Rettungsdienststrukturen neu denken – Ergebnisse der Expertenworkshops „Logistik in der präklinischen Notfallversorgung“ [Rethinking emergency medical services (EMS)—results of an interdisciplinary expert panel on logistics in EMS]

Die rettungsdienstliche Struktur in Deutschland stellt eine Versorgung auf sehr hohem Niveau sicher. Dennoch ist es notwendig, die vorhandenen Strukturen zu überdenken und für die Zukunft zu härten. Nicht nur vor dem Hintergrund stetig steigender Einsatzzahlen, sondern auch wegen der Herausforderungen der Personalgewinnung und der Alterung der Bevölkerung sollten Reformen im Rettungsdienst dringend angegangen werden. Hier kann der Rettungsdienst viel von der Mathematik und gerade vom Bereich „operations research“ lernen. Dieser Fachbereich beschäftigt sich explizit mit der Verbesserung von logistischen Herausforderungen, die der Rettungsdienst ohne Frage ist. In der vorliegenden Arbeit berichten die Autorinnen und Autoren über die ersten Ergebnisse zweier Workshops zum Thema „Logistik in der präklinischen Versorgung“ und möchten damit die Diskussion im Rettungsdienst auf breiter Basis anregen sowie Verbesserungspotenziale und Herausforderungen für die verschiedenen Akteure in der präklinischen Behandlung herausarbeiten, aber auch erste Ideen zu Lösungsansätzen liefern

Differences between manual CPR and corpuls cpr in regard to quality and outcome: study protocol of the comparing observational multi‐center prospective registry study on resuscitation (COMPRESS)

Background!#!The effect of mechanical CPR is diversely described in the literature. Different mechanical CPR devices are available. The corpuls cpr is a new generation of piston-driven devices and was launched in 2015. The COMPRESS-trial analyzes quality of chest compression and CPR-related injuries in cases of mechanical CPR by the corpuls cpr and manual CPR.!##!Methods!#!This article describes the design and study protocol of the COMPRESS-trial. This observational multi-center study includes all patients who suffered an out-of-hospital cardiac arrest (OHCA) where CPR is attempted in four German emergency medical systems (EMS) between January 2020 and December 2022. EMS treatment, in-hospital-treatment and outcome are anonymously reported to the German Resuscitation Registry (GRR). This information is linked with data from the defibrillator, the feedback system and the mechanical CPR device for a complete dataset. Primary endpoint is chest compression quality (complete release, compression rate, compression depth, chest compression fraction, CPR-related injuries). Secondary endpoint is survival (return of spontaneous circulation (ROSC), admission to hospital and survival to hospital discharge). The trial is sponsored by GS Elektromedizinische Geräte G. Stemple GmbH.!##!Discussion!#!This observational multi-center study will contribute to the evaluation of mechanical chest compression devices and to the efficacy and safety of the corpuls cpr.!##!Trial registration!#!DRKS, DRKS-ID DRKS00020819 . Registered 31 July 2020

Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest

Objective: During cardiac arrest treatment, a reliable detection of spontaneous circulation, usually performed by manual pulse checks, is both vital for patient survival and practically challenging. Methods: We developed a machine learning algorithm to automatically predict the circulatory state during cardiac arrest treatment from 4-second-long snippets of accelerometry and electrocardiogram data from real-world defibrillator records. The algorithm was trained based on 917 cases from the German Resuscitation Registry, for which ground truth labels were created by a manual annotation of physicians. It uses a kernelized Support Vector Machine classifier based on 14 features, which partially reflect the correlation between accelerometry and electrocardiogram data. Results: On a test data set, the proposed algorithm exhibits an accuracy of 94.4 (93.6, 95.2)%, a sensitivity of 95.0 (93.9, 96.1)%, and a specificity of 93.9 (92.7, 95.1)%. Conclusion and significance: In application, the algorithm may be used to simplify retrospective annotation for quality management and, moreover, to support clinicians to assess circulatory state during cardiac arrest treatment.Comment: 15 pages, 10 figure