Background/Objectives: Perioperative neurocognitive disorders (PNDs), including delirium and postoperative cognitive dysfunction, affect 10–50% of elderly surgical patients and are associated with increased morbidity and mortality, as well as substantial healthcare costs. Despite their clinical significance, the underlying mechanisms remain incompletely understood and effective interventions are limited. This narrative review synthesizes current evidence on the pathophysiology, risk factors, and management strategies for PNDs. Methods: We conducted a comprehensive literature review of peer-reviewed publications addressing PND epidemiology, mechanisms, assessment, and interventions. Key databases were searched for studies published through 2025, with emphasis on systematic reviews, meta-analyses, and landmark clinical trials. Results: PND represents a spectrum of cognitive impairments with multifactorial etiology involving neuroinflammation, neurotransmitter imbalances, and blood–brain barrier dysfunction. Advanced age, pre-existing cognitive impairment, and surgical factors constitute major risk domains. Validated assessment tools including the Confusion Assessment Method (CAM) and 4AT enable systematic detection. Multicomponent non-pharmacological interventions demonstrate 30–40% delirium reduction, while pharmacological prevention shows limited efficacy. Emerging evidence links perioperative delirium to accelerated long-term cognitive decline and increased dementia risk. Conclusions: PND represents a significant public health challenge requiring systematic attention in aging surgical populations. Evidence-based multicomponent interventions should be integrated into routine perioperative care pathways. Future research must elucidate mechanistic pathways linking acute delirium to chronic cognitive impairment and develop targeted therapies to preserve cognitive health in surgical populations.

Background: Unanticipated difficult airways remain a leading cause of anesthesia-related morbidity and mortality, with traditional bedside predictors demonstrating limited sensitivity. Point-of-Care Ultrasound (POCUS) has emerged as a non-invasive adjunct offering real-time visualization and quantitative measurement of airway anatomy. This narrative review, structured according to the Scale for the Assessment of Narrative Review Articles (SANRA), synthesizes current evidence on POCUS as an adjunct for airway evaluation. We explore the sonoanatomy of the upper airway, the utility of ultrasound in predicting difficult laryngoscopy and intubation, its critical role in emergency front-of-neck access, and the verification of endotracheal tube placement. Furthermore, we discuss the integration of Artificial Intelligence (AI) in image interpretation and the necessity of standardized training curricula. Methods: We systematically searched PubMed/MEDLINE, Scopus, and Web of Science for English-language peer-reviewed studies addressing sonographic airway assessment, including sonoanatomy, prediction of difficult laryngoscopy/intubation, guidance for emergency FONA and endotracheal tube confirmation. Results: POCUS enhances visualization of critical anatomical structures, may improve anatomical assessment and risk stratification when combined with clinical assessment, and it may provide real-time guidance during emergency procedures. Integration of AI has shown promising diagnostic performance, primarily based on surrogate outcomes. Conclusions: Airway ultrasound may represent a shift toward personalized, safer airway management. However, standardized training protocols and validation in diverse clinical settings remain essential. Future research should focus on developing evidence-based algorithms integrating POCUS into airway management guidelines.

Background: Unanticipated difficult airways remain a leading cause of anesthesia-related morbidity and mortality, with traditional bedside predictors demonstrating limited sensitivity. Point-of-Care Ultrasound (POCUS) has emerged as a non-invasive adjunct offering real-time visualization and quantitative measurement of airway anatomy. This narrative review, structured according to the Scale for the Assessment of Narrative Review Articles (SANRA), synthesizes current evidence on Point-of-Care Ultrasound (POCUS) as an adjunct for airway evaluation. We explore the sonoanatomy of the upper airway, the utility of ultrasound in predicting difficult laryngoscopy and intubation, its critical role in emergency front-of-neck access (FONA), and the verification of endotracheal tube placement. Furthermore, we discuss the integration of Artificial Intelligence (AI) in image interpretation and the necessity of standardized training curricula. Methods: We systematically searched PubMed/MEDLINE, Scopus, and Web of Science for English-language peer-reviewed studies addressing sonographic airway assessment, including sonoanatomy, prediction of difficult laryngoscopy/intubation, guidance for emergency front-of-neck access (FONA) and endotracheal tube confirmation. Results: POCUS enhances visualization of critical anatomical structures, improves predictive accuracy when combined with clinical assessment, and provides real-time guidance during emergency procedures. Integration of Artificial Intelligence shows promise for automated image interpretation. Conclusions: Airway ultrasound represents a paradigm shift toward personalized, safer airway management. However, standardized training protocols and validation in diverse clinical settings remain essential. Future research should focus on developing evidence-based algorithms integrating POCUS into airway management guidelines.

Cardiac arrhythmias complicate 20–50% of surgical procedures and contribute substantially to perioperative morbidity, mortality, and healthcare costs, with postoperative atrial fibrillation (POAF) being the most frequent form. Their genesis reflects the convergence of surgical stress, anaesthetic agents, autonomic imbalance, systemic inflammation, and electrolyte disturbances, explaining the limited efficacy of single-mechanism interventions. This narrative review synthesises contemporary evidence on pathophysiology, risk stratification, prevention, acute management, and emerging technologies, emphasising individualised, patient-tailored approaches. MEDLINE, Embase, and Cochrane CENTRAL were searched (January 2010–January 2026), prioritising randomised trials, meta-analyses, and guidelines. Contemporary risk stratification integrates clinical scores, biomarkers, and electrocardiographic parameters; machine-learning models show moderate discrimination (pooled AUC 0.84) and may enable more personalised prediction pending external validation. Evidence-based prophylaxis—beta-blockade, magnesium, selective amiodarone, and emerging anti-inflammatory strategies such as colchicine—reduces POAF in high-risk populations, while acute management is guided by haemodynamic status and individual risk. Anticoagulation follows CHA₂DS₂-VASc stratification, although optimal timing and duration remain undefined. Wearable monitoring, AI-based detection, and atrial-selective agents show clinical promise. Systematic, personalised integration of risk assessment, prophylaxis, monitoring, and management offers the clearest path to reducing arrhythmia-associated morbidity.

Background/Objectives: Perioperative neurocognitive disorders (PND), including de-lirium and postoperative cognitive dysfunction, affect 10-50% of elderly surgical patients and are associated with increased morbidity, mortality, and substantial healthcare costs. Despite their clinical significance, the underlying mechanisms remain incompletely understood and effective interventions are limited. This narrative review synthesizes current evidence on the pathophysiology, risk factors, and management strategies for PND. Methods: We conducted a comprehensive literature review of peer-reviewed publications addressing PND epidemiology, mechanisms, assessment, and interventions. Key databases were searched for studies published through 2025, with emphasis on systematic reviews, meta-analyses, and landmark clinical trials. Results: PND represents a spectrum of cognitive impairments with multifactorial etiology involving neuroin-flammation, neurotransmitter imbalances, and blood-brain barrier dysfunction. Ad-vanced age, pre-existing cognitive impairment, and surgical factors constitute major risk domains. Validated assessment tools including the Confusion Assessment Method (CAM) and 4AT enable systematic detection. Multicomponent non-pharmacological interven-tions demonstrate 30-40% delirium reduction, while pharmacological prevention shows limited efficacy. Emerging evidence links perioperative delirium to accelerated long-term cognitive decline and increased dementia risk. Conclusions: PND represents a significant public health challenge requiring systematic attention in aging surgical populations. Evidence-based multicomponent interventions should be integrated into routine peri-operative care pathways. Future research must elucidate mechanistic pathways linking acute delirium to chronic cognitive impairment and develop targeted therapies to pre-serve cognitive health in surgical populations.

Airway management remains one of the few domains of anaesthetic practice still governed by universal, dichotomous screening thresholds applied to profoundly heterogeneous populations. Conventional bedside tests—the modified Mallampati test, thyromental distance and the upper lip bite test—retain modest and inconsistent diagnostic accuracy, leaving the majority of difficult intubations unanticipated while generating false alarms that drive resource-intensive contingency plans. This narrative review reframes preoperative airway evaluation as a problem of individualised risk stratification rather than population screening. We first summarise the performance limits of classical bedside predictors and the reasons for their ceiling effect, including low target prevalence, inter-observer variability, spectrum bias and heterogeneous outcome definitions. We then examine the data streams reshaping the field: point-of-care upper airway ultrasound, whose parameters provide operator-accessible and objective anatomical information; cross-sectional and craniofacial imaging; and machine-learning models based on clinical variables, facial images and voice analysis. For each we discuss accuracy, reproducibility, validation status and barriers to adoption. We then propose a phenotype-oriented framework in which assessment intensity is matched to pre-test probability, with dedicated pathways for obesity and obstructive sleep apnoea, obstetric, head-and-neck oncological and post-radiotherapy, critically ill and emergency, and paediatric populations. We map this framework onto the 2025 European Society of Anaesthesiology and Intensive Care and Difficult Airway Society recommendations and outline the validation, calibration and equity requirements—including algorithmic bias across skin tone, sex and ethnicity—that must be met before precision airway assessment can enter routine practice.