The establishment of early warning systems in hospitals was strongly recommended in recent guidelines to detect deteriorating patients early and direct them to adequate care. Upon reaching predefined trigger criteria, Medical Emergency Teams (MET) should be notified and directed to these patients. The present study analyses the effect of introducing an automated multiparameter early warning score (MEWS)-based early warning system with paging functionality on 2 wards hosting patients recovering from highly complex surgical interventions.
The deployment of the system was accompanied by retrospective data acquisition during 12 months (intervention) using 4 routine databases: Hospital patient data management, anesthesia database, local data of the German Resuscitation Registry, and measurement logs of the deployed system (intervention period only). A retrospective 12-month data review using the same aforementioned databases before the deployment of the system served as control. Control and intervention phases were separated by a 6-month washout period for the installation of the system and for training.
Data from 3827 patients could be acquired from 2 surgical wards during the two 12-month periods, 1896 patients in the control and 1931 in the intervention cohorts. Patient characteristics differed between the 2 observation phases. American Society of Anesthesiologists risk classification and duration of surgery as well as German DRG case-weight were significantly higher in the intervention period. However, the rate of cardiac arrests significantly dropped from 5.3 to 2.1 per 1000 admissions in the intervention period (P < 0.001). This observation was paralleled by a reduction of unplanned ICU admissions from 3.6% to 3.0% (P < 0.001), and an increase of notifications of critical conditions to the ward surgeon. The primary triggers for MET activation were abnormal ECG alerts, specifically asystole (n = 5), and pulseless electric activity (n = 8).
In concert with a well-trained and organized MET, the early deterioration detection of patients on surgical wards outside the ICU may be improved by introducing an automated MEWS-based early warning system with paging functionality.
*Department of Anesthesiology and Intensive Care, Medizinische Fakultät an der TU-Dresden, Germany
†Department of Visceral, Thoracic, and Vascular Surgery, Medizinische Fakultät an der TU-Dresden, Germany.
Reprints: Axel R. Heller, MD, MBA, Klinik für Anästhesiologie und Intensivtherapie, Universitätsklinikum Dresden, Fetscherstr. 74, 01307 Dresden, Germany. E-mail: Axel.Heller@ukdd.de.
The IGS system hardware was granted over the study period by Philips, Hamburg, Germany.
Present data is part of the doctoral thesis of Benjamin Lauterwald. The authors thank the nurses and surgeons on the wards VTG-S1 and VTG-S2, as well as the Medical Emergency Team of the University Hospital Dresden. Carolin Eisold and Doris Sommer trained the staff on the wards. Setup of the IGS system, hardware maintenance as well as data acquisitions and management, was generously supported by Maic Regner, André Kühn, Michael Bertram and the IT Department of the University Hospital Dresden. ORBIS patient data Management was supported by Jörg Müller of the Controlling Dept. The anesthesia database was handled by Heiko Weigelt and Jens Helbig. German Resuscitation Registry database management was done by Sigrid Brenner. The initial idea was supported by Michael P. Müller, German Resuscitation Council, Freiburg, Germany.
The authors report no conflicts of interest.
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