Anesthesia and analgesia
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Anesthesia and analgesia · Nov 2024
Observational StudyA Prospective Observational Cohort Study of Language Preference and Preoperative Cognitive Screening in Older Adults: Do Language Disparities Exist in Cognitive Screening and Does the Association Between Test Results and Postoperative Delirium Differ Based on Language Preference?
A greater percentage of surgical procedures are being performed each year on patients 65 years of age or older. Concurrently, a growing proportion of patients in English-speaking countries such as the United States, United Kingdom, Australia, and Canada have a language other than English (LOE) preference. We aimed to measure whether patients with LOE underwent cognitive screening at the same rates as their English-speaking counterparts when routine screening was instituted. We also aimed to measure the association between preoperative Mini-Cog and postoperative delirium (POD) in both English-speaking and LOE patients. ⋯ We observed a disparity in the rates LOE patients were cognitively screened before surgery, despite the Mini-Cog being associated with POD in both English-speaking and LOE patients. Efforts should be made to identify barriers to cognitive screening in limited English-proficient older adults.
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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.