory surgeries without increasing readmission rates. Randomized evidence supports smartphone app home monitoring after ambulatory surgery as a safe alternative to in-person visits. Continuous vital sign monitoring is technically feasible in home-settings, and holds promise for early detection of critical deterioration. Structured and defined symptom-based escalation of care and coordinated cross-sector communication may reduce preventable emergency admissions. Safe ambulatory surgery extends well beyond the hospital. Effective post-discharge care requires standardized discharge education, stratified digital or telephone follow-up, and clear escalation protocols. Continuous vital sign monitoring represents a promising adjunct in higher-risk pathways. Integration of all post-discharge care elements within a quality improvement framework with defined key safety indicators are essential to realize the full safety potential of modern day-case surgery.
Artificial intelligence (AI) particularly machine learning (ML) are increasingly used to predict perioperative risk in patients undergoing major cancer surgery. This narrative review aims to orient perioperative clinicians to contemporary AI-based predictive modelling, critically appraise the current evidence, and examine how these tools can be integrated into clinical decision-making within a Learning Health System framework. Recent studies show promise for AI-based prediction of perioperative outcomes compared with traditional tools in selected settings. However, performance gains are variable. Many models lack explainability, external validation, calibration assessment, or evidence of real-world clinical impact. AI-based predictive models show promise for perioperative risk stratification in cancer surgery, but require external validation, interpretability, and links to actionable clinical pathways to be rigorously established. Future research should prioritise prospective evaluation, equity, workflow integration, and implementation science to translate predictive accuracy into meaningful clinical benefit.
