Richard Fidler

Innovative Approach Using Interprofessional Simulation to Educate Surgical Residents in Technical and Nontechnical Skills in High-Risk Clinical Scenarios

IMPORTANCE The Accreditation Council for Graduate Medical Education core competencies stress nontechnical skills that can be difficult to evaluate and teach to surgical residents. During emergencies, surgeons work in interprofessional teams and are required to perform certain procedures. To obtain proficiency in these skills, residents must be trained. OBJECTIVE To educate surgical residents in leadership, teamwork, effective communication, and infrequently performed emergency surgical procedures with the use of interprofessional simulations. DESIGN, SETTING, AND PARTICIPANTS SimMan 3GS was used to simulate high-risk clinical scenarios (15-20 minutes), followed by debriefings with real-time feedback (30 minutes). A modified Oxford Non-Technical Skills scale (score range, 1-4) was used to assess surgical resident performance during the first half of the academic year (July-December 2012) and the second half of the academic year (January-June 2013). Anonymous online surveys were used to solicit participant feedback. Simulations were conducted in the operating room, intensive care unit, emergency department, ward, and simulation center. A total of 43 surgical residents (postgraduate years [PGYs] 1 and 2) participated in interdisciplinary clinical scenarios, with other health care professionals (nursing, anesthesia, critical care, medicine, respiratory therapy, and pharmacy; mean number of nonsurgical participants/session: 4, range 0-9). Thirty seven surgical residents responded to the survey. EXPOSURES Simulation of high-risk clinical scenarios: postoperative pulmonary embolus, pneumothorax, myocardial infarction, gastrointestinal bleeding, anaphylaxis with a difficult airway, and pulseless electrical activity arrest. MAIN OUTCOMES AND MEASURES Evaluation of resident skills: communication, leadership, teamwork, problem solving, situation awareness, and confidence in performing emergency procedures (eg, cricothyroidotomy). RESULTS A total of 31 of 35 (89%) of the residents responding found the sessions useful. Additionally, 28 of 33 (85%) reported improved confidence doing procedures and 29 of 37 (78%) reported knowing when the procedure should be applied. Oxford Non-Technical Skills evaluation demonstrated significant improvement in PGY 2 resident performance assessed during the 2 study periods: communication score increased from 3 to 3.71 (P=.01), leadership score increased from 2.77 to 3.86 (P<.001), teamwork score increased from 3.15 to 3.86 (P=.007), and procedural ability score increased from 2.23 to 3.43 (P=.03). There were no statistically significant improved scores in PGY 2 decision making or situation awareness. No improvements in skills were seen among PGY 1 participants. CONCLUSIONS AND RELEVANCE The PGY 2 residents improved their skills, but the PGY 1 residents did not. Participants found interprofessional simulations to be realistic and a valuable educational tool. Interprofessional simulation provides a valuable means of educating surgical residents and evaluating their skills in real-life clinical scenarios.

Human factors approach to evaluate the user interface of physiologic monitoring.

BACKGROUND As technology infiltrates more of our personal and professional lives, user expectations for intuitive design have driven many consumer products, while medical equipment continues to have high training requirements. Not much is known about the usability and user experience associated with hospital monitoring equipment. This pilot project aimed to better understand and describe the user interface interaction and user experience with physiologic monitoring technology. DESIGN This was a prospective, descriptive, mixed-methods quality improvement project to analyze perceptions and task analyses of physiologic monitors. METHODS Following a survey of practice patterns and perceived abilities to accomplish key tasks, 10 voluntary experienced physician and nurse subjects were asked to perform a series of tasks in 7 domains of monitor operations on GE Monitoring equipment in a single institution. For each task analysis, data were collected on time to complete the task, the number of button pushes or clicks required to accomplish the task, economy of motion, and observed errors. RESULTS Although 60% of the participants reported incorporating monitoring data into patient care, 80% of participants preferred to receive monitoring data at the point of care (bedside). Average perceived central station usability is 5.3 out of 10 (ten is easiest). CONCLUSIONS High variability exists in monitoring station interaction performance among those participating in this project. Alarms were almost universally silenced without cognitive recognition of the alarm state. Education related to monitoring operations appeared largely absent in this sample. Most users perceived the interface to not be intuitive, complaining of multiple layers and steps for data retrieval. These clinicians report real-time monitoring helpful for abrupt changes in condition like arrhythmias; however, reviewing alarms is not prioritized as valuable due to frequent false alarms. Participants requested exporting monitoring data to electronic medical records. Much research is needed to develop best practices for display of real-time information, organization and filtering of meaningful data, and simplified ways to find information.

Electrocardiogram Signal Quality Assessment Based on Structural Image Similarity Metric

Objective: We developed an image-based electrocardiographic (ECG) quality assessment technique that mimics how clinicians annotate ECG signal quality. Methods: We adopted the structural similarity measure (SSIM) to compare images of two ECG records that are obtained from displaying ECGs in a standard scale. Then, a subset of representative ECG images from the training set was selected as templates through a clustering method. SSIM between each image and all the templates were used as the feature vector for the linear discriminant analysis classifier. We also employed three commonly used ECG signal quality index (SQI) measures: baseSQI, kSQI, and sSQI to compare with the proposed image quality index (IQI) approach. We used 1926 annotated ECGs, recorded from patient monitors, and associated with six different ECG arrhythmia alarm types which were obtained previously from an ECG alarm study at the University of California, San Francisco (UCSF). In addition, we applied the templates from the UCSF database to test the SSIM approach on the publicly available PhysioNet Challenge 2011 data. Results: For the UCSF database, the proposed IQI algorithm achieved an accuracy of 93.1% and outperformed all the SQI metrics, baseSQI, kSQI, and sSQI, with accuracies of 85.7%, 63.7%, and 73.8% respectively. Moreover, evaluation of our algorithm on the PhysioNet data showed an accuracy of 82.5%. Conclusion : The proposed algorithm showed better performance for assessing ECG signal quality than traditional signal processing methods. Significance: A more accurate assessment of ECG signal quality can lead to a more robust ECG-based diagnosis of cardiovascular conditions.

Understanding heart rate alarm adjustment in the intensive care units through an analytical approach

Background Heart rate (HR) alarms are prevalent in ICU, and these parameters are configurable. Not much is known about nursing behavior associated with tailoring HR alarm parameters to individual patients to reduce clinical alarm fatigue. Objectives To understand the relationship between heart rate (HR) alarms and adjustments to reduce unnecessary heart rate alarms. Methods Retrospective, quantitative analysis of an adjudicated database using analytical approaches to understand behaviors surrounding parameter HR alarm adjustments. Patients were sampled from five adult ICUs (77 beds) over one month at a quaternary care university medical center. A total of 337 of 461 ICU patients had HR alarms with 53.7% male, mean age 60.3 years, and 39% non-Caucasian. Default HR alarm parameters were 50 and 130 beats per minute (bpm). The occurrence of each alarm, vital signs, and physiologic waveforms was stored in a relational database (SQL server). Results There were 23,624 HR alarms for analysis, with 65.4% exceeding the upper heart rate limit. Only 51% of patients with HR alarms had parameters adjusted, with a median upper limit change of +5 bpm and -1 bpm lower limit. The median time to first HR parameter adjustment was 17.9 hours, without reduction in alarms occurrence (p = 0.57). Conclusions HR alarms are prevalent in ICU, and half of HR alarm settings remain at default. There is a long delay between HR alarms and parameters changes, with insufficient changes to decrease HR alarms. Increasing frequency of HR alarms shortens the time to first adjustment. Best practice guidelines for HR alarm limits are needed to reduce alarm fatigue and improve monitoring precision.

Unplanned transfer from the telemetry unit to the intensive care unit in hospitalized patients with suspected acute coronary syndrome.

BACKGROUND Most patients presenting with suspected acute coronary syndrome (ACS) are admitted to telemetry units. While telemetry is an appropriate level of care, acute complications requiring a higher level of care in the intensive care unit (ICU) occur. PURPOSE Among patients admitted to telemetry for suspected ACS, we determine the frequency of unplanned ICU transfer, and examine whether ECG changes indicative of myocardial ischemia, and/or symptoms preceded unplanned transfer. METHOD This was a secondary analysis from a study assessing occurrence rates for transient myocardial ischemia (TMI) using a 12-lead Holter. Clinicians were blinded to Holter data as it was used in the context research; off-line analysis was performed post discharge. Hospital telemetry monitoring was maintained as per hospital protocol. TMI was defined as >1mm ST-segment ↑ or ↓, in >1 ECG lead, >1minute. Symptoms were assessed by chart review. RESULTS In 409 patients (64±13years), most were men (60%), Caucasian (93%), and had a history of coronary artery disease (47%). Unplanned transfer to the ICU occurred in 9 (2.2%), was equivalent by gender, and age (no transfer 64±13years vs transfer 67±11years). Four patients were transferred following unsuccessful percutaneous coronary intervention (PCI) attempt, four due to recurrent angina, and one due to renal and hepatic failure. Mean time from admission to transfer was 13±6hours, mean time to ECG detected ischemia was 6±5hours, and 8.8±5hours for symptoms prompting transfer. In two patients ECG detected ischemia and acute symptoms prompting transfer were simultaneous. In five patients, ECG detected ischemia was clinically silent. All patients eventually had symptoms that prompted transfer to the ICU. In all nine patients, there was no documentation or nursing notes regarding bedside ECG monitor changes prior to unplanned transfer. Hospital length of stay was longer in the unplanned transfer group (2days ± 2 versus 6days ± 4; p=0.018). CONCLUSIONS In patients with suspected ACS, while unplanned transfer from telemetry to ICU is uncommon, it is associated with prolonged hospitalization. Two primary scenarios were identified; (1) following unsuccessful PCI, and (2) recurrent angina. Symptoms prompting unplanned transfer occurred, but happened on average 8.8 hours after hospital admission; whereas ECG detected ischemia preceding unplanned transfer occurred on average 6 hours after hospital admission.

Easy-to-implement oral cavity modification to expand simulation-based training in airway management.

Introduction Injuries to the oral cavity and teeth can occur during routine intubation and general anesthesia but often occur in emergency situations when the priority of securing the airway supersedes preanesthetic evaluation. This study demonstrates the feasibility of modifying the oral cavity to increase the dental fidelity during emergency airway management. Methods A Laerdal Manikin was used to manipulate the preexisting Polyester (hard) and the Vinyl (flexible) dentition sets that are interchangeable among the Laerdal family of manikins. Items easily available in a dental laboratory such as dental acrylic and dental impression material were used to create modifications. Results Laerdal dentition sets were altered to simulate common dental (tooth-related) trauma encountered during intubation such as a fracture, luxation, or avulsion injuries. Anatomic variations such as carious (decayed) teeth, loose teeth, and class II malocclusion (overbite) were also fabricated. Tooth luxation was engineered to occur with pressure by a laryngoscope, and bleeding teeth were also created to demonstrate excessive pressure applied during direct laryngoscopy. It is feasible to improve the realism of the Laerdal family of manikins with simple modifications. Conclusions This project proves the concept of feasibly fabricating anatomic variations to increase the fidelity of existing simulation manikins. Other anatomic variations present challenges to airway management, and future research will aim at creating additional modifications. In addition, future research will seek to quantify the improvement in airway management skills by anesthesia and emergency medicine providers by training on manikins with variable oral cavity anatomy.

Monitoring significant ST changes through deep learning

According to the statistics (2016 update) from the American Heart Association (AHA), 15.5 million people over 20 years old in the US have coronary heart disease, and every 42 s, an American suffers from myocardial infarction (MI) [1]. For patients admitted into hospitals with suspected acute coronary syndrome (ACS), electrocardiography (ECG) is an important risk-stratification and assessment tool to guide further treatment for MI, and ST-segment changes in ECG constitute the principle biomarker for such purpose. However, b25% of ACS patients present ST elevation (ST-elevation MI, or STEMI) and receive immediate medical attention. For the other 75% of myocardial infarctions, includingnon-ST elevationACS (NSTE-ACS) or unstable angina (UA) [2], continuous ST-segment monitoring is crucial for early identification of transientmyocardial ischemia (TMI, precursor ofMI) and to prevent adverse clinical events. Unfortunately, current ST-segment monitoring systems have yet to fulfil their designed purpose due to excessive false positive alarms. One study tracking a 16-bed intensive cardiac care (ICC) unit during a 31-day period discovered an average of 200 ST alarms per day, even with stricter trigger threshold at 200 μV being adopted in the facility instead of the recommended 100 μV, and over 90% of them are nonactionable alarms [3]. These nuisance alarms further contribute to the issue of alarm fatigue, which is ranked as one of the top 10 technology hazards by the Emergency Care Research Institute (ECRI) in 2014 [4]. Alarm fatigue is described as the sensory overload caused by the overwhelming visual and auditory alerts generated by bedside physiologic motors to caregivers, whichmay lead tomissed critical clinical opportunities [3]. Due to alarm fatigue, a recent statement from AHA has decreased the class of recommendation (COR) for ST alarms from class I (should be performed) to class IIa (is reasonable to perform) [5]. Thus, there is an urgent unmet need for ST-segment monitoring algorithms with improved precision. Recent advancement of deep learning has transformed many fields of study by taking advantage of big data and modern computing resources. The tremendous amount of digitized ECG data generated in

Interventions to improve the mechanical ventilation fidelity of the Laerdal SimMan® 3G simulation mannequin

To the Editor, The Laerdal SimMan 3G mannequin (Laerdal USA, Wappingers Falls, NY, USA) is widely used for highfidelity simulation. Nevertheless, providers familiar with mechanical ventilators commonly notice a lower lung compliance, necessitating lower set tidal volumes (VT) to reduce airway pressure. SimMan 3G is 180 cm tall, which corresponds to an ideal body weight of 75 kg (according to http://www.ardsnet.org/tools.shtml) and a VT of 600 mL at 8 mL kg. This ‘‘normal’’ VT without positive endexpiratory pressure (PEEP) results in a peak airway pressure of 34 cmH2O. For a VT of 500 mL, the peak airway pressure was 28 cmH2O and the static airway compliance was 24 mL cmH2O. This static airway compliance of the stock mannequin is about 30-50% of typical human values. These values are reproducible among six SimMan 3G mannequins to which our group has access. Adding PEEP exacerbates problems with highpressure alarms, and a PEEP above 5 cmH2O and VT of 500 mL results in an air leak greater than 2 L min (ventilator bellows cannot be driven with 2 L min flow). The high peak airway pressures and air leak are frequently noticed in simulation sessions for maintenance of certification in anesthesia done for the American Board of Anesthesiology. High airway pressures may mislead participants by suggesting acute lung injury, endotracheal tube kinking, or a mucus plug. This causes trainee confusion and frequent intervention and interruption by the simulation team, making it difficult for participants to be fully engaged in the scenario. To circumvent these problems, we investigated fitting larger, more elastic reservoirs (Rusch 500-mL ventilator test lungs) inside the mannequin. The stock Laerdal SimMan 3G mannequin has 350-mL reservoir bags (part 212-11150). We also attached satellite reservoirs (Siemens Maquet ventilator test lungs; Maquet, Rastatt, Germany) with a Y connector to the circuit outside the mannequin. A third modification was to extend the internal connection tubing to avoid disconnections. Replacement of the stock reservoirs (Figure A) inside the mannequin with a 500-mL Rusch ventilator test lung (Teleflex, Wayne, PA, USA) (Figure B) resulted in higher fidelity in VT, peak airway pressure, and calculated compliance. For a 500-mL VT, peak airway pressure was 12 cmH2O and static compliance was 50 mL cmH2O. The fitting of an external ‘‘satellite’’ bag (Figure D) was done with 250-, 500-, and 1000-mL Siemens Maquet ventilator test lungs. Using the 1000-mL bag and a 500-mL VT, peak airway pressure was 8 cmH2O and static compliance was normal at 83 mL cmH2O. Additionally, we were able to achieve a PEEP of 15 cmH2O and a VT of 400 mL without high pressure alarms (peak airway pressure was 38 cmH2O). High airway pressures combined with chest compressions in simulation scenarios contribute to frequent tubing disconnects and ventilation reservoir ruptures. Replacement of the internal connection tubing with a longer piece of tubing and stronger cable ties abolished further occurrences of tubing disconnect (Figure C). N. J. Mehta, MD (&) R. Latoures, RN, MS M. M. Stechert, MD, PhD R. L. Fidler, PhD, CRNA, NP, MBA J. Hirsch, MD, PhD Simulation Center and Anesthesia Service, San Francisco VA Medical Center and University of California San Francisco, San Francisco, CA, USA e-mail: neilmehta83@gmail.com

Research: Association of Low-Amplitude QRSs with False-Positive Asystole Alarms

BACKGROUND Although electrocardiographic monitoring is valuable for continuous surveillance of intensive care unit (ICU) patients, false alarms are common and have been cited as a cause of alarm fatigue. ANSI/AAMI EC12:2002 states that electrocardiograms (ECGs) should not detect a QRS if the waveform is less than 0.15 mV (1.5 mm) for adult patients, in order to avoid mislabeling P waves or baseline noise as QRSs during complete heart block or asystole. However, ECG software algorithms often use more conservative QRS thresholds, which may result in false-positive asystole alarms in patients with low-amplitude QRS complexes. OBJECTIVES To 1) assess the frequency of low QRS amplitude in a group of ICU patients with one or more false-positive asystole alarms and 2) determine whether low-amplitude QRSs are associated with false-positive asystole alarms during continuous ECG monitoring. METHODS Hospital-acquired standard 12-lead ECGs were examined in a group of 82 ICU patients who had one or more false-positive asystole alarms. Low QRS amplitude was defined as a unidirectional (only positive or negative) QRS of less than 5 mm in two of four leads (I, II, III, and V1). RESULTS Low-amplitude QRSs were present in 45 of 82 (55%) patients. The presence of low-amplitude QRSs did not differ according to age, sex, or race. Patients treated in the cardiac ICU had the highest proportion of low-amplitude QRSs. An equivalent proportion of patients had false-positive asystole alarms by group (no low-amplitude QRSs 95% vs. low-amplitude QRSs 87%; P = 0.229). Eight patients (10%) had both true- and false-positive asystole alarms (two [5%] with no low-amplitude QRSs and six [13%] with low-amplitude QRSs; P = 0.229). CONCLUSION Low-amplitude QRS, as assessed from hospital 12-lead ECGs, occurs frequently and is more common in cardiac ICU patients. However, this ECG feature did not identify patients with false-positive asystole alarms during continuous ECG monitoring.

Simulation Manikin Modifications for High-Fidelity Training of Advanced Airway Procedures.

Thoracic anesthesia procedures are challenging to master during anesthesia training. A Laerdal ALS Simulator® manikin was modified by adding a bronchial tree module to create fidelity to the fourth generation. After modification, placement of endotracheal tubes up to 8.0 mm is possible by direct laryngoscopy, video laryngoscopy, and fiberoptically; in addition, it allows fiberoptically guided insertion of endobronchial blockers. Insertion of left and right 35-Fr double-lumen tubes permits double- and single-lung ventilation with continuous positive airway pressure and positive end-expiratory pressure. This anatomical modification created a high-fidelity training tool for thoracic anesthesia that has been incorporated into educational curricula for anesthesia.