Does Team Leader Gender Matter? A Bayesian Reconciliation of Leadership and Patient Care During Trauma Resuscitations

OBJECTIVE: Team leadership facilitates teamwork and is important to patient care. It is unknown whether physician gender-based differences in team leadership exist. The objective of this study was to assess and compare team leadership and patient care in trauma resuscitations led by male and female physicians. METHODS: We performed a secondary analysis of data from a larger randomized controlled trial using video recordings of emergency department trauma resuscitations at a Level 1 trauma center from April 2016 to December 2017. Subjects included emergency medicine and surgery residents functioning as trauma team leaders. Eligible resuscitations included adult patients meeting institutional trauma activation criteria. Two video-recorded observations for each participant were coded for team leadership quality and patient care by 2 sets of raters. Raters were balanced with regard to gender and were blinded to study hypotheses. We used Bayesian regression to determine whether our data supported gender-based advantages in team leadership. RESULTS: A total of 60 participants and 120 video recorded observations were included. The modal relationship between gender and team leadership (β = 0.94, 95% highest density interval [HDI], -.68 to 2.52) and gender and patient care (β = 2.42, 95% HDI, -2.03 to 6.78) revealed a weak positive effect for female leaders on both outcomes. Gender-based advantages to team leadership and clinical care were not conclusively supported or refuted, with the exception of rejecting a strong male advantage to team leadership. CONCLUSIONS: We prospectively measured team leadership and clinical care during patient care. Our findings do not support differences in trauma resuscitation team leadership or clinical care based on the gender of the team leader

2016 ESC/EAS Guidelines for the Management of Dyslipidaemias

The Task Force for the Management of Dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS)  Developed with the special contribution of the European Assocciation for Cardiovascular Prevention & Rehabilitation (EACPR)  ABI : ankle-brachial inde

Targeted Simulation-based Leadership Training for Trauma Team Leaders

Introduction: Effective team leadership is linked to better teamwork, which in turn is believed to improve patientcare. Simulation-based training provides a mechanism to develop effective leadership behaviors. Traditionally, healthcare curricula have included leadership as a small component of broader teamwork training, with very few examples of leadership-focused curricula. The objective of this work is to describe a novel simulation-basedteam leadership curriculum that easily adapts to individual learners.Methods: We created a simulation-based team leadership training for trauma team leaders in graduatemedical education. Participants included second- and third-year emergency medicine and surgery residents. Training consisted of a single, four-hour session and included facilitated discussion of trauma leadership skills,a brief didactic session integrating leadership behaviors into Advanced Trauma Life Support®, and a seriesof simulations and debriefing sessions. The simulations contained adaptable components that facilitated individualized learning while delivering set curricular content. A survey evaluation was administered 7-24 months following the training to assess self-reported implementation of trained material.Results: A total of 36 residents participated in the training and 23 (64%) responded to the survey. The majority of respondents (n = 22, 96%) felt the training was a valuable component of their residency education and allrespondents reported ongoing use of at least one behavior learned during the training. The most commonly cited skills for ongoing use included the pre-arrival brief (n = 21, 91%) and prioritization (n = 21, 91%).Conclusion: We delivered a leadership-focused, simulation-based training that 1) adapted to learners’individual needs, and 2) was perceived to impact practice up to 24 months post-training. More work is needed tounderstand the impact of this training on learner knowledge and behavior, as well as patient outcomes.

Leveraging a health information exchange to examine the accuracy of self-report emergency department utilization data among hospitalized injury survivors

Background Accurate acute care medical utilization history is an important outcome for clinicians and investigators concerned with improving trauma center care. The objective of this study was to examine the accuracy of self-report emergency department (ED) utilization compared with utilization obtained from the Emergency Department Information Exchange (EDIE) in admitted trauma surgery patients with comorbid mental health and substance use problems.Methods This is a retrospective cohort study of 169 injured patients admitted to the University of Washington’s Harborview Level I Trauma Center. Patients had high levels of post-traumatic stress disorder and depressive symptoms, suicidal ideation and alcohol comorbidity. The investigation used EDIE, a novel health technology tool that collects information at the time a patient checks into any ED in Washington and other US states. Patterns of EDIE-documented visits were described, and the accuracy of injured patients’ self-report visits was compared with EDIE-recorded visits during the course of the 12 months prior to the index trauma center admission.Results Overall, 45% of the sample (n=76) inaccurately recalled their ED visits during the past year, with 36 participants (21%) reporting less ED visits than EDIE indicated and 40 (24%) reporting more ED visits than EDIE indicated. Patients with histories of alcohol use problems and major psychiatric illness were more likely to either under-report or over-report ED health service use.Discussion Nearly half of all patients were unable to accurately recall ED visits in the previous 12 months compared with EDIE, with almost one-quarter of patients demonstrating high levels of disagreement. The improved accuracy and ease of use when compared with self-report make EDIE an important tool for both clinical and pragmatic trial longitudinal outcome assessments. Orchestrated investigative and policy efforts could further examine the benefits of introducing EDIE and other information exchanges into routine acute care clinical workflows.Level of evidence II/III.Trial registration number ClinicalTrials.gov NCT02274688

Targeted Simulation-based Leadership Training for Trauma Team Leaders

Introduction: Effective team leadership is linked to better teamwork, which in turn is believed to improve patient care. Simulation-based training provides a mechanism to develop effective leadership behaviors. Traditionally, healthcare curricula have included leadership as a small component of broader teamwork training, with very few examples of leadership-focused curricula. The objective of this work is to describe a novel simulation-based team leadership curriculum that easily adapts to individual learners. Methods: We created a simulation-based team leadership training for trauma team leaders in graduate medical education. Participants included second- and third-year emergency medicine and surgery residents. Training consisted of a single, four-hour session and included facilitated discussion of trauma leadership skills, a brief didactic session integrating leadership behaviors into Advanced Trauma Life Support®, and a series of simulations and debriefing sessions. The simulations contained adaptable components that facilitated individualized learning while delivering set curricular content. A survey evaluation was administered 7–24 months following the training to assess self-reported implementation of trained material. Results: A total of 36 residents participated in the training and 23 (64%) responded to the survey. The majority of respondents (n = 22, 96%) felt the training was a valuable component of their residency education and all respondents reported ongoing use of at least one behavior learned during the training. The most commonly cited skills for ongoing use included the pre-arrival brief (n = 21, 91%) and prioritization (n = 21, 91%). Conclusion: We delivered a leadership-focused, simulation-based training that 1) adapted to learners’ individual needs, and 2) was perceived to impact practice up to 24 months post-training. More work is needed to understand the impact of this training on learner knowledge and behavior, as well as patient outcomes

Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study

Objectives: Identification of patients with coronavirus disease 2019 (COVID-19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID-19 at risk for return and hospital admission within 30 days of ED discharge. Methods: We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID-19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches. Results: Among patients with COVID-19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine-learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71–0.78), with a sensitivity of 0.46 (95% CI, 0.39–0.54) and a specificity of 0.84 (95% CI, 0.82–0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models. Conclusions: A predictive model identifying adult ED patients with COVID-19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods (eg, GBM, RF, and LASSO) outperform the single-tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow-up resources

Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021.

Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. Results Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation75% probability with +5 or more points). Conclusion Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints