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Ensuring a productive clinical and research workforce requires bringing together physicians and communities to improve health, by strategic targeting of initiatives with clear and significant public health relevance. Within anesthesiology, the traditional perspective of the field's health impact has focused on providing safe and effective intraoperative care, managing critical illness, and treating acute and chronic pain. However, there are limitations to such a framework for anesthesiology's public health impact, including the transient nature of acute care episodes such as the intraoperative period and critical illness, and a historical focus on analgesia alone-rather than the complex psychosocial milieu-for pain management. Due to the often episodic nature of anesthesiologists' interactions with patients, it remains challenging for anesthesiologists to achieve their full potential for broad impact and leadership within increasingly integrated health systems. To unlock this potential, anesthesiologists should cultivate new clinical, research, and administrative roles within the health system-transcending traditional missions, seeking interdepartmental collaborations, and taking measures to elevate anesthesiologists as dynamic and trusted leaders. This special article examines 3 core themes for how anesthesiologists can enhance their impact within the health care system and pursue new collaborative health missions with nonanesthesiologist clinicians, researchers, and administrative leaders. These themes include (1) reframing of traditional anesthesiologist missions toward a broader health system-wide context; (2) leveraging departmental and institutional support for professional career development; and (3) strategically prioritizing leadership attributes to enhance system-wide anesthesiologist contributions to improving overall patient health., Competing Interests: Conflicts of Interest: See Disclosures at the end of the article., (Copyright © 2021 International Anesthesia Research Society.)

Background: The National Anesthesia Clinical Outcomes Registry collects demographic and outcome data from anesthesia cases, with the goal of improving safety and quality across the specialty. The authors present a preliminary analysis of the National Anesthesia Clinical Outcomes Registry database focusing on the rates of and associations with perioperative mortality (within 48 h of anesthesia induction)., Methods: The authors retrospectively analyzed 2,948,842 cases performed between January 1, 2010, and May 31, 2014. Cases without procedure information and vaginal deliveries were excluded. Mortality and other outcomes were reported by the anesthesia provider. Hierarchical logistic regression was performed on cases with complete information for patient age group, sex, American Society of Anesthesiologists physical status, emergency case status, time of day, and surgery type, controlling for random effects within anesthesia practices., Results: The final analysis included 2,866,141 cases and 944 deaths (crude mortality rate, 33 per 100,000). Increasing American Society of Anesthesiologists physical status, emergency case status, cases beginning between 4:00 PM and 6:59 AM, and patient age less than 1 yr or greater than or equal to 65 yr were independently associated with higher perioperative mortality. A post hoc subgroup analysis of 279,154 patients limited to 22 elective case types, post hoc models incorporating either more granular estimate of surgical risk or work relative value units, and a post hoc propensity score-matched cohort confirmed the association with time of day., Conclusions: Several factors were associated with increased perioperative mortality. A case start time after 4:00 PM was associated with an adjusted odds ratio of 1.64 (95% CI, 1.22 to 2.21) for perioperative death, which suggests a potentially modifiable target for perioperative risk reduction. Limitations of this study include nonstandardized mortality reporting and limited ability to adjust for missing data.

BackgroundAccurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression.MethodsThis was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models ("clinician-guided" and "ML hybrid"), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded.ResultsPOD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816-0.863] and for XGBoost was 0.851 [95% CI 0.827-0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734-0.793], p

BackgroundPerioperative pediatric anxiety is common and can have a negative psychological impact on children undergoing surgery and anesthesia. Studies have shown an incidence of anxiety at induction of up to 50%. Audiovisual distraction, including virtual reality (VR), is a noninvasive, nonpharmacological modality that may reduce perioperative anxiety. The goal of this study was to determine whether immersive audiovisual distraction with a VR headset during induction of general anesthesia (GA) in pediatric patients reduced preoperative anxiety.MethodsIn this randomized-controlled, parallel-group study, 71 children 5-12 years of age scheduled for elective surgery with GA were randomly allocated to a VR group or a non-VR (No VR) control group. VR group patients underwent audiovisual distraction with a VR headset during induction in the operating room, whereas the control group received no audiovisual distraction. The primary outcome was the Modified Yale Preoperative Anxiety Scale (mYPAS), which was measured at 3 time points to assess patient anxiety: in the preoperative holding area before randomization, on entering the operating room, and during induction of GA. The primary outcome was analyzed using univariate analysis and a linear mixed-effects model. Secondary outcomes included postinduction parental anxiety measured by the State-Trait Anxiety Inventory, pediatric induction compliance, and parental satisfaction.ResultsAverage patient age was 8.0 ± 2.3 years (mean ± standard deviation [SD]), and 51.4% of patients were female. Baseline variables were not substantially different between the VR group (33 patients) and the No VR group (37 patients). No patients received preoperative anxiolytic medication. Baseline mYPAS scores were not different between the groups, with scores of 28.3 (23.3-28.3) (median [interquartile range {IQR}]) in both. The change in mYPAS scores from baseline to time of induction was significantly lower in the VR group versus control group (0.0 [0.0-5.0] vs 13.3 [5.0-26.7]; P < .0001). In the mixed-effects model, the VR group had an estimated 6.0-point lower mYPAS score (95% confidence interval [CI], 0.7-11.3; P = .03) at room entry than the No VR group, and 14.5-point lower score (95% CI, 9.3-19.8; P < .0001) at induction versus control. Randomization to VR did not alter parental anxiety (0 [-2 to 2]), pediatric induction compliance (0 [0-0]), or parental satisfaction (-3 [-8 to 2]) (difference in medians [95% CI]).ConclusionsThis study demonstrates a reduction in pediatric preoperative anxiety with the use of VR. Preoperative VR may be an effective noninvasive modality for anxiolysis during induction of anesthesia in children.

BackgroundPostoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a semiautomated delirium risk stratification instrument, Age, WORLD backwards, Orientation, iLlness severity, Surgery-specific risk (AWOL-S), in 3 independent cohorts from our tertiary care hospital and describe its performance characteristics and impact on clinical care.MethodsThe risk stratification instrument was derived with elective surgical patients who were admitted at least overnight and received at least 1 postoperative delirium screen (Nursing Delirium Screening Scale [NuDESC] or Confusion Assessment Method for the Intensive Care Unit [CAM-ICU]) and preoperative cognitive screening tests (orientation to place and ability to spell WORLD backward). Using data pragmatically collected between December 7, 2016, and June 15, 2017, we derived a logistic regression model predicting probability of delirium in the first 7 postoperative hospital days. A priori predictors included age, cognitive screening, illness severity or American Society of Anesthesiologists physical status, and surgical delirium risk. We applied model odds ratios to 2 subsequent cohorts ("validation" and "sustained performance") and assessed performance using area under the receiver operator characteristic curves (AUC-ROC). A post hoc sensitivity analysis assessed performance in emergency and preadmitted patients. Finally, we retrospectively evaluated the use of benzodiazepines and anticholinergic medications in patients who screened at high risk for delirium.ResultsThe logistic regression model used to derive odds ratios for the risk prediction tool included 2091 patients. Model AUC-ROC was 0.71 (0.67-0.75), compared with 0.65 (0.58-0.72) in the validation (n = 908) and 0.75 (0.71-0.78) in the sustained performance (n = 3168) cohorts. Sensitivity was approximately 75% in the derivation and sustained performance cohorts; specificity was approximately 59%. The AUC-ROC for emergency and preadmitted patients was 0.71 (0.67-0.75; n = 1301). After AWOL-S was implemented clinically, patients at high risk for delirium (n = 3630) had 21% (3%-36%) lower relative risk of receiving an anticholinergic medication perioperatively after controlling for secular trends.ConclusionsThe AWOL-S delirium risk stratification tool has moderate accuracy for delirium prediction in a cohort of elective surgical patients, and performance is largely unchanged in emergent/preadmitted surgical patients. Using AWOL-S risk stratification as a part of a multidisciplinary delirium reduction intervention was associated with significantly lower rates of perioperative anticholinergic but not benzodiazepine, medications in those at high risk for delirium. AWOL-S offers a feasible starting point for electronic medical record-based postoperative delirium risk stratification and may serve as a useful paradigm for other institutions.

BackgroundPostoperative delirium is a common and serious problem for older adults. To better align local practices with delirium prevention consensus guidelines, we implemented a 5-component intervention followed by a quality improvement (QI) project at our institution.MethodsThis hybrid implementation-effectiveness study took place at 2 adult hospitals within a tertiary care academic health care system. We implemented a 5-component intervention: preoperative delirium risk stratification, multidisciplinary education, written memory aids, delirium prevention postanesthesia care unit (PACU) orderset, and electronic health record enhancements between December 1, 2017 and June 30, 2018. This was followed by a department-wide QI project to increase uptake of the intervention from July 1, 2018 to June 30, 2019. We tracked process outcomes during the QI period, including frequency of preoperative delirium risk screening, percentage of "high-risk" screens, and frequency of appropriate PACU orderset use. We measured practice change after the interventions using interrupted time series analysis of perioperative medication prescribing practices during baseline (December 1, 2016 to November 30, 2017), intervention (December 1, 2017 to June 30, 2018), and QI (July 1, 2018 to June 30, 2019) periods. Participants were consecutive older patients (≥65 years of age) who underwent surgery during the above timeframes and received care in the PACU, compared to a concurrent control group

INTRODUCTION:Understanding the duration of pediatric general anesthesia exposure in contemporary practice is important for identifying groups at risk for long general anesthesia exposures and designing trials examining associations between general anesthesia exposure and neurodevelopmental outcomes. METHODS:We performed a retrospective cohort analysis to estimate pediatric general anesthesia exposure duration during 2010-2015 using the National Anesthesia Clinical Outcomes Registry. RESULTS:A total of 1 548 021 pediatric general anesthetics were included. Median general anesthesia duration was 57 minutes (IQR: 28-86) with 90th percentile 145 minutes. Children aged 3 hours. High ASA physical status and care at a university hospital were associated with longer exposure times. CONCLUSION:While the vast majority (94%) of children undergoing general anesthesia are exposed for

BackgroundPostoperative delirium in the intensive care unit (ICU) is a frequent complication after cardiac or thoracic surgery and is associated with increased morbidity and mortality.MethodsIn this single-center substudy of the BAG-RECALL trial (NCT00682825), we screened patients after cardiac or thoracic surgery in the ICU twice daily for delirium using the Confusion Assessment Method for the ICU. The primary outcome was the incidence of delirium in patients who had been randomized to intraoperative Bispectral Index (BIS)-guided and end-tidal anesthetic concentration-guided depth of anesthesia protocols. As a secondary analysis, a Bayesian stochastic search variable selection strategy was used to rank a field of candidate risk factors for delirium, followed by binary logistic regression.ResultsOf 310 patients assessed, 28 of 149 (18.8%) in the BIS group and 45 of 161 (28.0%) in the end-tidal anesthetic concentration group developed postoperative delirium in the ICU (odds ratio 0.60, 95% confidence interval, 0.35-1.02, P= 0.058). Low average volatile anesthetic dose, intraoperative transfusion, ASA physical status, and European System for Cardiac Operative Risk Evaluation were identified as independent predictors of delirium.ConclusionsA larger randomized study should determine whether brain monitoring with BIS or an alternative method decreases delirium after cardiac or thoracic surgery. The association between low anesthetic concentration and delirium is a surprising finding and could reflect that patients with poor health are both more sensitive to the effects of volatile anesthetic drugs and are also more likely to develop postoperative delirium. Investigation of candidate methods to prevent delirium should be prioritized in view of the established association between postoperative delirium and adverse patient outcomes.