Artificial intelligence and nonoperating room anesthesia.

PURPOSE OF REVIEW: The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. RECENT FINDINGS: AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. SUMMARY: The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

Dynamic assessment of prealbumin for nutrition support effectiveness in critically ill patients.

BACKGROUND & AIMS: Serum prealbumin is considered to be a sensitive predictor of clinical outcomes and a quality marker for nutrition support. However, its susceptibility to inflammation restricts its usage in critically ill patients according to current guidelines. We assessed the performance of the initial value of prealbumin and dynamic changes for predicting the ICU mortality and the effectiveness of nutrition support in critically ill patients. METHODS: This monocentric study included patients admitted to the ICU between 2009 and 2016, having at least one initial prealbumin value available. Prospectively recorded data were extracted from the electronic ICU charts. We used both univariable and multivariable logistic regressions to estimate the performance of prealbumin for the prediction of ICU mortality. Additionally, the association between prealbumin dynamic changes and nutrition support was assessed via a multivariable linear mixed-effects model and multivariable linear regression. Performing subgroup analysis assisted in identifying patients for whom prealbumin dynamic assessment holds specific relevance. RESULTS: We included 3136 patients with a total of 4942 prealbumin levels available. Both prealbumin measured at ICU admission (adjusted odds-ratio (aOR) 0.04, confidence interval (CI) 95% 0.01-0.23) and its change over the first week (aOR 0.02, CI 95 0.00-0.19) were negatively associated with ICU mortality. Throughout the entire ICU stay, prealbumin dynamic changes were associated with both cumulative energy (estimate: 33.2, standard error (SE) 0.001, p < 0.01) and protein intakes (1.39, SE 0.001, p < 0.01). During the first week of stay, prealbumin change was independently associated with mean energy (6.03e-04, SE 2.32e-04, p < 0.01) and protein intakes (1.97e-02, SE 5.91e-03, p < 0.01). Notably, the association between prealbumin and energy intake was strongest among older or malnourished patients, those suffering from increased inflammation and those with high disease severity. Finally, prealbumin changes were associated with a positive mean nitrogen balance at day 7 only in patients with SOFA <4 (p = 0.047). CONCLUSION: Prealbumin measured at ICU admission and its change during the first-week serve as an accurate predictor of ICU mortality. Prealbumin dynamic assessment may be a reliable tool to estimate the effectiveness of nutrition support in the ICU, especially among high-risk patients.