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The American Society of Anesthesiologists (ASA) Physical Status classification system celebrates its 80th anniversary in 2021. Its simplicity represents its greatest strength as well as a limitation in a world of comprehensive multisystem tools. It was developed for statistical purposes and not as a surgical risk predictor. However, since it correlates well with multiple outcomes, it is widely used-appropriately or not-for risk prediction and many other purposes. It is timely to review the history and development of the system. The authors describe the controversies surrounding the ASA Physical Status classification, including the problems of interrater reliability and its limitations as a risk predictor. Last, the authors reflect on the current status and potential future of the ASA Physical Status system., (Copyright © 2021, the American Society of Anesthesiologists. All Rights Reserved.)

Health care is undergoing major transformation with a shift from fee-for-service care to fee-for-value. The advent of new care delivery and payment models is serving as a driver for value-based care. Hospitals, payors, and patients increasingly expect physicians and healthcare systems to improve outcomes and manage costs. The impact of the coronavirus disease 2019 (COVID-19) pandemic on surgical and procedural practices further highlights the urgency and need for anesthesiologists to expand their roles in perioperative care, and to impact system improvement. While there have been substantial advances in anesthesia care, perioperative complications and mortality after surgery remain a key concern. Anesthesiologists are in a unique position to impact perioperative health care through their multitude of interactions and influences on various aspects of the perioperative domain, by using the surgical experience as the first touchpoint to reengage the patient in their own health care. Among the key interventions that are being effectively instituted by anesthesiologists include proactive engagement in preoperative optimization of patients' health; personalization and standardization of care delivery by segmenting patients based upon their complexity and risk; and implementation of best practices that are data-driven and evidence-based and provide structure that allow the patient to return to their optimal state of functional, cognitive, and psychologic health. Through collaborative relationships with other perioperative stakeholders, anesthesiologists can consolidate their role as clinical leaders driving value-based care and healthcare transformation in the best interests of patients., (Copyright © 2021, the American Society of Anesthesiologists, Inc. All Rights Reserved.)

There are an increasing number of "big data" studies in anesthesia that seek to answer clinical questions by observing the care and outcomes of many patients across a variety of care settings. This Readers' Toolbox will explain how to estimate the influence of patient factors on clinical outcome, addressing bias and confounding. One approach to limit the influence of confounding is to perform a clinical trial. When such a trial is infeasible, observational studies using robust regression techniques may be able to advance knowledge. Logistic regression is used when the outcome is binary (e.g., intracranial hemorrhage: yes or no), by modeling the natural log for the odds of an outcome. Because outcomes are influenced by many factors, we commonly use multivariable logistic regression to estimate the unique influence of each factor. From this tutorial, one should acquire a clearer understanding of how to perform and assess multivariable logistic regression.

Automated medical technology is becoming an integral part of routine anesthetic practice. Automated technologies can improve patient safety, but may create new workflows with potentially surprising adverse consequences and cognitive errors that must be addressed before these technologies are adopted into clinical practice. Industries such as aviation and nuclear power have developed techniques to mitigate the unintended consequences of automation, including automation bias, skill loss, and system failures. In order to maximize the benefits of automated technology, clinicians should receive training in human-system interaction including topics such as vigilance, management of system failures, and maintaining manual skills. Medical device manufacturers now evaluate usability of equipment using the principles of human performance and should be encouraged to develop comprehensive training materials that describe possible system failures. Additional research in human-system interaction can improve the ways in which automated medical devices communicate with clinicians. These steps will ensure that medical practitioners can effectively use these new devices while being ready to assume manual control when necessary and prepare us for a future that includes automated health care.

Making good decisions in the era of Big Data requires a sophisticated approach to causality. We are acutely aware that association ≠ causation, yet untangling the two remains one of our greatest challenges. This realization has stimulated a Causal Revolution in epidemiology, and the lessons learned are highly relevant to anesthesia research. This article introduces readers to directed acyclic graphs; a cornerstone of modern causal inference techniques. These diagrams provide a robust framework to address sources of bias and discover causal effects. We use the topical question of whether anesthetic technique (total intravenous anesthesia vs. volatile) affects outcome after cancer surgery as a basis for a series of example directed acyclic graphs, which demonstrate how variables can be chosen to statistically control confounding and other sources of bias. We also illustrate how controlling for the wrong variables can introduce, rather than eliminate, bias; and how directed acyclic graphs can help us diagnose this problem.This is a rapidly evolving field, and we cover only the most basic elements. The true promise of these techniques is that it may become possible to make robust statements about causation from observational studies-without the expense and artificiality of randomized controlled trials.

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

Background: Compassionate behavior in clinicians is described as seeking to understand patients' psychosocial, physical and medical needs, timely attending to these needs, and involving patients as they desire. The goal of our study was to evaluate compassionate behavior in patient interactions, pain management, and the informed consent process of anesthesia residents in a simulated preoperative evaluation of a patient in pain scheduled for urgent surgery., Methods: Forty-nine Clinical Anesthesia residents in year 1 and 16 Clinical Anesthesia residents in year 3 from three residency programs individually obtained informed consent for anesthesia for an urgent laparotomy from a standardized patient complaining of pain. Encounters were assessed for ordering pain medication, for patient-resident interactions by using the Empathic Communication Coding System to code responses to pain and nausea cues, and for the content of the informed consent discussion., Results: Of the 65 residents, 56 (86%) ordered pain medication, at an average of 4.2 min (95% CI, 3.2 to 5.1) into the encounter; 9 (14%) did not order pain medication. Resident responses to the cues averaged between perfunctory recognition and implicit recognition (mean, 1.7 [95% CI, 1.6 to 1.9]) in the 0 (less empathic) to 6 (more empathic) system. Responses were lower for residents who did not order pain medication (mean, 1.2 [95% CI, 0.8 to 1.6]) and similar for those who ordered medication before informed consent signing (mean, 1.9 [95% CI, 1.6 to 2.1]) and after signing (mean, 1.9 [95% CI, 1.6 to 2.0]; F (2, 62) = 4.21; P = 0.019; partial η = 0.120). There were significant differences between residents who ordered pain medication before informed consent and those who did not order pain medication and between residents who ordered pain medication after informed consent signing and those who did not., Conclusions: In a simulated preoperative evaluation, anesthesia residents have variable and, at times, flawed recognition of patient cues, responsiveness to patient cues, pain management, and patient interactions.

Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated.The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them-perhaps bringing anesthesiology into an era of machine-assisted discovery.

What We Already Know About This Topic: Debriefing after an actual critical event is an established good practice in medicine, but a gap exists between principle and implementation., What This Article Tells Us That Is New: Failure to debrief after critical events is common among anesthesia trainees and likely anesthesia teams. Communication breakdowns are associated with a high rate of the failure to debrief., Background: Debriefing after an actual critical event is an established good practice in medicine, but a gap exists between principle and implementation. The authors' objective was to understand barriers to debriefing, characterize quantifiable patterns and qualitative themes, and learn potential solutions through a mixed-methods study of actual critical events experienced by anesthesia personnel., Methods: At a large academic medical center, anesthesiology residents and a small number of attending anesthesiologists were audited and/or interviewed for the occurrence and patterns of debriefing after critical events during their recent shift, including operating room crises and disruptive behavior. Patterns of the events, including event locations and event types, were quantified. A comparison was done of the proportion of cases debriefed based on whether the event contained a critical communication breakdown. Qualitative analysis, using an abductive approach, was performed on the interviews to add insight to quantitative findings., Results: During a 1-yr period, 89 critical events were identified. The overall debriefing rate was 49% (44 of 89). Nearly half of events occurred outside the operating room. Events included crisis events (e.g., cardiac arrest, difficult airway requiring an urgent surgical airway), disruptive behavior, and critical communication breakdowns. Events containing critical communication breakdowns were strongly associated with not being debriefed (64.4% [29 of 45] not debriefed in events with a communication breakdown vs. 36.4% [16 of 44] not debriefed in cases without a communication breakdown; P = 0.008). Interview responses qualitatively demonstrated that lapses in communication were associated with enduring confusion that could inhibit or shape the content of discussions between involved providers., Conclusions: Despite the value of proximal debriefing to reducing provider burnout and improving wellness and learning, failure to debrief after critical events can be common among anesthesia trainees and perhaps anesthesia teams. Modifiable interpersonal factors, such as communication breakdowns, were associated with the failure to debrief.

Background: Obtaining reliable and valid information on resident performance is critical to patient safety and training program improvement. The goals were to characterize important anesthesia resident performance gaps that are not typically evaluated, and to further validate scores from a multiscenario simulation-based assessment., Methods: Seven high-fidelity scenarios reflecting core anesthesiology skills were administered to 51 first-year residents (CA-1s) and 16 third-year residents (CA-3s) from three residency programs. Twenty trained attending anesthesiologists rated resident performances using a seven-point behaviorally anchored rating scale for five domains: (1) formulate a clear plan, (2) modify the plan under changing conditions, (3) communicate effectively, (4) identify performance improvement opportunities, and (5) recognize limits. A second rater assessed 10% of encounters. Scores and variances for each domain, each scenario, and the total were compared. Low domain ratings (1, 2) were examined in detail., Results: Interrater agreement was 0.76; reliability of the seven-scenario assessment was r = 0.70. CA-3s had a significantly higher average total score (4.9 ± 1.1 vs. 4.6 ± 1.1, P = 0.01, effect size = 0.33). CA-3s significantly outscored CA-1s for five of seven scenarios and domains 1, 2, and 3. CA-1s had a significantly higher proportion of worrisome ratings than CA-3s (chi-square = 24.1, P < 0.01, effect size = 1.50). Ninety-eight percent of residents rated the simulations more educational than an average day in the operating room., Conclusions: Sensitivity of the assessment to CA-1 versus CA-3 performance differences for most scenarios and domains supports validity. No differences, by experience level, were detected for two domains associated with reflective practice. Smaller score variances for CA-3s likely reflect a training effect; however, worrisome performance scores for both CA-1s and CA-3s suggest room for improvement.

Background: The American Board of Anesthesiology recently introduced the BASIC Examination, a component of its new staged examinations for primary certification, typically offered to residents at the end of their first year of clinical anesthesiology training. This analysis tested the hypothesis that the introduction of the BASIC Examination was associated with an acceleration of knowledge acquisition during the residency training period, as measured by increments in annual In-Training Examination scores., Methods: In-Training Examination performance was compared longitudinally among four resident cohorts (n = 6,488) before and after the introduction of the staged system using mixed-effects models that accounted for possible covariates., Results: Compared with previous cohorts in the traditional examination system, the first resident cohort in the staged system had a greater improvement in In-Training Examination scores between the first and second years of clinical anesthesiology training (by an estimated 2.0 points in scaled score on a scale of 1 to 50 [95% CI, 1.7 to 2.3]). By their second year, they had achieved a score similar to that of third-year clinical anesthesiology residents in previous cohorts. The second cohort to enter the staged system had a greater improvement of the scores between the clinical base year and the first clinical anesthesiology year, compared with the previous cohorts., Conclusions: These results support the hypothesis that the introduction of the BASIC Examination is associated with accelerated knowledge acquisition in residency training and provides evidence for the value of the new staged system in promoting desired educational outcomes of anesthesiology training.

This third installment of the history of basic airway management discusses the transitional-"progressive"-years of anesthesia from 1904 to 1960. During these 56 yr, airway management was provided primarily by basic techniques with or without the use of a face mask. Airway maneuvers were inherited from the artisanal era: head extension and mandibular advancement. The most common maneuver was head extension, also used in bronchoscopy and laryngoscopy. Basic airway management success was essential for traditional inhalation anesthesia (ether, chloroform) and for the use of the new anesthetic agents (cyclopropane, halothane) and intravenous drugs (thiopental, curare, succinylcholine). By the end of the era, the superiority of intermittent positive pressure ventilation to spontaneous ventilation in anesthesia and negative pressure ventilation in resuscitation had been demonstrated and accepted, and the implementation of endotracheal intubation as a routine technique was underway.

Background: We sought to determine whether mannequin-based simulation can reliably characterize how board-certified anesthesiologists manage simulated medical emergencies. Our primary focus was to identify gaps in performance and to establish psychometric properties of the assessment methods., Methods: A total of 263 consenting board-certified anesthesiologists participating in existing simulation-based maintenance of certification courses at one of eight simulation centers were video recorded performing simulated emergency scenarios. Each participated in two 20-min, standardized, high-fidelity simulated medical crisis scenarios, once each as primary anesthesiologist and first responder. Via a Delphi technique, an independent panel of expert anesthesiologists identified critical performance elements for each scenario. Trained, blinded anesthesiologists rated video recordings using standardized rating tools. Measures included the percentage of critical performance elements observed and holistic (one to nine ordinal scale) ratings of participant's technical and nontechnical performance. Raters also judged whether the performance was at a level expected of a board-certified anesthesiologist., Results: Rater reliability for most measures was good. In 284 simulated emergencies, participants were rated as successfully completing 81% (interquartile range, 75 to 90%) of the critical performance elements. The median rating of both technical and nontechnical holistic performance was five, distributed across the nine-point scale. Approximately one-quarter of participants received low holistic ratings (i.e., three or less). Higher-rated performances were associated with younger age but not with previous simulation experience or other individual characteristics. Calling for help was associated with better individual and team performance., Conclusions: Standardized simulation-based assessment identified performance gaps informing opportunities for improvement. If a substantial proportion of experienced anesthesiologists struggle with managing medical emergencies, continuing medical education activities should be reevaluated.

Background: Despite its widespread use, the American Society of Anesthesiologists (ASA)-Physical Status Classification System has been shown to result in inconsistent assignments among anesthesiologists. The ASA-Physical Status Classification System is also used by nonanesthesia-trained clinicians and others. In 2014, the ASA developed and approved examples to assist clinicians in determining the correct ASA-Physical Status Classification System assignment. The effect of these examples by anesthesia-trained and nonanesthesia-trained clinicians on appropriate ASA-Physical Status Classification System assignment in hypothetical cases was examined., Methods: Anesthesia-trained and nonanesthesia-trained clinicians were recruited via email to participate in a web-based questionnaire study. The questionnaire consisted of 10 hypothetical cases, for which respondents were first asked to assign ASA-Physical Status using only the ASA-Physical Status Classification System definitions and a second time using the newly ASA-approved examples., Results: With ASA-approved examples, both anesthesia-trained and nonanesthesia-trained clinicians improved in mean number of correct answers (out of possible 10) compared to ASA-Physical Status Classification System definitions alone (P < 0.001 for all). However, with examples, nonanesthesia-trained clinicians improved more compared to anesthesia-trained clinicians. With definitions only, anesthesia-trained clinicians (5.8 ± 1.6) scored higher than nonanesthesia-trained clinicians (5.4 ± 1.7; P = 0.041). With examples, anesthesia-trained (7.7 ± 1.8) and nonanesthesia-trained (8.0 ± 1.7) groups were not significantly different (P = 0.100)., Conclusions: The addition of examples to the definitions of the ASA-Physical Status Classification System increases the correct assignment of patients by anesthesia-trained and nonanesthesia-trained clinicians.

MicroRNAs (miRNAs) are small, non-protein-coding, single-stranded RNAs. They function as posttranscriptional regulators of gene expression by interacting with target mRNAs. This process prevents translation of target mRNAs into a functional protein. miRNAs are considered to be functionally involved in virtually all physiologic processes, including differentiation and proliferation, metabolism, hemostasis, apoptosis, and inflammation. Many of these functions have important implications for anesthesiology and critical care medicine. Studies indicate that miRNA expression levels can be used to predict the risk for eminent organ injury or sepsis. Pharmacologic approaches targeting miRNAs for the treatment of human diseases are currently being tested in clinical trials. The present review highlights the important biological functions of miRNAs and their usefulness as perioperative biomarkers and discusses the pharmacologic approaches that modulate miRNA functions for disease treatment. In addition, the authors discuss the pharmacologic interactions of miRNAs with currently used anesthetics and their potential to impact anesthetic toxicity and side effects.

Background: Management of cardiovascular implantable electronic devices (CIEDs), including pacemakers and implantable cardioverter defibrillators, for surgical procedures is challenging due to the increasing number of patients with CIEDs and limited availability of trained providers. At the authors' institution, a small group of anesthesiologists were trained to interrogate CIEDs, devise a management plan, and perform preoperative and postoperative programming and device testing whenever necessary., Methods: Patients undergoing surgery between October 1, 2009 and June 30, 2013 at the University of Washington Medical Center were included in a retrospective chart review to determine the number of devices actively managed by the Electrophysiology/Cardiology Service (EPCS) versus the Anesthesiology Device Service (ADS), changes in workload over time, surgical case delays due to device management, and errors and problems encountered in device programming., Results: The EPCS managed 254 CIEDs, the ADS managed 548, and 227 by neither service. Over time, the ADS providers managed an increasing percentage of devices with decreasing supervision from the EPCS. Only two CIEDs managed by the ADS required immediate assistance from the EPCS. Patients who were unstable postoperatively were referred to the EPCS. Although numerous issues in programming were encountered, primarily when restoring demand pacing after programming asynchronous pacing for surgery, no patient harm resulted from ADS or EPCS management of CIEDs., Conclusions: An ADS can provide safe CIED management for surgery, but it requires specialized provider training and strong support from the EPCS. Due to the complexity of CIED management, an ADS will likely only be feasible in high-volume settings.

Background: Concerns have long existed about potential shortages in the anesthesiologist workforce. In addition, many changes have occurred in the economy, demographics, and the healthcare sector in the last few years, which may impact the workforce. The authors documented workforce trends by region of the United States and gender, trends that may have implications for the supply and demand of anesthesiologists., Methods: The authors conducted a national survey of American Society of Anesthesiologists members (accounting for >80% of all practicing anesthesiologists in the United States) in 2007 and repeated it in 2013. The authors used logistic regression analysis and Seemingly Unrelated Regression to test across several indicators under an overarching hypothesis., Results: Anesthesiologists in Western states had markedly different patterns of practice relative to anesthesiologists in other regions in 2007 and 2013, including differences in employer type, the composition of anesthesia teams, and the time spent on monitored anesthesia care. The number and proportion of female anesthesiologists in the workforce increased between 2007 and 2013, and females differed from males in employment arrangements, compensation, and work hours., Conclusions: Regional differences remained stable during this time period although the reasons for these differences are speculative. Similarly, how and whether the gender difference in work hours and shift to younger anesthesiologists during this period will impact workforce needs is uncertain.

Background: Periodic assessment of performance by anesthesiologists is required by The Joint Commission Ongoing Professional Performance Evaluation program., Methods: The metrics used in this study were the (1) measurement of blood pressure and (2) oxygen saturation (SpO2) either before or less than 5 min after anesthesia induction. Noncompliance was defined as no measurement within this time interval. The authors assessed the frequency of noncompliance using information from 63,913 cases drawn from the anesthesia information management system. To adjust for differences in patient and procedural characteristics, 135 preoperative variables were analyzed with decision trees. The retained covariate for the blood pressure metric was patient's age and, for SpO2 metric, was American Society of Anesthesiologist's physical status, whether the patient was coming from an intensive care unit, and whether induction occurred within 5 min of the start of the scheduled workday. A Bayesian hierarchical model, designed to identify anesthesiologists as "performance outliers," after adjustment for covariates, was developed and was compared with frequentist methods., Results: The global incidences of noncompliance (with frequentist 95% CI) were 5.35% (5.17 to 5.53%) for blood pressure and 1.22% (1.14 to 1.30%) for SpO2 metrics. By using unadjusted rates and frequentist statistics, it was found that up to 43% of anesthesiologists would be deemed noncompliant for the blood pressure metric and 70% of anesthesiologists for the SpO2 metric. By using Bayesian analyses with covariate adjustment, only 2.44% (1.28 to 3.60%) and 0.00% of the anesthesiologists would be deemed "noncompliant" for blood pressure and SpO2, respectively., Conclusion: Bayesian hierarchical multivariate methodology with covariate adjustment is better suited to faculty monitoring than the nonhierarchical frequentist approach.

Background: The stepped care program Bridging Intervention in Anesthesiology (BRIA) aims at motivating and supporting surgical patients with comorbid mental disorders to engage in psychosocial mental healthcare options. This study examined the efficacy of BRIA., Methods: This randomized, parallel-group, open-label, controlled trial was conducted in the preoperative anesthesiological assessment clinics and surgical wards of a large university hospital in Germany. A total of 220 surgical patients with comorbid mental disorders were randomized by using the computer-generated lists to one of two intervention groups: BRIA psychotherapy sessions up to 3 months postoperatively (BRIA) versus no psychotherapy/computerized brief written advice (BWA) only. Primary outcome was participation in psychosocial mental healthcare options at month 6. Secondary outcome was change of self-reported general psychological distress (Global Severity Index of the Brief Symptom Inventory) between baseline and month 6., Results: At 6-month follow-up, the rate of patients who engaged in psychosocial mental healthcare options was 30% (33 of 110) in BRIA compared with 11.8% (13 of 110) in BWA (P = 0.001). Number needed to treat and relative risk reduction were 6 (95% CI, 4 to 13) and 0.21 (0.09 to 0.31), respectively. In BRIA, Global Severity Index decreased between baseline and month 6 (P < 0.001), whereas it did not change significantly in BWA (P = 0.197)., Conclusions: Among surgical patients with comorbid mental disorders, BRIA results in an increased engagement in subsequent therapy options and a decrease of general psychological distress. These data suggest that it is reasonable to integrate innovative psychotherapy programs into the context of interdisciplinary surgical care.