Cognitive Processes in Anesthesiology Decision Making

Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial

BACKGROUND

Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment.

METHODS

This single-centre, prospective, randomised clinical trial enrolled surgical patients aged ≥18 yr. Anaesthesiologists and nurse anaesthetists providing remote telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury (AKI) within 7 days. The primary outcome was area under the receiver operating characteristic curve (AUROC) for clinician predictions of mortality and AKI, comparing AUROCs between assisted and unassisted assessments.

RESULTS

We analysed 5071 patients (mean [range] age: 58 [18-100] yr; 52% female) assessed by 89 clinicians. Of these, 98 (2.2%) patients died within 30 days of surgery and 450 (11.1%) patients sustained AKI. Clinician predictions agreed with the models more strongly in the assisted vs unassisted group (weighted kappa 0.75 vs 0.62 for death, mean difference: 0.13 [95% CI 0.10-0.17]; and 0.79 vs 0.54 for AKI, mean difference: 0.25 [95% CI 0.21-0.29]). Clinical prediction of death was similar between the assisted (AUROC 0.793) and unassisted (AUROC 0.780) groups (mean difference: 0.013 [95% CI -0.070 to 0.097]; P=0.76). Prediction of AKI had an AUROC of 0.734 in the assisted group vs 0.688 in the unassisted group (difference 0.046 [95% CI -0.003 to 0.091]; P=0.06).

CONCLUSIONS

Clinician performance was not improved by machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification.

CLINICAL TRIAL REGISTRATION

NCT05042804.

Critical airway-related incidents and near misses in anaesthesia: a qualitative study of a critical incident reporting system

BACKGROUND

Many serious adverse events in anaesthesia are retrospectively rated as preventable. Anonymous reporting of near misses to a critical incident reporting system (CIRS) can identify structural weaknesses and improve quality, but incidents are often underreported.

METHODS

This prospective qualitative study aimed to identify conceptions of a CIRS and reasons for underreporting at a single Swiss centre. Anaesthesia cases were screened to identify critical airway-related incidents that qualified to be reported to the CIRS. Anaesthesia providers involved in these incidents were individually interviewed. Factors that prevented or encouraged reporting of critical incidents to the CIRS were evaluated. Interview data were analysed using the Framework method.

RESULTS

Of 3668 screened airway management procedures, 101 cases (2.8%) involved a critical incident. Saturation was reached after interviewing 21 anaesthesia providers, who had been involved in 42/101 critical incidents (41.6%). Only one incident (1.0%) had been reported to the CIRS, demonstrating significant underreporting. Interviews revealed highly variable views on the aims of the CIRS with an overall high threshold for reporting a critical incident. Factors hindering reporting of cases included concerns regarding identifiability of the reported incident and involved healthcare providers.

CONCLUSIONS

Methods to foster anonymity of reporting, such as by national rather than departmental critical incident reporting system databases, and a change in culture is required to enhance reporting of critical incidents. Institutions managing a critical incident reporting system need to ensure timely feedback to the team regarding lessons learned, consequences, and changes to standards of care owing to reported critical incidents. Consistent reporting and assessment of critical incidents is required to allow the full potential of a critical incident reporting system.

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Artificial intelligence (AI) applications have great potential to enhance perioperative care. This paper explores promising areas for AI in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation.

Cognitive biases in pediatric cardiac care

Medical practitioners are entrusted with the pivotal task of making optimal decisions in healthcare delivery. Despite rigorous training, our confidence in reasoning can fail when faced with pressures, uncertainties, urgencies, difficulties, and occasional errors. Day-to-day decisions rely on swift, intuitive cognitive processes known as heuristic or type 1 decision-making, which, while efficient in most scenarios, harbor inherent vulnerabilities leading to systematic errors. Cognitive biases receive limited explicit discussion during our training as junior doctors in the domain of paediatric cardiology. As pediatric cardiologists, we frequently confront emergencies necessitating rapid decision-making, while contending with the pressures of stress, fatigue, an earnest interest in "doing the right thing" and the impact of parental involvement. This article aims to describe cognitive biases in pediatric cardiology, highlighting their influence on therapeutic interventions for congenital heart disease. Whether future pediatric cardiologists or experienced professionals, understanding and actively combating cognitive biases are essential components of our ongoing medical education. Furthermore, it is our responsibility to thoroughly examine our own practices in our unwavering commitment to providing high-quality care.

Development and validation of the Ex-Care BR model: a multicentre initiative for identifying Brazilian surgical patients at risk of 30-day in-hospital mortality

BACKGROUND

Surgical risk stratification is crucial for enhancing perioperative assistance and allocating resources efficiently. However, existing models may not capture the complexity of surgical care in Brazil. Using data from various healthcare settings nationwide, we developed a new risk model for 30-day in-hospital mortality (the Ex-Care BR model).

METHODS

A retrospective cohort study was conducted in 10 hospitals from different geographic regions in Brazil. Data were analysed using multilevel logistic regression models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration plots. Derivation and validation cohorts were randomly assigned.

RESULTS

A total of 107,372 patients were included, and 30-day in-hospital mortality was 2.1% (n=2261). The final risk model comprised four predictors related to the patient and surgery (age, ASA physical status classification, surgical urgency, and surgical size), and the random effect related to hospitals. The model showed excellent discrimination (AUROC=0.93, 95% confidence interval [CI], 0.93-0.94), calibration, and overall performance (Brier score=0.017) in the derivation cohort (n=75,094). Similar results were observed in the validation cohort (n=32,278) (AUROC=0.93, 95% CI, 0.92-0.93).

CONCLUSIONS

The Ex-Care BR is the first model to consider regional and organisational peculiarities of the Brazilian surgical scene, in addition to patient and surgical factors. It is particularly useful for identifying high-risk surgical patients in situations demanding efficient allocation of limited resources. However, a thorough exploration of mortality variations among hospitals is essential for a comprehensive understanding of risk.

CLINICAL TRIAL REGISTRATION

NCT05796024.

[Airway management in neonates and infants : Recommendations according to the ESAIC/BJA guidelines]

Securing an airway enables the oxygenation and ventilation of the lungs and is a potentially life-saving medical procedure. Adverse and critical events are common during airway management, particularly in neonates and infants. The multifactorial reasons for this include patient-dependent, user-dependent and also external factors. The recently published joint ESAIC/BJA international guidelines on airway management in neonates and infants are summarized with a focus on the clinical application. The original publication of the guidelines focussed on naming formal recommendations based on systematically documented evidence, whereas this summary focusses particularly on the practicability of their implementation.

Identifying, Understanding, and Minimizing Unconscious Cognitive Biases in Perioperative Crisis Management: A Narrative Review

Rapid clinical decision-making behavior is often based on pattern recognition and other mental shortcuts. Although such behavior is often faster than deliberative thinking, it can also lead to errors due to unconscious cognitive biases (UCBs). UCBs may contribute to inaccurate diagnoses, hamper interpersonal communication, trigger inappropriate clinical interventions, or result in management delays. The authors review the literature on UCBs and discuss their potential impact on perioperative crisis management. Using the Scale for the Assessment of Narrative Review Articles (SANRA), publications with the most relevance to UCBs in perioperative crisis management were selected for inclusion. Of the 19 UCBs that have been most investigated in the medical literature, the authors identified 9 that were judged to be clinically relevant or most frequently occurring during perioperative crisis management. Formal didactic training on concepts of deliberative thinking has had limited success in reducing the presence of UCBs during clinical decision-making. The evolution of clinical decision support tools (CDSTs) has demonstrated efficacy in improving deliberative clinical decision-making, possibly by reducing the intrusion of maladaptive UCBs and forcing reflective thinking. Anesthesiology remains a leader in perioperative crisis simulation and CDST implementation, but spearheading innovations to reduce the adverse impact of UCBs will further improve diagnostic precision and patient safety during perioperative crisis management.

User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications

BACKGROUND

Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians.

METHODS

Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis.

RESULTS

During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5).

CONCLUSIONS

Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.

Airway management in neonates and infants: European Society of Anaesthesiology and Intensive Care and British Journal of Anaesthesia joint guidelines

Airway management is required during general anaesthesia and is essential for life-threatening conditions such as cardiopulmonary resuscitation. Evidence from recent trials indicates a high incidence of critical events during airway management, especially in neonates or infants. It is important to define the optimal techniques and strategies for airway management in these groups. In this joint European Society of Anaesthesiology and Intensive Care (ESAIC) and British Journal of Anaesthesia (BJA) guideline on airway management in neonates and infants, we present aggregated and evidence-based recommendations to assist clinicians in providing safe and effective medical care. We identified seven main areas of interest for airway management: i) preoperative assessment and preparation; ii) medications; iii) techniques and algorithms; iv) identification and treatment of difficult airways; v) confirmation of tracheal intubation; vi) tracheal extubation, and vii) human factors. Based on these areas, Population, Intervention, Comparison, Outcomes (PICO) questions were derived that guided a structured literature search. GRADE (Grading of Recommendations, Assessment, Development and Evaluation) methodology was used to formulate the recommendations based on those studies included with consideration of their methodological quality (strong '1' or weak '2' recommendation with high 'A', medium 'B' or low 'C' quality of evidence). In summary, we recommend: 1. Use medical history and physical examination to predict difficult airway management (1C). 2. Ensure adequate level of sedation or general anaesthesia during airway management (1B). 3. Administer neuromuscular blocker before tracheal intubation when spontaneous breathing is not necessary (1C). 4. Use a videolaryngoscope with an age-adapted standard blade as first choice for tracheal intubation (1B). 5. Apply apnoeic oxygenation during tracheal intubation in neonates (1B). 6. Consider a supraglottic airway for rescue oxygenation and ventilation when tracheal intubation fails (1B). 7. Limit the number of tracheal intubation attempts (1C). 8. Use a stylet to reinforce and preshape tracheal tubes when hyperangulated videolaryngoscope blades are used and when the larynx is anatomically anterior (1C). 9. Verify intubation is successful with clinical assessment and end-tidal CO2 waveform (1C). 10. Apply high-flow nasal oxygenation, continuous positive airway pressure or nasal intermittent positive pressure ventilation for postextubation respiratory support, when appropriate (1B).

Recommendations from the Brazilian Society of Anesthesiology (SBA) for difficult airway management in pediatric care

Difficult airway management in pediatrics during anesthesia represents a major challenge, requiring a careful approach, advanced technical expertise, and accurate protocols. The task force of the Brazilian Society of Anesthesiology (SBA) presents a report containing updated recommendations for the management of difficult airways in children and neonates. These recommendations have been developed based on the consensus of a panel of experts, with the objective of offering strategies to overcome challenges during airway management in pediatric patients. Grounded in evidence published in international guidelines and expert opinions, the report highlights crucial steps for the appropriate management of difficult airways in pediatrics, encompassing assessment, preparation, positioning, pre-oxygenation, minimizing trauma, and, paramountly, the maintenance of arterial oxygenation. The report also delves into additional strategies involving the use of advanced tools, such as video laryngoscopy, flexible intubating bronchoscopy, and supraglottic devices. Emphasis is placed on the simplicity of implementing the outlined recommendations, with a focus on the significance of continuous education, training through realistic simulations, and familiarity with the latest available technologies. These practices are deemed essential to ensure procedural safety and contribute to the enhancement of anesthesia outcomes in pediatrics.

Airway management in neonates and infants: European Society of Anaesthesiology and Intensive Care and British Journal of Anaesthesia joint guidelines

Airway management is required during general anaesthesia and is essential for life-threatening conditions such as cardiopulmonary resuscitation. Evidence from recent trials indicates a high incidence of critical events during airway management, especially in neonates or infants. It is important to define the optimal techniques and strategies for airway management in these groups. In this joint European Society of Anaesthesiology and Intensive Care (ESAIC) and British Journal of Anaesthesia (BJA) guideline on airway management in neonates and infants, we present aggregated and evidence-based recommendations to assist clinicians in providing safe and effective medical care. We identified seven main areas of interest for airway management: i) preoperative assessment and preparation; ii) medications; iii) techniques and algorithms; iv) identification and treatment of difficult airways; v) confirmation of tracheal intubation; vi) tracheal extubation, and vii) human factors. Based on these areas, Population, Intervention, Comparison, Outcomes (PICO) questions were derived that guided a structured literature search. GRADE (Grading of Recommendations, Assessment, Development and Evaluation) methodology was used to formulate the recommendations based on those studies included with consideration of their methodological quality (strong '1' or weak '2' recommendation with high 'A', medium 'B' or low 'C' quality of evidence). In summary, we recommend: 1. Use medical history and physical examination to predict difficult airway management (1С). 2. Ensure adequate level of sedation or general anaesthesia during airway management (1B). 3. Administer neuromuscular blocker before tracheal intubation when spontaneous breathing is not necessary (1С). 4. Use a videolaryngoscope with an age-adapted standard blade as first choice for tracheal intubation (1B). 5. Apply apnoeic oxygenation during tracheal intubation in neonates (1B). 6. Consider a supraglottic airway for rescue oxygenation and ventilation when tracheal intubation fails (1B). 7. Limit the number of tracheal intubation attempts (1C). 8. Use a stylet to reinforce and preshape tracheal tubes when hyperangulated videolaryngoscope blades are used and when the larynx is anatomically anterior (1C). 9. Verify intubation is successful with clinical assessment and end-tidal CO 2 waveform (1C). 10. Apply high-flow nasal oxygenation, continuous positive airway pressure or nasal intermittent positive pressure ventilation for postextubation respiratory support, when appropriate (1B).

The Cognitive Basis for Human Error and the Best Practices to Reduce Error

A great deal of knowledge exists about how to make health care safer than it is currently. The tools exist but all too often, they are not implemented. All anesthesia providers need to understand what safety best practices are and continue to advocate for them in their workplaces.

Anesthesia Risk Alert Program: A Proactive Safety Initiative

BACKGROUND

Analyzing adverse events data collected over a three-year period on all anesthetic cases, North American Partners in Anesthesia (NAPA), a nationwide anesthesia practice, found a correlation between certain high-risk clinical factors and a number of critical events. Seeking to reduce the incidence of critical adverse events associated with these high-risk factors, the quality team of the NAPA Anesthesia Patient Safety Institute (NAPSI) developed the Anesthesia Risk Alert (ARA) program, which guides clinicians in proactively applying targeted risk mitigation interventions in five specific clinical scenarios. NAPSI is NAPA's Patient Safety Organization (PSO).

METHODS

ARA promotes a proactive (Safety II) approach to patient safety. The protocol incorporates innovative collaboration techniques to improve clinical decision-making, along with recommendations by professional medical societies. ARA risk mitigation strategies also adapt decision tools from other industries, such as red team/blue team methodology. Following implementation training to approximately 6,000 NAPA clinicians, ongoing compliance is tracked for the program's two components: screening patients for the five high-risk clinical scenarios and performing the associated mitigation strategy when one or more risk factor is identified.

RESULTS

Since launching the ARA program in 2019, clinician compliance consistently exceeds 95%. Simultaneously, available data indicate that the incidence of selected adverse events has decreased.

CONCLUSION

ARA, developed as a process improvement initiative to reduce patient harm in several vulnerable perioperative patient populations, demonstrates how proactive safety strategies can improve clinical outcomes and create better perioperative cultures. At various sites, NAPA anesthesia clinicians reported that ARA's collaboration strategies were transformative behaviors that extended beyond the operating room. Other health care providers may customize the lessons learned from ARA with a Safety II approach.

A scoping review and behavioural analysis of factors underlying overuse of antimicrobials

Background

Overuse of antimicrobials is a challenging global issue that contributes to antimicrobial resistance. Despite widespread awareness of the problem among members of the medical community and various attempts to improve prescription practices, existing antimicrobial stewardship programmes are not always effective. In our view, this may reflect limited understanding of factors that influence prescription of antimicrobials as empirical therapy, implying a need to address the psychological mechanisms behind some of the specific behaviours involved.

Objectives

To identify factors that influence the antimicrobials prescription as empirical therapy, and to relate these factors to findings from behavioural science.

Methods

We conducted a scoping review of the literature on the factors underlying antimicrobial prescription decisions, following the protocol designed using PRISMA guidelines.

Results and conclusions

From a final sample of 90 sources, we identified ten factors important in antimicrobial prescription decisions. In the second stage of our analysis, we grouped them into five final categories: (1) nature of the decision, (2) social influences, (3) individual differences, (4) characteristics of the patient, (5) context. We analyse these categories using a behavioural science perspective.

Investigating the impact of brief training in decision-making on treatment escalation to intensive care using objective structured clinical examination-style scenarios

Background

The decision to admit patients to the intensive care unit (ICU) is complex. Structuring the decision-making process may be beneficial to patients and decision-makers alike. The aim of this study was to investigate the feasibility and impact of a brief training intervention on ICU treatment escalation decisions using the Warwick model- a structured decision-making framework for treatment escalation decisions.

Methods

Treatment escalation decisions were assessed using Objective Structured Clinical Examination-style scenarios. Participants were ICU and anaesthetic registrars with experience of making ICU admission decisions. Participants completed one scenario, followed by training with the decision-making framework and subsequently a second scenario. Decision-making data was collected using checklists, note entries and post-scenario questionnaires.

Results

Twelve participants were enrolled. Brief decision-making training was successfully delivered during the normal ICU working day. Following training participants demonstrated greater evidence of balancing the burdens and benefits of treatment escalation. On visual analogue scales of 0-10, participants felt better trained to make treatment escalation decisions (4.9 vs 6.8, p = 0.017) and felt their decision-making was more structured (4.7 vs 8.1, p = 0.017).Overall, participants provided positive feedback and reported feeling more prepared for the task of making treatment escalation decisions.

Conclusion

Our findings suggest that a brief training intervention is a feasible way to improve the decision-making process by improving decision-making structure, reasoning and documentation. Training was implemented successfully, acceptable to participants and participants were able to apply their learning. Further studies of regional and national cohorts are needed to determine if training benefit is sustained and generalisable.

The Ecological-Dynamics Framework for Medical Skills

Physicians are required to move and manipulate equipment to achieve motor tasks such as surgical operations, endotracheal intubations, and intravenous cannulation. Understanding how movements are generated allows for the analysis of performance, skill development, and methods of teaching. Ecological-Dynamics (ECD) is a theoretical framework successfully utilized in sports to explain goal-directed actions and guide coaching and performance analysis via a Constraint-Led Approach (CLA). Its principles have been adopted by other domains including learning music and mathematics. Healthcare is yet to utilize ECD for analyzing and teaching practical skills. This article presents ECD theory and considers it as the approach to understand skilled behavior and developing training in medical skills.

Postpartum hemorrhage: The role of simulation

Postpartum hemorrhage (PPH) persists as a leading cause of maternal death worldwide, and in the United States, most maternal deaths due to hemorrhage are deemed preventable. While essential preparations for hemorrhage include protocols and checklists, implementation science has revealed that it is not enough to merely introduce these tools into units. Simulation affords safe opportunities for practice and produces reliable behavior change, and it does not always need to be highly expensive and resource consuming. We review how simulation can be applied to address a unit's vulnerabilities in identifying, managing, and resolving PPH, as well as considerations for crafting a comprehensive simulation program for your unit.

Remimazolam Requires Less Vasopressor Support during Induction and Maintenance of General Anesthesia in Patients with Severe Aortic Stenosis Undergoing Transcatheter Aortic Valve Replacement: A Retrospective Analysis from a Single Center

Introduction

We compared the hemodynamics during general anesthesia with remimazolam and conventional anesthetics in patients with severe aortic stenosis (AS).

Methods

This was a retrospective single-center analysis. We reviewed the records of 42 patients who underwent transcatheter aortic valve implantation with a transfemoral artery approach under general anesthesia from January to December 2020. Patients were divided into three groups based on the general anesthetic used for (induction/maintenance) remimazolam/remimazolam (Group R/R), propofol/sevoflurane (Group P/S), and midazolam/propofol (Group M/P). Vasopressor use (ephedrine, phenylephrine, and noradrenaline) was compared among the groups.

Results

The number of patients in each group was 15 (Group R/R), 13 (Group P/S), and 14 (Group M/P), with no significant difference in background characteristics and intraoperative vital signs. For anesthesia induction, doses of ephedrine and phenylephrine used were significantly lower in Group R/R (ephedrine [mg]: Group R/R 2 [0-4] vs. Group P/S 8 [8-12], P < 0.001, Group R/R vs. Group M/P 5 [0-15], P = 0.39; phenylephrine (mg): Group R/R 0 [0-0.08] vs. Group P/S 0.15 [0.10-0.20], P = 0.03, Group M/P 0.21 [0.04-0.40], P = 0.08). For anesthesia maintenance, the noradrenaline dose used was low in the Group R/R (noradrenaline [μg/kg/min]: Group R/R 0.019 [0.015-0.039], Group P/S 0.042 [0.035-0.045], P = 0.02, Group M/P 0.048 [0.040-0.059], P < 0.01).

Conclusion

In patients with severe AS, induction and maintenance of anesthesia with remimazolam resulted in less overall vasopressor use than conventional general anesthetics.

An ethnographic study on the impact of a novel telemedicine-based support system in the operating room

OBJECTIVE

The Anesthesiology Control Tower (ACT) for operating rooms (ORs) remotely assesses the progress of surgeries and provides real-time perioperative risk alerts, communicating risk mitigation recommendations to bedside clinicians. We aim to identify and map ACT-OR nonroutine events (NREs)-risk-inducing or risk-mitigating workflow deviations-and ascertain ACT's impact on clinical workflow and patient safety.

MATERIALS AND METHODS

We used ethnographic methods including shadowing ACT and OR clinicians during 83 surgeries, artifact collection, chart reviews for decision alerts sent to the OR, and 10 clinician interviews. We used hybrid thematic analysis informed by a human-factors systems-oriented approach to assess ACT's role and impact on safety, conducting content analysis to assess NREs.

RESULTS

Across 83 cases, 469 risk alerts were triggered, and the ACT sent 280 care recommendations to the OR. 135 NREs were observed. Critical factors facilitating ACT's role in supporting patient safety included providing backup support and offering a fresh-eye perspective on OR decisions. Factors impeding ACT included message timing and ACT and OR clinician cognitive lapses. Suggestions for improvement included tailoring ACT message content (structure, timing, presentation) and incorporating predictive analytics for advanced planning.

DISCUSSION

ACT served as a safety net with remote surveillance features and as a learning healthcare system with feedback/auditing features. Supporting strategies include adaptive coordination and harnessing clinician/patient support to improve ACT's sustainability. Study insights inform future intraoperative telemedicine design considerations to mitigate safety risks.

CONCLUSION

Incorporating similar remote technology enhancement into routine perioperative care could markedly improve safety and quality for millions of surgical patients.

Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study

Background: More than four million people die each year in the month following surgery, and many more experience complications such as acute kidney injury. Some of these outcomes may be prevented through early identification of at-risk patients and through intraoperative risk mitigation. Telemedicine has revolutionized the way at-risk patients are identified in critical care, but intraoperative telemedicine services are not widely used in anesthesiology. Clinicians in telemedicine settings may assist with risk stratification and brainstorm risk mitigation strategies while clinicians in the operating room are busy performing other patient care tasks. Machine learning tools may help clinicians in telemedicine settings leverage the abundant electronic health data available in the perioperative period. The primary hypothesis for this study is that anesthesiology clinicians can predict postoperative complications more accurately with machine learning assistance than without machine learning assistance. Methods: This investigation is a sub-study nested within the TECTONICS randomized clinical trial (NCT03923699). As part of TECTONICS, study team members who are anesthesiology clinicians working in a telemedicine setting are currently reviewing ongoing surgical cases and documenting how likely they feel the patient is to experience 30-day in-hospital death or acute kidney injury. For patients who are included in this sub-study, these case reviews will be randomized to be performed with access to a display showing machine learning predictions for the postoperative complications or without access to the display. The accuracy of the predictions will be compared across these two groups. Conclusion: Successful completion of this study will help define the role of machine learning not only for intraoperative telemedicine, but for other risk assessment tasks before, during, and after surgery. Registration: ORACLE is registered on ClinicalTrials.gov: NCT05042804; registered September 13, 2021.

Decision Support Needs for Patients with Severe Symptomatic Aortic Stenosis

Social workers in healthcare settings often support patient decision-making processes for complex medical decisions. The objective of this study was to examine decision support needs for patients considering aortic valve replacement (AVR) for aortic stenosis. Seventeen qualitative interviews were conducted to explore treatment decision experiences of patients who accepted AVR. Analysis was conducted using a mixed inductive-deductive approach. Fear was a prevalent response for most participants in the face of AVR. Two general paths of decision making emerged: an "active" information seeking approach, or a "passive" simplicity seeking approach. Patients with unique clinical presentations felt alienated by the decision-making process. Acknowledging fear while understanding different decision-making styles provide opportunities for social workers and other members of multidisciplinary teams to support complex patient decisions.

Complications associated with removal of airway devices under deep anesthesia in children: an analysis of the Wake Up Safe database

Background

Previous studies examining removal of endotracheal tubes and supraglottic devices under deep anesthesia were underpowered to identify rare complications. This study sought to report all adverse events associated with this practice found in a large national database of pediatric anesthesia adverse events.

Methods

An extract of an adverse events database created by the Wake Up Safe database, a multi-institutional pediatric anesthesia quality improvement initiative, was performed for this study. It was screened to identify anesthetics with variables indicating removal of airway devices under deep anesthesia. Three anesthesiologists screened the data to identify events where this practice possibly contributed to the event. Event data was extracted and collated.

Results

One hundred two events met screening criteria and 66 met inclusion criteria. Two cardiac etiology events were identified, one of which resulted in the patient’s demise. The remaining 97% of events were respiratory in nature (64 events), including airway obstruction, laryngospasm, bronchospasm and aspiration. Some respiratory events consisted of multiple distinct events in series. Nineteen respiratory events resulted in cardiac arrest (29.7%) of which 15 (78.9%) were deemed preventable by local anesthesiologists performing independent review. Respiratory events resulted in intensive care unit admission (37.5%), prolonged intubation and temporary neurologic injury but no permanent harm. Provider and patient factors were root causes in most events. Upon investigation, areas for improvement identified included improving patient selection, ensuring monitoring, availability of intravenous access, and access to emergency drugs and equipment until emergence.

Conclusions

Serious adverse events have been associated with this practice, but no respiratory events were associated with long-term harm.

Long Non-coding RNA HOTAIR in Central Nervous System Disorders: New Insights in Pathogenesis, Diagnosis, and Therapeutic Potential

Central nervous system (CNS) disorders, such as ischemic stroke, neurodegenerative diseases, multiple sclerosis, traumatic brain injury, and corresponding neuropathological changes, often lead to death or long-term disability. Long non-coding RNA (lncRNA) is a class of non-coding RNA with a transcription length over 200 nt and transcriptional regulation. lncRNA is extensively involved in physiological and pathological processes through epigenetic, transcription, and post-transcriptional regulation. Further, dysregulated lncRNA is closely related to the occurrence and development of human diseases, including CNS disorders. HOX Transcript antisense RNA (HOTAIR) is the first discovered lncRNA with trans-transcriptional regulation. Recent studies have shown that HOTAIR may participate in the regulation of the occurrence and development of CNS disorders. In addition, HOTAIR has the potential to become a new biomarker for the diagnosis and prognosis assessment of CNS disorders and even provide a new therapeutic target for CNS disorders. Here, we reviewed the research results of HOTAIR in CNS disorders to provide new insights into the pathogenesis, diagnostic value, and therapeutic target potential of HOTAIR in human CNS disorders.

“Could You Work in My Team?”: Exploring How Professional Clinical Role Expectations Influence Decision-Making of Assessors During Exit-Level Medical School OSCEs

Decision-making in clinical assessment, such as exit-level medical school Objective Structured Clinical Examinations (OSCEs), is complex. This study utilized an empirical phenomenological qualitative approach with thematic analysis to explore OSCE assessors' perceptions of the concept of a “prototypical intern” expressed during focus group discussions. Topics discussed included the concept of a prototypical intern, qualities to be assessed, and approaches to clinical assessment decision-making. The thematic analysis was then applied to a theoretical framework (Cultural Historical Activity Theory—CHAT) that explored the complexity of making assessment decisions amidst potentially contradicting pressures from academic and clinical perspectives. Ten Australasian medical schools were involved with 15 experienced and five less experienced assessors participating. Thematic analysis of the data revealed four major themes in relation to how the prototypical intern concept influences clinical assessors' judgements: (a) Suitability of marking rubric based on assessor characteristics and expectations; (b) Competence as final year student vs. performance as a prototypical intern; (c) Safety, trustworthiness and reliability as constructs requiring assessment and (d) Contradictions in decision making process due to assessor differences. These themes mapped well within the interaction between two proposed activity systems in the CHAT model: academic and clinical. More clinically engaged and more experienced assessors tend to fall back on a heuristic, mental construct of a “prototypical intern,” to calibrate judgements, particularly, in difficult situations. Further research is needed to explore whether consensus on desirable intern qualities and their inclusion into OSCE marksheets decreases the cognitive load and increases the validity of assessor decision making.

Dual-processing theory helps to explain delay in diagnosis of Stanford type A aortic dissection

A woman in her 70s presented with chest pain, which was initially thought to be an acute coronary syndrome but subsequently felt to be pericarditis. Chest radiography and echocardiography demonstrated striking cardiomegaly and marked biatrial dilatation, likely secondary to undiagnosed restrictive cardiomyopathy. The patient remained well on the ward for some days with only mild discomfort and stable haemodynamics. CT of the thorax went on to unexpectedly demonstrate a Stanford type A aortic dissection. The patient was promptly transferred for emergent surgery but sadly died intraoperatively.Delayed or missed diagnosis of acute aortic dissection (AAD) is common. The dual-processing theory (DPT) of human judgement can be applied to medical decision making and used to explain this potential for diagnostic error in AAD diagnosis. A greater awareness of DPT and the role of heuristics and biases in medical decision making may help to reduce medical diagnostic error.

Chronic stress indicated by hair cortisol concentration in anaesthesiologists and its relationship to work experience and emotional intelligence: A cross-sectional biomarker and survey study

BACKGROUND

Anaesthesia is a stressful medical specialty. The reaction to stress is constituted by behavioural, psychological and physiological components. Chronic physiological stress can have negative consequences for health.

OBJECTIVES

First, we hypothesised that chronic physiological stress is higher for both beginning and late-career consultant anaesthesiologists. Second, we hypothesised that individuals high in emotional intelligence endure lower physiological stress.

DESIGN

Cross-sectional biomarker and survey study.

SETTING

Participants were recruited during the May 2019 annual meeting of the Dutch Anaesthesia Society.

PARTICIPANTS

Of the 1348 colleagues who attended the meeting, 184 (70 male/114 female) participated in the study. Of the study participants, 123 (67%) were consultant anaesthesiologists (52 male/71 female) and 61 (33%) were resident anaesthesiologists (18 male/43 female). Exclusion criteria were endocrine disorders and not having enough hair. Also, experience of a recent major life event led to exclusion from analysis of our hypotheses.

MAIN OUTCOME MEASURES

Chronic physiological stress was measured by hair cortisol concentration. Emotional intelligence was assessed using a validated Dutch version of the Trait Emotional Intelligence Questionnaire. As secondary measures, psychological sources of stress were assessed using validated Dutch versions of the home-work interference (SWING) and the effort-reward imbalance questionnaires.

RESULTS

In support of Hypothesis 1, hair cortisol concentration was highest among early and late-career consultant anaesthesiologists (quadratic effect: b = 45.5, SE = 16.1, t = 2.8, P = 0.006, R2 = 0.14). This nonlinear pattern was not mirrored by self-reported sources of psychological stress. Our results did not support Hypothesis 2; we found no evidence for a relationship between emotional intelligence and physiological stress.

CONCLUSION

In the early and later phases of an anaesthesiologist's career, physiological chronic stress is higher than in the middle of the career. However, this physiological response could not be explained from known sources of psychological stress. We discuss these findings against the background of key differences between physiological and psychological stress.

Prediction of hemodynamic tolerance of intermittent hemodialysis in critically ill patients: a cohort study

The evaluation and management of fluid balance are key challenges when caring for critically ill patients requiring renal replacement therapy. The aim of this study was to assess the ability of clinical judgment and other variables to predict the occurrence of hypotension during intermittent hemodialysis (IHD) in critically ill patients. This was a prospective, observational, single-center study involving critically ill patients undergoing IHD. The clinical judgment of hypervolemia was determined by the managing nephrologists and critical care physicians in charge of the patients on the basis of the clinical data used to calculate the ultrafiltration volume and rate for each dialysis treatment. Seventy-nine (31.9%) patients presented with hypotension during IHD. Patients were perceived as being hypervolemic in 109 (43.9%) of the cases by nephrologists and in 107 (43.1%) by intensivists. The agreement between nephrologists and intensivists was weak (kappa = 0.561). Receiver operating characteristic curve analysis yielded an AUC of 0.81 (95% CI 0.75 to 0.84; P < 0.0001), and a cutoff value of 70 mm for the vascular pedicle width (VPW) had the highest accuracy for the prediction of the absence of hypotension. The clinical judgment of hypervolemia did not predict hypotension during IHD. The high predictive ability of the VPW may assist clinicians with critical thinking.

Exploring patient perspectives on telemedicine monitoring within the operating room

BACKGROUND

Clinical decision support systems and telemedicine for remote monitoring can together support clinicians' intraoperative decision-making and management of surgical patients' care. However, there has been limited investigation on patient perspectives about advanced health information technology use in intraoperative settings, especially an electronic OR (eOR) for remote monitoring and management of surgical patients.

PURPOSE

Our study objectives were: (1) to identify participant-rated items contributing to patient attitudes, beliefs, and level of comfort with eOR monitoring; and (2) to highlight barriers and facilitators to eOR use.

METHODS

We surveyed 324 individuals representing surgical patients across the United States using Amazon Mechanical Turk, an online platform supporting internet-based work. The structured survey questions examined the level of agreement and comfort with eOR for remote patient monitoring. We calculated descriptive statistics for demographic variables and performed a Wilcoxon matched-pairs signed-rank test to assess whether participants were more comfortable with familiar clinicians from local hospitals or health systems monitoring their health and safety status during surgery than clinicians from hospitals or health systems in other regions or countries. We also analyzed open-ended survey responses using a thematic approach informed by an eight-dimensional socio-technical model.

RESULTS

Participants' average age was 34.07 (SD = 10.11). Most were white (80.9%), male (57.1%), and had a high school degree or more (88.3%). Participants reported a higher level of comfort with clinicians they knew monitoring their health and safety than clinicians they did not know, even within the same healthcare system (z = -4.012, p < .001). They reported significantly higher comfort levels with clinicians within the same hospital or health system in the United States than those in a different country (z = -10.230, p < .001). Facilitators and barriers to eOR remote monitoring were prevalent across four socio-technical dimensions: 1) organizational policies, procedures, environment, and culture; 2) people; 3) workflow and communication; and 4) hardware and software. Facilitators to eOR use included perceptions of improved patient safety through a safeguard system and perceptions of streamlined care. Barriers included fears of incorrect eOR patient assessments, decision-making conflicts between care teams, and technological malfunctions.

CONCLUSIONS

Participants expressed significant support for intraoperative telemedicine use and greater comfort with local telemedicine systems instead of long-distance telemedicine systems. Reservations centered on organizational policies, procedures, environment, culture; people; workflow and communication; and hardware and software. To improve the buy-in and acceptability of remote monitoring by an eOR team, we offer a few evidence-based guidelines applicable to telemedicine use within the context of OR workflow. Guidelines include backup plans for technical challenges, rigid care, and privacy standards, and patient education to increase understanding of telemedicine's potential to improve patient care.

Error traps in pediatric difficult airway management

Difficult airway management in children is associated with significant morbidity. This narrative review on error traps in airway management aims to highlight the common pitfalls and proposes solutions to optimize best practices for pediatric difficult airway management. We have categorized common errors of pediatric difficult airway management into three main error traps: preparation, performance, and proficiency, and present potential strategies to improve patient safety and successful tracheal intubation in infants and children with difficult airways.

Decision Making in Obstetric Anesthesia

Obstetric anesthesiologists provide care under unique conditions, where frequently unscheduled cases demand flexibility in thinking and acting. And although most obstetric patients may be healthy, they can quickly deteriorate, necessitating rapid team diagnostic and treatment interventions. Examining decision making is a critical step in improving care to these patients. This article reviews evidence-based models of decision making both with individuals and with teams, and presents strategies to improve decision making under any circumstance.

The impact of the incorporation of a feasible postoperative mortality model at the Post-Anaesthestic Care Unit (PACU) on postoperative clinical deterioration: A pragmatic trial with 5,353 patients

Background

Practical use of risk predictive tools and the assessment of their impact on outcome reduction is still a challenge. This pragmatic study of quality improvement (QI) describes the preoperative adoption of a customised postoperative death probability model (SAMPE model) and the evaluation of the impact of a Postoperative Anaesthetic Care Unit (PACU) pathway on the clinical deterioration of high-risk surgical patients.

Methods

A prospective cohort of 2,533 surgical patients compared with 2,820 historical controls after the adoption of a quality improvement (QI) intervention. We carried out quick postoperative high-risk pathways at PACU when the probability of postoperative death exceeded 5%. As outcome measures, we used the number of rapid response team (RRT) calls within 7 and 30 postoperative days, in-hospital mortality, and non-planned Intensive Care Unit (ICU) admission.

Results

Not only did the QI succeed in the implementation of a customised risk stratification model, but it also diminished the postoperative deterioration evaluated by RRT calls on very high-risk patients within 30 postoperative days (from 23% before to 14% after the intervention, p = 0.05). We achieved no survival benefits or reduction of non-planned ICU. The small group of high-risk patients (13% of the total) accounted for the highest proportion of RRT calls and postoperative death.

Conclusion

Employing a risk predictive tool to guide immediate postoperative care may influence postoperative deterioration. It encouraged the design of pragmatic trials focused on feasible, low-technology, and long-term interventions that can be adapted to diverse health systems, especially those that demand more accurate decision making and ask for full engagement in the control of postoperative morbi-mortality.

Human errors and their prevention in healthcare

Human errors form a significant portion of preventable mishaps in healthcare. Even the most competent clinicians are not immune to it. Errors in the perioperative period can often have grave consequences, and hence, perioperative clinicians need to be aware of the impact of human errors and how to minimize them. Errors are broadly classified into two kinds: latent and active errors. While active errors need to be addressed at the individual level, latent errors indicate organizational inadequacies. This review describes common errors in perioperative settings, the impact of such errors on healthcare, and preventive strategies to minimize such errors in daily patient care.

Predicting Future Care Requirements Using Machine Learning for Pediatric Intensive and Routine Care Inpatients

Supplemental Digital Content is available in the text.

OBJECTIVES:

Develop and compare separate prediction models for ICU and non-ICU care for hospitalized children in four future time periods (6–12, 12–18, 18–24, and 24–30 hr) and assess these models in an independent cohort and simulated children’s hospital.

DESIGN:

Predictive modeling used cohorts from the Health Facts database (Cerner Corporation, Kansas City, MO).

SETTING:

Children hospitalized in ICUs.

PATIENTS:

Children with greater than or equal to one ICU admission (n = 20,014) and randomly selected routine care children without ICU admission (n = 20,130) from 2009 to 2016 were used for model development and validation. An independent 2017–2018 cohort consisted of 80,089 children.

INTERVENTIONS:

None.

MEASUREMENT AND MAIN RESULTS:

Initially, we undersampled non-ICU patients for development and comparison of the models. We randomly assigned 64% of patients for training, 8% for validation, and 28% for testing in both clinical groups. Two additional validation cohorts were tested: a simulated children’s hospitals and the 2017–2018 cohort. The main outcome was ICU care or non-ICU care in four future time periods based on physiology, therapy, and care intensity. Four independent, sequential, and fully connected neural networks were calibrated to risk of ICU care at each time period. Performance for all models in the test sample were comparable including sensitivity greater than or equal to 0.727, specificity greater than or equal to 0.885, accuracy greater than 0.850, area under the receiver operating characteristic curves greater than or equal to 0.917, and all had excellent calibration (all R²s > 0.98). Model performance in the 2017–2018 cohort was sensitivity greater than or equal to 0.545, specificity greater than or equal to 0.972, accuracy greater than or equal to 0.921, area under the receiver operating characteristic curves greater than or equal to 0.946, and R²s greater than or equal to 0.979. Performance metrics were comparable for the simulated children’s hospital and for hospitals stratified by teaching status, bed numbers, and geographic location.

CONCLUSIONS:

Machine learning models using physiology, therapy, and care intensity predicting future care needs had promising performance metrics. Notably, performance metrics were similar as the prediction time periods increased from 6–12 hours to 24–30 hours.

Racial/Ethnic Inequities in Pregnancy-Related Morbidity and Mortality

Racism in America has deep roots that impact maternal health, particularly through pervasive inequities among Black women as compared with White, although other racial and ethnic groups also suffer. Health care providers caring for pregnant women are optimally positioned to maintain vigilance for these disparities in maternal care, and to intervene with their diverse skillsets and knowledge. By increasing awareness of how structural racism drives inequities in health, these providers can encourage hospitals and practices to develop and implement national bundles for patient safety, and use bias training and team-based training practices aimed at improving care for racially diverse mothers.

Potential Renoprotective Strategies in Adult Cardiac Surgery: A Survey of Society of Cardiovascular Anesthesiologists Members to Explore the Rationale and Beliefs Driving Current Clinical Decision-Making

OBJECTIVES

The authors sought to (1) characterize the rationale underpinning anesthesiologists' use of various perioperative strategies hypothesized to affect renal function in adult patients undergoing cardiac surgery, (2) characterize existing belief about the quality of evidence addressing the renal impact of these strategies, and (3) identify potentially renoprotective strategies for which anesthesiologists would most value a detailed, evidence-based review.

DESIGN

Survey of perioperative practice in adult patients undergoing cardiac surgery.

SETTING

Online survey.

PARTICIPANTS

Members of the Society of Cardiovascular Anesthesiologists (SCA).

INTERVENTIONS

None.

MEASUREMENTS & MAIN RESULTS

The survey was distributed to more than 2,000 SCA members and completed in whole or in part by 202 respondents. Selection of target intraoperative blood pressure (and relative hypotension avoidance) was the strategy most frequently reported to reflect belief about its potential renal effect (79%; 95% CI: 72-85). Most respondents believed the evidence supporting an effect on renal injury of intraoperative target blood pressure during cardiac surgery was of high or moderate quality. Other factors, including a specific nonrenal rationale, surgeon preference, department- or institution-level decisions, tradition, or habit, also frequently were reported to affect decision making across queried strategies. Potential renoprotective strategies most frequently requested for inclusion in a subsequent detailed, evidence-based review were intraoperative target blood pressure and choice of vasopressor agent to achieve target pressure.

CONCLUSIONS

A large number of perioperative strategies are believed to variably affect renal injury in adult patients undergoing cardiac surgery, with wide variation in perceived quality of evidence for a renal effect of these strategies.

Racial Differences in Pregnancy-Related Morbidity and Mortality

Racism in the United States has deep roots that affect maternal health, particularly through pervasive inequalities among black women compared with white. Anesthesiologists are optimally positioned to maintain vigilance for these disparities in maternal care, and to intervene with their unique acute critical care skills and knowledge. As leaders in patient safety, anesthesiologists should drive hospitals and practices to develop and implement national bundles for patient safety, as well as using team-based training practices designed to improve hospitals that care for racially diverse mothers.

The Haemostasis Traffic Light, a user-centred coagulation management tool for acute bleeding situations: a simulation-based randomised dual-centre trial

The Haemostasis Traffic Light is a cognitive aid with a user-centred design to enhance and simplify situation awareness and decision-making during peri-operative bleeding. Its structure helps to prioritise therapeutic interventions according to the pathophysiology and the severity of the bleeding. This investigator-initiated, randomised, prospective, international, dual-centre study aimed to validate the Haemostasis Traffic Light by adapting it to the local coagulation protocols of two university hospitals. Between 9 January and 12 May 2020, we recruited 84 participants at the University Hospital Zurich, Switzerland, and the Italian Hospital of Buenos Aires, Argentina. Each centre included 21 resident and 21 staff anaesthetists. Participants were randomly allocated to either the text-based algorithm or the Haemostasis Traffic Light. All participants managed six bleeding scenarios using the same algorithm. In simulated bleeding scenarios, the design of the Haemostasis Traffic Light algorithm enabled more correctly solved cases, OR (95%CI) 7.23 (3.82-13.68), p < 0.001, and faster therapeutic decisions, HR (95%CI) 1.97 (1.18-3.29, p = 0.010). In addition, the tool improved therapeutic confidence, OR (95%CI) 4.31 (1.67-11.11, p = 0.003), and reduced perceived work-load coefficient (95%CI) -6.1 (-10.98 to -1.22), p = 0.020). This study provides empirical evidence for the importance of user-centred design in the development of haemostatic management protocols.

Factors Influencing Physicians' Clinical Decision-making at Upazila Health Complexes in Bangladesh

Introduction

Selecting the most appropriate treatment for each patient is the key activity in patient-physician encounters and providing healthcare services. Achieving desirable clinical goals mostly depends on making the right decision at the right time in any healthcare setting. But little is known about physicians' clinical decision-making in the primary care setting in Bangladesh. Therefore, this study explored the factors that influence decisions about prescribing medications, ordering pathologic tests, counseling patients, average length of patient visits in a consultation session, and referral of patients to other physicians or hospitals by physicians at Upazila Health Complexes (UHCs) in the country. It also explored the structure of physicians' social networks and their association with the decision-making process.

Methods

This was a cross-sectional descriptive study that used primary data collected from 85 physicians. The respondents, who work at UHCs in the Rajshahi Division, were selected purposively. The collected data were analyzed with descriptive statistics including frequency, percentage, one-way analysis of variance, and linear regression to understand relationships among the variables.

Results

The results of the study reveal that multiple factors influence physicians' decisions about prescribing medications, ordering pathologic tests, length of visits, counseling patients, and referring patients to other physicians or hospitals at the UHCs. Most physicians prescribe drugs to their patients, keeping in mind their purchasing capacity. Risk of violence by patients' relatives and better management are the two key factors that influence physicians' referral decisions. The physicians' professional and personal social networks also play an influential role in the decision-making process. It was found that physicians dedicate on average 16.17 minutes to a patient in a consultation session. The length of visits is influenced by various factors including the distance between the physicians' residence and their workplace, their level of education, and the number of colleagues with whom they have regular contact and from whom they can seek help.

Conclusion

The results of the study have yielded some novel insights about the complexity of physicians' everyday tasks at the UHCs in Bangladesh. The results would be of interest to public health researchers and policy makers.

Break the Spasm with Succinylcholine, but Risk Intraoperative Awareness with Undiagnosed Pseudocholinesterase Deficiency

Succinylcholine is a commonly used medication in all aspects of anesthetic care, and there are a number of known side effects and complications associated with its use. However, when succinylcholine is used emergently, anesthesia providers must remain vigilant to undiagnosed conditions that pose additional risks to patients. We report the use of succinylcholine to treat acute, refractory laryngospasm after extubation leading to prolonged neuromuscular paralysis. There are unique challenges presented by this case including the risk of anesthesia awareness with recall due to the cognitive biases that prevent the clinical diagnosis of pseudocholinesterase deficiency.

Anesthesia assessment in the era of competences: state of the art

Introduction: Anesthesiology requires procedure fulfillment, problem, and real-time crisis resolution, problem, and complications forecast, among others; therefore, the evaluation of its learning should center around how students achieve competence rather than solely focusing on knowledge acquisition. Literature  shows that despite the existence of numerous evaluation strategies, these are still underrated in most cases due to unawareness. Objective: The present article aims to explain the process of competency-based anesthesiology assessment, in addition to suggesting a brief description of the learning domains evaluated, theories of knowledge, instruments, and assessment systems in the area; and finally, to show some of the most relevant results regarding assessment systems in Colombia. Methodology: The results obtained in “Characteristics of the evaluation systems used by anesthesiology residency programs stakeholders in the educational process, a fact that motivated the publishing of this discussion around the topic of competency-based assessment in anesthesiology. Following a bibliography search with the keywords through PubMed, OVID, ERIC, DIALNET, and REDALYC, 110 articles were reviewed and 75 were established as relevant for the research’s theoretical  framework. Results and conclusion: Anesthesiology assessment should be conceived from the competency’s multidimensionality; it must be longitudinal and focused on the learning objectives.

Autopilots in the Operating Room

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.