Detection of Deteriorating Patients on Peripheral Surgical Wards by an Automated Notification System
Conditions: Safety Issues, Emergencies
Sponsor: University Hospital Carl Gustav Carus

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Study protocol and statistical analysis plan 
 
Title: 
Detection of deteriorating patients on peripheral surgical wards by an automated notification system 
 
 
Protocol code: DrePaSiNet 
Dresdner Patienten Sicherheits- Netzwerk   
 
ClinicalTrials.gov ID: not yet assigned (by 2018-03-04) 
IRB approval: EK DD 291072016 
 
Protocol version: V1.0  
Protocol date: 2015-01-01 
 
 
Responsible Party: 
 
Prof. Dr. med. Axel R. Heller DEAA, MBA 
Klinik für Anästhesiologie und Intensivtherapie 
Universitätsklinikum Dresden 
Fetscherstr. 74 
01307 Dresden, Germany 
Axel.Heller@ukdd.de 

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Data collection 
After approval of the institutional review board (EK DD 291072016) data from four clinical routine 
databases were collected and analyzed over two 12 months periods. Individual informed consent 
was waived by the IRB. 
On 2016-07-01 a Multiparameter Early Warning Score (MEWS) – based vital monitoring and paging 
system (Intelligent Guardian Solution (IGS), Phillips, Hamburg, Germany) was deployed on two 
surgical wards with 56 beds in total. Medical staff received intense training on the handling of the IGS 
before deployment over two months. 
All measurements from the monitors were transmitted via a wireless local area network to a central 
server connected to the hospital data network and to a paging server (Digitaler Anruf- und 
Kommunikationsserver (DAKS), Siemens, Erlangen, Germany) for automated notification of the 
attending surgeon or the MET. All notifications were given via text message on custom DECT phones, 
including room, and bed number, as well as the present MEWS score and the time over which it 
changed. 
Handling protocol 
The handling and escalation/ de-escalation protocol for measurement intervals and devices is 
displayed in Figure 1.  
Text prompts on the spotcheck monitors directly advised the nurses according to the handling 
protocol. According to the protocol the density of measurements was adapted to the patient’s 
condition. When the MEWS reached 7 or higher the surgical registrar was automatically paged by the 
system to take care for the deteriorating patient. It was to his discretion to call the MET if necessary. 
In case of an obviously life threatening condition, the MET could directly be called by a button on the 
monitor.  

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Figure 1 Flowchart for patient condition- adapted measurement, handling of IGS components, and 
communication. 
Database management 
Deployment of the IGS was accompanied by data acquisition in four routine databases from 2016-07-
01 to 2017-06-30. The complete year of 2015 served as control period. Data on length of stay, ICU 
admissions, age, gender, survival, G-DRG- codes and case weight originate from the hospital 
information system (ORBIS, AGFA HealthCare, Bonn, Germany). Data on comorbidities derive from 
the anesthesia information system (ANDOK, DATAPEC, Pliezhausen, Germany). The local dataset of 
the German Resuscitation registry provided all data recorded during the MET missions with or 
without cardiac arrest. In the intervention phase the IGS database (Philips, Hamburg, Germany) 
provided all data on MEWS and individual physiologic parameters of all measurements. Aggregation 
of databases was conducted by use of the key variable “FALLNUMMER” which was part of each 
dataset.  

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Statistical analysis 
Data are presented as absolute number of patients with respective percentage per group or as 
parameter mean ± SD. For comparisons of interval-scaled variables between the observation periods, 
two tailed, unpaired t-tests were performed. Levene’s test was used to check these variables for 
Gaussian distribution. Nonparametric between group testing was achieved with two tailed Mann- 
Whitney U- test. Additionally, the chi-square test or Fisher’s exact test was applied to nominal-scaled 
data. To address the fact that patient condition was significantly poorer during the intervention 
period e.g. in terms of DRG- case weight, comorbidity, and surgical complexity as indicated by 
duration of surgery, statistical comparisons between the observation intervals were completed 
regarding the respective G-DRG case weight as a statistical confounder.  
SPSS software (IBM SPSS statistics, version 24.0.0.1, Armonk, NY, USA) was used for all calculations. 
In all statistical comparisons, significance was accepted with an error probability of p < 0.05.