Anesthesia and analgesia
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Anesthesia and analgesia · Nov 2024
Meta AnalysisDoes Nociception Level Index-Guided Opioid Administration Reduce Intraoperative Opioid Consumption? A Systematic Review and Meta-Analysis.
The nociception level (NOL) index is a quantitative parameter derived from physiological signals to measure intraoperative nociception. The aim of this systematic review and meta-analysis was to evaluate if NOL monitoring reduces intraoperative opioid use compared to conventional therapy (opioid administered at clinician discretion). ⋯ This meta-analysis does not provide evidence supporting the role of NOL monitoring in reducing intraoperative opioid consumption.
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Anesthesia and analgesia · Nov 2024
Comparative StudyA Propensity-Matched Cohort Study of Intravenous Iron versus Red Cell Transfusions for Preoperative Iron-Deficiency Anemia.
While preoperative anemia is associated with adverse perioperative outcomes, the benefits of treatment with iron replacement versus red blood cell (RBC) transfusion remain uncertain. We used a national database to establish trends in preoperative iron-deficiency anemia (IDA) treatment and to test the hypothesis that treatment with preoperative iron may be superior to RBC transfusion. ⋯ In a risk-adjusted analysis, preoperative IDA treatment with IV iron compared to RBC transfusion was associated with a reduction in 30-day postoperative mortality and morbidity, a higher 30-day postoperative hemoglobin level, and reduced postoperative RBC transfusion. This evidence represents a promising opportunity to improve patient outcomes and reduce blood transfusions and their associated risk and costs.
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Anesthesia and analgesia · Nov 2024
Assessing the Utility of a Machine-Learning Model to Assist With the Assignment of the American Society of Anesthesiology Physical Status Classification in Pediatric Patients.
The American Society of Anesthesiologists Physical Status Classification System (ASA-PS) is used to classify patients' health before delivering an anesthetic. Assigning an ASA-PS Classification score to pediatric patients can be challenging due to the vast array of chronic conditions present in the pediatric population. The specific aims of this study were to (1) suggest an ASA-PS score for pediatric patients undergoing elective surgical procedures using machine-learning (ML) methods; and (2) assess the impact of presenting the suggested ASA-PS score to clinicians when making their final ASA-PS assignment. The intent was not to create a new ASA-PS score but to use ML methods to generate a suggested score, along with information on how the score was generated (ie, historical information on patient comorbidities) to assist clinicians when assigning their final ASA-PS score. ⋯ ML derivation of predicted pediatric ASA-PS scores was successful, with a strong agreement between predicted and clinician-entered ASA-PS scores. Presentation of predicted ASA-PS scores was associated with revision in final scoring for 1-in-10 pediatric patients.