Background: Noninvasive respiratory support (NRS), including high-flow nasal oxygen therapy (HFNOT), noninvasive ventilation (NIV) and continuous positive airway pressure (CPAP), are routinely used in the perioperative period. Objectives: This narrative review provides an overview on the perioperative use of NRS. Preoperative, intraoperative, and postoperative respiratory support is discussed, along with potential future areas of research. Results: During induction of anesthesia, in selected patients at high risk of difficult intubation, NIV is associated with improved gas exchange and reduced risk of postoperative respiratory complications. HFNOT demonstrated an improvement in oxygenation. Evidence on the intraoperative use of NRS is limited. Compared with conventional oxygenation, HFNOT is associated with a reduced risk of hypoxemia during procedural sedation, and recent data indicate a possible role for HFNOT for intraoperative apneic oxygenation in specific surgical contexts. After extubation, “preemptive” NIV and HFNOT in unselected cohorts do not affect clinical outcome. Postoperative “curative” NIV in high-risk patients and among those exhibiting signs of respiratory failure can reduce reintubation rate, especially after abdominal surgery. Data on postoperative “curative” HFNOT are limited. Conclusions: There is increasing evidence on the perioperative use of NRS. Use of NRS should be tailored based on the patient’s specific characteristics and type of surgery, aimed at a personalized cost-effective approach.

Advanced respiratory monitoring encompasses a diverse range of mini- or noninvasive tools used to evaluate various aspects of respiratory function in patients experiencing acute respiratory failure, including those requiring extracorporeal membrane oxygenation (ECMO) support. Among these techniques, key modalities include esophageal pressure measurement (including derived pressures), lung and respiratory muscle ultrasounds, electrical impedance tomography, the monitoring of diaphragm electrical activity, and assessment of flow index. These tools play a critical role in assessing essential parameters such as lung recruitment and overdistention, lung aeration and morphology, ventilation/perfusion distribution, inspiratory effort, respiratory drive, respiratory muscle contraction, and patient–ventilator synchrony. In contrast to conventional methods, advanced respiratory monitoring offers a deeper understanding of pathological changes in lung aeration caused by underlying diseases. Moreover, it allows for meticulous tracking of responses to therapeutic interventions, aiding in the development of personalized respiratory support strategies aimed at preserving lung function and respiratory muscle integrity. The integration of advanced respiratory monitoring represents a significant advancement in the clinical management of acute respiratory failure. It serves as a cornerstone in scenarios where treatment strategies rely on tailored approaches, empowering clinicians to make informed decisions about intervention selection and adjustment. By enabling real-time assessment and modification of respiratory support, advanced monitoring not only optimizes care for patients with acute respiratory distress syndrome but also contributes to improved outcomes and enhanced patient safety.

Purpose: This paper aims to describe a straightforward, efficient, and reliable technique to simplify cannulation maneuvers during aortic branched endograft repair. Technique: The suggested approach utilizes the Penumbra Neuron Select catheter, which combines diagnostic, sizing, and support capabilities in one. This has the potential to reduce procedural time and minimize the need for serial catheter and guidewire exchanges. Conclusions: The proposed technique offers a simple yet effective tool to mitigate the risk of vessel loss and injury, and to streamline complex aortic repair procedures.

Background: Critical illness is characterized by profound and rapidly evolving metabolic derangements driven by systemic inflammation, hypercatabolism, fluid shifts, and endocrine dysregulation. These dynamic changes markedly limit the accuracy of predictive equations, increasing the risk of both underfeeding and overfeeding. Indirect Calorimetry Energy represents the gold standard for measuring energy expenditure, while bioelectrical impedance vector analysis (BIVA) provides complementary insights into hydration status, cellular integrity, and body cell mass. In palliative care, AI-supported integration of indirect calorimetry and BIVA enables goal-concordant artificial nutrition by aligning energy delivery with real-time metabolic status while minimizing symptom burden. Artificial intelligence (AI) has emerged as a promising tool to integrate these heterogeneous data streams and support adaptive nutritional strategies. Methods: We conducted a structured narrative review of the literature published between 2000 and 2025 using PubMed, Scopus, Embase, and Web of Science. Artificial intelligence was not used to perform the literature search or study selection. Instead, AI was analyzed as a clinical and technological component within the included studies and explored as a future enabling strategy. Eligible publications involved adult critically ill patients and addressed indirect calorimetry, BIVA-derived parameters, or AI-based metabolic modeling applied to nutritional support. Given the heterogeneity of study designs and outcomes, findings were synthesized qualitatively. Results: Predictive equations showed substantial inaccuracy in unstable metabolic states, with errors frequently exceeding ±20–40%. Indirect calorimetry enabled individualized assessment of energy expenditure but remained limited by intermittent availability. Serial BIVA assessments consistently identified clinically relevant alterations in hydration status, body cell mass, and phase angle, the latter being strongly associated with adverse outcomes. Studies incorporating AI demonstrated improved integration of calorimetry, BIVA, and clinical variables, allowing identification of metabolic phenotypes, anticipation of metabolic shifts, and generation of adaptive nutritional recommendations. Conclusions: This narrative review highlights the complementary roles of Indirect Calorimetry and BIVA in characterizing metabolic needs in critical illness. Artificial intelligence does not replace these tools but enhances their clinical utility by integrating multidimensional data into dynamic, patient-specific nutritional strategies. The combined AI–IC–BIVA approach represents a promising framework for metabolic precision nutrition in the ICU, warranting prospective validation.