Intraoperative Prophylaxis with Palonosetron for Postoperative Nausea and/or Vomiting in Adults Undergoing Cardiothoracic Surgery Under General Anesthesia: A Single-Center Retrospective Study.

OBJECTIVE: This study assessed the efficacy of palonosetron, alone or with dexamethasone, in reducing postoperative nausea and/or vomiting (PONV) and its impact on hospitalization duration in patients who undergo adult cardiothoracic surgery (CTS) under general anesthesia. DESIGN: This retrospective analysis involved 540 adult patients who underwent CTS from a single-center cohort, spanning surgeries between September 2021 and March 2023. Sensitivity, logistic, and Cox regression analyses evaluated antiemetic effects, PONV risk factors, and outcomes. SETTING: At the Virginia Mason Medical Center (VMMC), Seattle, WA. PARTICIPANTS: Adults undergoing cardiothoracic surgery at VMMC during the specified period. INTERVENTIONS: Patients were categorized into the following 4 groups based on antiemetic treatment: dexamethasone, palonosetron, dexamethasone with palonosetron, and no antiemetic. MEASUREMENTS AND MAIN RESULTS: Primary outcomes encompassed PONV incidence within 96 hours postoperatively. Secondary outcomes included intensive care unit stay duration and postoperative opioid use. Palonosetron recipients showed a significantly lower PONV rate of 42% (v controls at 63%). The dexamethasone and palonosetron combined group also demonstrated a lower rate of 40%. Sensitivity analysis revealed a notably lower 0- to 12-hour PONV rate for palonosetron recipients (9% v control at 28%). Logistic regression found decreased PONV risk (palonosetron odds ratio [OR]: 0.24; dexamethasone and palonosetron OR: 0.26). Cox regression identified varying PONV hazard ratios related to female sex, PONV history, and lower body mass index. CONCLUSIONS: This single-center retrospective study underscored palonosetron's efficacy, alone or combined with dexamethasone, in managing PONV among adult patients who undergo CTS. These findings contribute to evolving antiemetic strategies in cardiothoracic surgery, potentially impacting patient outcomes and satisfaction positively.

Artificial Intelligence in Anesthetic Care: A Survey of Physician Anesthesiologists.

BACKGROUND: This study explored physician anesthesiologists' knowledge, exposure, and perceptions of artificial intelligence (AI) and their associations with attitudes and expectations regarding its use in clinical practice. The findings highlight the importance of understanding anesthesiologists' perspectives for the successful integration of AI into anesthesiology, as AI has the potential to revolutionize the field. METHODS: A cross-sectional survey of 27,056 US physician anesthesiologists was conducted to assess their knowledge, perceptions, and expectations regarding the use of AI in clinical practice. The primary outcome measured was attitude toward the use of AI in clinical practice, with scores of 4 or 5 on a 5-point Likert scale indicating positive attitudes. The anticipated impact of AI on various aspects of professional work was measured using a 3-point Likert scale. Logistic regression was used to explore the relationship between participant responses and attitudes toward the use of AI in clinical practice. RESULTS: A 2021 survey of 27,056 US physician anesthesiologists received 1086 responses (4% response rate). Most respondents were male (71%), active clinicians (93%) under 45 (34%). A majority of anesthesiologists (61%) had some knowledge of AI and 48% had a positive attitude toward using AI in clinical practice. While most respondents believed that AI can improve health care efficiency (79%), timeliness (75%), and effectiveness (69%), they are concerned that its integration in anesthesiology could lead to a decreased demand for anesthesiologists (45%) and decreased earnings (45%). Within a decade, respondents expected AI would outperform them in predicting adverse perioperative events (83%), formulating pain management plans (67%), and conducting airway exams (45%). The absence of algorithmic transparency (60%), an ambiguous environment regarding malpractice (47%), and the possibility of medical errors (47%) were cited as significant barriers to the use of AI in clinical practice. Respondents indicated that their motivation to use AI in clinical practice stemmed from its potential to enhance patient outcomes (81%), lower health care expenditures (54%), reduce bias (55%), and boost productivity (53%). Variables associated with positive attitudes toward AI use in clinical practice included male gender (odds ratio [OR], 1.7; P < .001), 20+ years of experience (OR, 1.8; P < .01), higher AI knowledge (OR, 2.3; P = .01), and greater AI openness (OR, 10.6; P < .01). Anxiety about future earnings was associated with negative attitudes toward AI use in clinical practice (OR, 0.54; P < .01). CONCLUSIONS: Understanding anesthesiologists' perspectives on AI is essential for the effective integration of AI into anesthesiology, as AI has the potential to revolutionize the field.