Showing papers by "Jo Kramer-Johansen published in 2017"

Improved Retention of Chest Compression Psychomotor Skills With Brief "Rolling Refresher" Training.

Abstract: IntroductionHigh-quality cardiopulmonary resuscitation (CPR) is critical to improve survival from cardiac arrest. However, cardiopulmonary resuscitation knowledge and psychomotor skill proficiency are transient. We hypothesized that brief, in situ refresher training will improve chest compression (C

ECG-Based Classification of Resuscitation Cardiac Rhythms for Retrospective Data Analysis

Abstract: Objective: There is a need to monitor the heart rhythm in resuscitation to improve treatment quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electrical activity (PEA), asystole (AS), and pulse-generating rhythm (PR). Manual annotation of rhythms is time-consuming and infeasible for large datasets. Our objective was to develop ECG-based algorithms for the retrospective and automatic classification of resuscitation cardiac rhythms. Methods: The dataset consisted of 1631 3-s ECG segments with clinical rhythm annotations, obtained from 298 out-of-hospital cardiac arrest patients. In total, 47 wavelet- and time-domain-based features were computed from the ECG. Features were selected using a wrapper-based feature selection architecture. Classifiers based on Bayesian decision theory, k-nearest neighbor, k-local hyperplane distance nearest neighbor, artificial neural network (ANN), and ensemble of decision trees were studied. Results: The best results were obtained for ANN classifier with Bayesian regularization backpropagation training algorithm with 14 features, which forms the proposed algorithm. The overall accuracy for the proposed algorithm was 78.5%. The sensitivities (and positive-predictive-values) for AS, PEA, PR, VF, and VT were 88.7% (91.0%), 68.9% (70.4%), 65.9% (69.0%), 86.2% (83.8%), and 78.8% (72.9%), respectively. Conclusions: The results demonstrate that it is possible to classify resuscitation cardiac rhythms automatically, but the accuracy for the organized rhythms (PEA and PR) is low. Significance: We have made an important step toward making classification of resuscitation rhythms more efficient in the sense of minimal feedback from human experts.

Meeting abstracts from the first European Emergency Medical Services congress (EMS2016): Copenhagen, Denmark. 30 May - 1 June 2016

Abstract: A1 Measuring the effectiveness of a novel CPRcard feedback device during simulated chest compressions by non-healthcare workers Alexander White*, Han Xian Ng, Wai Yee Ng, Eileen Kai Xin Ng, Stephanie Fook-Chong, Phek Hui Jade Kua, Marcus Eng Hock Ong Health Services Research, Division of Research, Singapore General Hospital, Singapore, Singapore; London School of Medicine, London, UK; Unit for Pre-hospital Emergency Care Clinical Support Team, Singapore General Hospital, Singapore, Singapore; Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore, Singapore; Department of Emergency Medicine, KK Women’s and Children’s Hospital Singapore, Singapore, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore; Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore, Singapore Correspondence: Alexander White (alexander.elgin.white@sgh.com.sg) Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 2017, 25(Suppl 1):A1

Removing piston-driven mechanical chest compression artefacts from the ECG

Abstract: Piston-driven mechanical chest compression (CC) devices induce a quasi-periodic artefact in the ECG, making rhythm diagnosis unreliable. Data from 230 out-of-hospital cardiac arrest (OHCA) patients were collected in which CCs were delivered using the piston driven LUCAS-2 device. Underlying rhythms were annotated by expert reviewers in artefact-free intervals. Two artefact removal methods (filters) were introduced: a static solution based on Goertzel's algorithm, and an adaptive solution based on a Recursive Least Squares (RLS) filter. The filtered ECG was diagnosed by a shock/no-shock decision algorithm used in a commercial defibrillator and compared with the rhythm annotations. Filter performance was evaluated in terms of balanced accuracy (BAC), the mean of sensitivity (shockable) and specificity (nonshockable). Compared to the unfiltered signal, the static filter increased BAC by 20 points, and the RLS filter by 25 points. Adaptive filtering results in 99.0% sensitivity and 87.3% specificity.

Abstract 18119: Mechanical Active Compression-Decompression With LUCAS2AD Provide Equivocal pEtCO2 Values to the Standard LUCAS2 During Out-of-Hospital Cardiopulmonary Resuscitation

Abstract: Introduction: Studies comparing manual active compression-decompression CPR (ACD-CPR) to manual CPR have shown similar survival rates. Recently a new mechanical ACD-CPR device (LUCAS2AD, Jolife AB/...

22 Out-of-hospital cardiac arrest as a mandatory reportable disease – first experiences from norway

Abstract: Aim Survival after cardiac arrest (CA) depends on the time-critical interventions summarised in the chain of survival –identification and alarming, cardiopulmonary resuscitation (CPR), defibrillation (if appropriate), and standardised post-arrest care. Voluntary, population based CA-registries have indicated significant improvements in survival associated with improved performance. Systematic improvement is based on repeated cycles of; measure to identify weakness, interventions to improve, and measure again to verify changes and effects. Strengthening CA-registries by making CA a mandatory reportable disease enables implementation. Methods Norway has a population of ~5.2 million. The Norwegian Cardiac Arrest Registry (NorCAR) restarted in 2013 with mandatory reporting in collaboration with Norwegian Cardiovascular Disease Registry. We measured “coverage” as the percentage of the Norwegian population served by the reporting EMS, and report incidence and survival rates per 100 000 person-years. Results Out of the 19 EMS health trusts in Norway, the number reporting to NorCAR (coverage) increased from 8 (47%) in 2013, to 17 (92%) in 2015, and by 2017 all EMS health trust are reporting. Incidence rates of ambulance-treated CA have increased: 41, 44, 48, and 51. Thirty-day survival rates from all-cause out-of-hospital CA in 2013, 2014, and 2015 were: 7.7 (19%), 5.9 (14%), 7.3 (15%), respectively. For first 2/3 of 2016 numbers are 6.8 (13%). Conclusion Establishing mandatory reporting is valuable when creating a population based registry. Regional variations inspire further work to improve reporting and quality. Close involvement of the local registrars and feeding back the results to local EMS are our main strategies. Conflict of interest None declared. Funding None declared.