Worth Center Research Articles

Abstract 280: Effectiveness of Bag-Valve-Mask Ventilation During Out-of-Hospital Pediatric CPR

Background: Few studies have measured ventilation during early cardiopulmonary resuscitation (CPR) before advanced airway placement. Resuscitation guidelines recommend pauses after every 15 or 30 chest compressions to deliver ventilations for children. The effectiveness of bag-valve-mask (BVM) ventilation delivered during the pause in chest compressions is unknown. Hypothesis: Lung inflation occurs infrequently with BVM ventilation during 30:2 (and 15:2) CPR. Aim: To determine the incidence of lung inflation with BVM ventilation during 15:2 or 30:2 CPR for pediatric OHCA. Methods: This is a retrospective, observational study of 63 OHCA patients, <18 years of age, with non-traumatic cardiac arrest from the Dallas-Fort Worth Center for Resuscitation Research registry. Emergency medical service rescuers provided 30:2 or 15:2 CPR with BVM ventilation and ≥ 2 min of recorded CPR. We measured bioimpedance ventilation (lung inflation) waveforms in the pauses between chest compression segments recorded through defibrillation pads. A detectable ventilation waveform had a bioimpedance amplitude ≥0.5 Ohm and duration ≥1 sec. We measured the incidence of ventilation in two pre-specified groups: patients with ventilation waveforms in <50% of pauses (Group 1) vs. those with waveforms in ≥50% of pauses (Group 2). Results: The mean duration of 30:2 or 15:2 CPR was 15(8) min prior to advanced airway placement. During this period, we identified 683 pauses in chest compressions, 386 measurable ventilations, and the median (IQR) number of ventilations was 2 (0-4). Of 20 advanced airway attempts, nine were successful.Group 1 (N=48) had a median 8 pauses and 1.5 ventilations/patient, Group 2 (N= 15) had a median 10 pauses and 12 ventilations/patient (Table). Return of spontaneous circulation for Group 1 (15%, 7/48) vs. Group 2 (33%, 5/15). Conclusion: This study shows that use of a BVM device results in infrequent lung inflation during 30:2 or 15:2 CPR in pediatric patients.

Abstract 235: The Covid-19 Pandemic in Dallas: Increased Out-of-hospital Cardiac Arrest and Death

Introduction: Since March, the CDC has reported weekly excess deaths of up to 40% in the US. Spikes in excess deaths were associated with spikes in COVID-19 cases. In Texas, 55% of excess deaths from March 1 to May 30, 2020 were unattributed to COVID-19. One possible source of this unaccounted excess death is out-of-hospital cardiac arrest, which corresponds to a notable decline in hospitalizations for acute cardiovascular events. Objective: To compare the number of out-of-hospital cardiac arrest (OOHCA) and dead-on-scene cases in Dallas, Texas in years prior to and during the COVID-19 pandemic. Methods: The Dallas-Fort Worth Center for Resuscitation Research Cardiac Arrest Registry provided data for this IRB-approved study. Eligible cases were non-traumatic OOHCAs who received cardiopulmonary resuscitation (CPR) from emergency medical services (EMS) providers. We compared monthly case numbers in 2017, 2018, and 2019 with case numbers in 2020 at the time of the COVID-19 pandemic in Dallas, TX. Additionally, we compared the numbers of non-traumatic deaths (declared dead on scene without receiving CPR from EMS providers) in the same timeframes. Results: While total counts of out-of-hospital cardiac arrest during the months of March to June decreased from 2017 to 2019 by 9.2% and 1.2% respectively, the number of cardiac arrest cases rose dramatically by 47.9% in 2020 (p < 0.05).(Table) The percentage of OOHCAs with an initial rhythm of asystole increased as well, from 50.1% in 2018 and 2019 to 60.4% in 2020. The total number of dead-on-scene cases was also higher in March to June 2020 than in 2017 to 2019 (NS). Conclusion: Increased out-of-hospital cardiac arrest is likely contributing to the unattributed excess deaths noted during the months of the COVID-19 pandemic in Dallas, TX. The rise in cases may be indirectly attributable to COVID-19 as cardiovascular events go untreated or treated too late.

Abstract 308: Automated Detection of Patients with Return of Spontaneous Circulation in the Retrospective Analysis of Resuscitation Episodes

Introduction: Retrospective analysis of out-of-hospital cardiac arrest episodes is important to evaluate the performance of resuscitation teams, and to advance research. Large numbers of electronic files are compiled in cardiac arrest registries and identifying patients with return of spontaneous circulation (ROSC) is expensive and time-consuming. The aim of this study was to analyze the feasibility of automatically annotating ROSC using the signals recorded by defibrillators. Materials and methods: A set of 893 patients (261 with ROSC, of which 127 were transient) recorded by the Dallas-Fort Worth Center for Resuscitation Research using Philips HeartStart MRx devices were included. Every record contained the ECG and the thoracic impedance (TI) signals. ROSC was automatically identified as follows: 1) chest compression pauses longer than 60 s were identified using either the TI or the compression depth signals. 2) Organized ECG rhythms were identified using a comercial defibrillator's algorithm. 3) Pulsatile rhythms were identified using a published machine learning algorithm based on the ECG or the ECG and TI signals. The model uses up to 9 features, extracted from both the ECG (6) and the TI (3) signals. ROSC was identified when a 10s-interval was classified as pulsatile and the ROSC onset was set at the beginning of the pause. The performance was assessed in terms of Sensitivity (Se, ROSC patients), Specificity (Sp, no ROSC patients) and overall F1-score. Error in the ROSC onset was calculated comparing the automatically detected onset to the reviewed time of the clinical annotation of ROSC. Results: The algorithm based exclusively on ECG showed Se, Sp and F1 scores of 90.0%, 92.7% and 90.5%, respectively. The algorithm that included both ECG and TI improved scores to 91.2%, 94.3% and 92.1%. For the set of patients with transient ROSC the algorithm showed a Se of 81.9% (95.6% for permanent ROSC) using the ECG and TI signals. The median (IQR) absolute error on the time of first ROSC onset was 0.8 (0.6-8.2) s. Conclusions: An accurate algorithm to automatically identify patients with ROSC is demonstrated. This technique could be useful for retrospective analysis of cardiac arrest registries.

Abstract 264: Automatic Detection of Ventilations Using the Thoracic Impedance Signal During Lucas Chest Compressions

Introduction: Resuscitation from out-of-cardiac arrest (OHCA) requires control of both chest compressions and lung ventilation. There are few effective methods for detecting ventilations during cardiopulmonary resuscitation. Thoracic impedance (TI) is sensitive to changes in lung air volumes and may allow detection of ventilations but has not been tested with concurrent mechanical chest compressions. Hypothesis: It is possible to automatically detect and characterize ventilations from TI changes during mechanical chest compressions. Methods: A cohort of 420 OHCA cases (27 survivors to hospital discharge) were enrolled in the Dallas-Fort Worth Center for Resuscitation Research cardiac arrest registry. These patients were treated with the LUCAS-2 CPR device and had concurrent TI and capnogram recordings from MRx (Philips, Andover, MA) monitor-defibrillators. We developed a signal processing algorithm to suppress chest compression artifacts from the TI signal, allowing identification of ventilations. We used the capnogram as gold standard for delivered ventilations. We determined the accuracy of the algorithm for detecting capnogram-indicated ventilations, calculating sensitivity, the proportion of true ventilations detected in the TI, and positive predictive value (PPV), the proportion of true ventilations within the detections. We calculated per minute ventilation rate and mean TI amplitude, as surrogate for tidal volume. Statistical differences between survivors and non-survivors were assessed using the Mann-Whitney test. Results: We studied 4331 minutes of TI during CPR. There were a median of 10 (IQR 6-14) ventilations per min and 52 (30-81) ventilations per patient. Sensitivity of TI was 95.9% (95% CI, 74.5-100), and PPV was 95.8% (95% CI, 80.0-100). The median ventilation rates for survivors and non-survivors were 7.75 (5.37-9.91) min -1 and 5.64 (4.46-7.15) min -1 (p<10 -3 ), and the median TI amplitudes were 1.33 (1.03-1.75) Ω and 1.14 (0.77-1.66) Ω (p=0.095). Conclusions: An accurate automatic TI ventilation detection algorithm was demonstrated during mechanical CPR. The relation between ventilation rate during mechanical CPR and survival was significant, but it was not for impedance amplitude.

Aviation Human Factors Related Industry News

Aviation Human Factors Related Industry News

Top-of-Climb Matching Method for Reducing Aircraft Trajectory Prediction Errors

The inaccuracies of the aircraft performance models used by trajectory predictors with regard to takeoff weight, thrust, climb profile, and other parameters result in altitude errors during the climb phase that often exceed the vertical separation standard of 1000 ft. This study investigates the potential reduction in altitude trajectory prediction errors that could be achieved for climbing flights if just one additional parameter is made available: top-of-climb time. The top-of-climb matching method developed and evaluated in this paper is straightforward: A set of candidate trajectory predictions is generated using different aircraft weight parameters, and the one that most closely matches top of climb in terms of time is selected. This algorithm was tested using more than 1000 climbing flights in Fort Worth Center. Compared with the baseline trajectory predictions of a real-time research prototype (Center/Terminal Radar Approach Control Automation System), the top-of-climb matching method reduced the altitude root mean square error for a 5 min prediction time by 38%. It also decreased the percentage of flights with absolute altitude error greater than the vertical separation standard of 1000 ft for the same look-ahead time from 55 to 30%.

Dynamic Weather Routes: Two Years of Operational Testing at American Airlines

The Dynamic Weather Routes (DWR) tool continuously and automatically analyzes active flights in en route airspace and finds simple route corrections to achieve more time- and fuel-efficient routes around convective weather. A strong partnership between the National Aeronautics and Space Administration (NASA), American Airlines (AA), and the Federal Aviation Administration (FAA) has enabled testing of DWR in real-world air traffic operations. NASA and AA have been conducting a trial of DWR at AA’s Integrated Operations Control Center in Fort Worth, Texas since July 2012. This paper describes test results based on AA’s use of DWR for their flights in and around Fort Worth Center (ZFW). Results indicate an actual savings of 3,949 flying minutes for 624 AA revenue flights from January 2013 through December 2014. Of these, 101 flights each indicate a savings of 15 min or more. Potential savings for all flights in ZFW airspace, corrected for savings flights achieve today through normal pilot requests and controller clearances without DWR, is about 100,000 flying minutes for 15,000 flights in 2013. Results indicate that AA flights with DWR in use realize about 15% more savings than non-AA flights. A weather forecast analysis examines the extent to which DWR routes rated acceptable by AA users remain clear of downstream weather. A sector congestion analysis indicates congestion could be reduced by about 20% if all flights fly DWR routes rather than nominal weather-avoidance routes.

Adaptive Algorithm to Improve Trajectory Prediction Accuracy of Climbing Aircraft

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced concepts in the Next Generation Air Transportation System. An algorithm that dynamically adjusts modeled aircraft weights based on observed track data to improve the accuracy of trajectory predictions for climbing flights has been developed. In real-time evaluation with actual Fort Worth Center traffic, the algorithm decreased the altitude root-mean-square error by about 20%. It also reduced the root-mean-square error of predicted time at top of climb by the same amount.

Adaptive Eulerian Modeling for Air Traffic Flow Management

A method for deriving an Eulerian traffic flow model in real time without depending on online integration of aircraft trajectories is introduced. In this method, online model derivation starts with a baseline Eulerian airspace model, which is derived offline using historical track data. In real time, parameters of this baseline model are adapted depending on the differences in flight plans and control volume aircraft counts between the baseline model and the real world. The baseline model derivation method is based on bookkeeping of control volume aircraft counts and, hence, the model produces control decisions in terms of whole number aircraft counts, unlike the fractional counts produced by Eulerian models based on average aircraft speeds. The bookkeeping-based model indirectly retains trajectory information and aircraft identities. Hence, it serves as an excellent tradeoff between Eulerian and trajectory-based modeling approaches. Most importantly, as an improvement over previous approaches, we take into consideration the control-dependent nature of the Eulerian model while computing optimal flow-control decisions. A key feature of this model is the use of interstream jump (y) parameters. These parameters make the model easily amenable to emulate a wide variety of control actions like rerouting. As a proof of concept, we derive a baseline model for the Fort Worth center from recorded track data for a period of 1 h on 13 March 2005 and adapt it it to predict sector counts for another time period. An application of the adaptive model to optimal rerouting is also presented.

Developing ATC Automation in the Field: It Pays to Get Your Hands Dirty

This paper describes the field assessment process that has been applied to the development of an advanced ATC automation system, the Center/Terminal Radar Approach Control (TRACON) Automation System, known as CTAS. CTAS is installed at the Denver Center and TRACON, the Ft. Worth Center, and the Dallas-Ft. Worth TRACON for development purposes. The field assessment process is focused on refining the system and on enhancing our understanding of the potential impact of the CTAS tools on problem solving, decision making, facility coordination, and workload. Key human factors assessments of CTAS are described. Because of the complexity of the ATC environment, the opportunity to assess CTAS in an operational context has provided insights that would not have been possible in simpler simulation environments.

The Case for Comparable Worth

Comparable worth is a necessary and feasible remedy for the systematic, sex‐related pay inequities found in most contemporary work organizations. It is necessary because discriminatory pay practices of the past were not fully addressed by equal employment opportunity legislation, and their effects have been perpetuated by conventional pay administration practices. It is feasible because it can be assessed and implemented simply, often using data already available, at a relatively small cost to the organization. Arguments against the necessity of comparable worth are based largely on academic studies of pay discrimination, which differ from comparable worth studies in their context, purpose, methods, and results. Arguments against the feasibility of comparable worth center on potential costs and technical criticisms of comparable worth pay policies, objections contradicted by the facts of actual comparable worth implementation.