Showing papers by "Jo Kramer-Johansen published in 2016"

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.

Abstract: Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily assessed using Holter recordings from public electrocardiogram (ECG) databases, which may be different from the ECG seen during OHCA events. This study evaluates VF-detection using data from both OHCA patients and public Holter recordings. ECG-segments of 4-s and 8-s duration were analyzed. For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate algorithm performance in terms of sensitivity (Se), specificity (Sp) and balanced error rate (BER). Performance was significantly better for public data with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of 94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times more features than the data from public databases for an accurate detection (6 vs 3). No significant differences in performance were found for different segment lengths, the BER differences were below 0.5-points in all cases. Our results show that VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s.

Radiological assessment of chest compression point and achievable compression depth in cardiac patients.

Abstract: Using magnetic resonance imaging (MRI) to relate cardiovascular structures to surface anatomy in a population relevant to cardiac arrest victims, relate the external thoracic anterior-posterior (AP) diameter (APEXTERNAL) and blood-filled structures to recommended chest compression depths, and define an optimal compression point (OCP). MRI axial scans of referred patients were analysed. We defined origo as the skin surface of the centre of sternum in the internipple line. The blood-filled structures beneath origo were identified and the sum of their inner diameters (APBLOOD) and APEXTERNAL were measured. We defined OCP based on the image with maximum compressible left and right ventricle and where LVOT was not present. We measured the distance from origo to OCP. Consecutive patients, mean (SD), age 52 (17) years, 110 (76 %) males, were categorized: cardiac disease (n = 74), aortic disease (n = 13), no findings/study patient (included in another study) (n = 57). The structure LVOT/aortic valve (AV)/aortic root was present in 46 % of patients with cardiac disease vs. 19 % of patients with no findings. APEXTERNAL for males and females was 25 (2) cm and 22 (2) cm, and APBLOOD 6.5 cm (2) and 4.7 cm (2), respectively. Distance from origo to OCP was 32 (11) mm to the left and 16 (21) mm caudally. LVOT/AV/aortic root was present beneath the origo in almost half the patients with cardiac disease. Recommended chest compression depths exceeded the anterior-posterior diameter of blood-filled structures in more than half of the females. OCP was found 3 cm left of the origo. Based on our study, individualized compression point and depth could be further studied in a prospective, clinical study.

Does the Norwegian emergency medical dispatch classification as non-urgent predict no need for pre-hospital medical treatment? An observational study

Abstract: The number of ambulance call-outs in Norway is increasing owing to societal changes and increased demand from the public. Together with improved but more expensive education of ambulance staff, this leads to increased costs and staffing shortages. We wanted to study whether the current dispatch triage tools could reliably identify patients who only required transport, and not pre-hospital medical care. This could allow selection of such patients for designated transport units, freeing up highly trained ambulance staff to attend patients in greater need. A cross-sectional observational study was used, drawing on all electronic and paper records in our ambulance service from four random days in 2012. The patients were classified into acuity groups, based on Emergency Medical Dispatch codes, and pre-hospital interventions were extracted from the Patient Report Forms. Of the 1489 ambulance call-outs included in this study, 82 PRFs (5 %) were missing. A highly significant association was found between acuity group and recorded pre-hospital intervention (p ≤ 0.001). We found no correlation between gender, distance to hospital, age and pre-hospital interventions. Ambulances staffed by paramedics performed more interventions (234/917, 26 %) than those with emergency medical technicians (42/282, 15 %). The strongest predictor for needing pre-hospital interventions was found to be the emergency medical dispatch acuity descriptor. This study has demonstrated that the Norwegian dispatch system is able to correctly identify patients who do not need pre-hospital interventions. Patients with a low acuity code had a very low level of pre-hospital interventions. Evaluation of adherence to protocol in the Emergency Medical Dispatch is not possible due to the inherent need for medical experience in the triage process. This study validates the Norwegian dispatch tool (Norwegian index) as a predictor of patients who do not need pre-hospital interventions.

Prospective cohort study of change of practice from Manual to AED-mode defibrillation in Oslo and Akershus ambulance service

Abstract: Author affiliations: Henning Naas, University of Oslo, Oslo, Norway Andres Neset, Department of medicine, Stavanger University Hospital, Stavanger, Norway Christian Johansson, Division of Prehospital Services, Oslo University Hospital, Oslo, Norway Anne-Cathrine Braarud, Division of Prehospital Services, Oslo University Hospital, Oslo, Norway Tomas Drægni, Division of Emergencies and Critical Care, Oslo University Hospital, Oslo, Norway Jo Kramer-Johansen*, Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS) and Division of Prehospital Services, Oslo University Hospital and University of Oslo, Oslo, Norway