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Author(s): Yun-Yun K. Chen [sup.1], Mieke A. Soens [sup.1], Vesela P. Kovacheva [sup.1] Author Affiliations: (1) grid.38142.3c, 000000041936754X, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard [...], Preoperative anxiety has an incidence of 11-80% in patients undergoing surgical or interventional procedures. Understanding the role of preoperative anxiety on intraoperative anesthetic requirements and postoperative analgesic consumption would allow personalized anesthesia care. Over- or under-anesthetizing patients can lead to complications such as postoperative cognitive dysfunction in elderly patients, or procedural discomfort, respectively. Our scoping review focuses on the current evidence regarding the association between preoperative anxiety and intraoperative anesthetic and/or postoperative analgesic consumption in patients undergoing elective surgical or interventional procedures. Based on 44 studies that met the inclusion criteria, we found that preoperative anxiety has a significant positive correlation effect on intraoperative propofol and postoperative opioid consumption. The analysis of the literature is limited by the heterogeneity of preoperative anxiety tools used, study designs, data analyses, and outcomes. The use of shorter, validated preoperative anxiety assessment tools may help optimize the intraoperative anesthetic and postoperative analgesic regimen. Further research to determine the most feasible and clinically relevant preoperative anxiety tool and subsequent implementation has the potential to optimize perioperative care and improve patient outcomes.

BACKGROUND: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed. METHODS: We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk. RESULTS: Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score. CONCLUSIONS: Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes. [ABSTRACT FROM AUTHOR]

Objective Pain is a variably experienced symptom during pregnancy, and women scheduled for cesarean delivery, an increasingly common procedure, are a relatively understudied group who might be at higher pain risk. Although biopsychosocial factors are known to modulate many types of chronic pain, their contribution to late pregnancy pain has not been comprehensively studied. We aimed to identify biopsychosocial factors associated with greater pain severity and interference during the last week of pregnancy. Methods In this prospective, observational study, 662 pregnant women scheduled for cesarean delivery provided demographic and clinical information and completed validated psychological and pain assessments. Multivariable hierarchical linear regressions assessed independent associations of demographic, clinical, and psychological characteristics with pain severity and pain interference during the last week of pregnancy. Results Women in the study had a mean age of 34 years, and 73% identified as White, 11% as African American, 10% as Hispanic/Latina, and 6% as Asian. Most women (66%) were scheduled for repeat cesarean delivery. Significant independent predictors of worse pain outcomes included identifying as African American or Hispanic/Latina and having greater depression, sleep disturbance, and pain catastrophizing. Exploratory analyses showed that women scheduled for primary (versus repeat) cesarean delivery reported higher levels of anxiety and pain catastrophizing. Conclusions Independent of demographic or clinical factors, psychological factors, including depression, sleep disturbance, and pain catastrophizing, conferred a greater risk of late pregnancy pain. These findings suggest that women at higher risk of pain during late pregnancy could benefit from earlier nonpharmacological interventions that concurrently focus on psychological and pain symptoms. [ABSTRACT FROM AUTHOR]

Objective To evaluate and understand pregnant patients' perspectives on the implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to improve healthcare technologies and healthcare delivery. Materials and Methods We developed an anonymous survey and enrolled patients presenting to the labor and delivery unit at a tertiary care center September 2019–June 2020. We investigated the role and interplay of patient demographic factors, healthcare literacy, understanding of AI, comfort levels with various AI scenarios, and preferences for AI use in clinical care. Results Of the 349 parturients, 57.6% were between the ages of 25–34 years, 90.1% reported college or graduate education and 69.2% believed the benefits of AI use in clinical care outweighed the risks. Cluster analysis revealed 2 distinct groups: patients more comfortable with clinical AI use (Pro-AI) and those who preferred physician presence (AI-Cautious). Pro-AI patients had a higher degree of education, were more knowledgeable about AI use in their daily lives and saw AI use as a significant advancement in medicine. AI-Cautious patients reported a lack of human qualities and low trust in the technology as detriments to AI use. Discussion Patient trust and the preservation of the human physician-patient relationship are critical in moving forward with AI implementation in healthcare. Pregnant individuals are cautiously optimistic about AI use in their care. Conclusion Our findings provide insights into the status of AI use in perinatal care and provide a platform for driving patient-centered innovations. [ABSTRACT FROM AUTHOR]