Inter-Rater Agreement on Cincinnati Prehospital Stroke Scale (CPSS) and Prehospital Acute Stroke Severity Scale (PASS) Between EMS Providers, Neurology Residents and Neurology Consultants

Objective

To examine the agreement between emergency medical service (EMS) providers, neurology residents and neurology consultants, using the Cincinnati Prehospital Stroke Scale (CPSS) and the Prehospital Acute Stroke Severity Scale (PASS).

Methods

Patients with stroke, transient ischemic attack (TIA) and stroke mimic were included upon primary stroke admission or during rehabilitation. Patients were included from June 2018 to September 2019. Video recordings were made of patients being assessed with CPSS and PASS. The recordings were later presented to the healthcare professionals. To determine relative and absolute interrater reliability in terms of inter-rater agreement (IRA), we used generalisability theory. Group-level agreement was determined against a gold standard and presented as an area under the curve (AUC). The gold standard was a consensus agreement between two neurology consultants.

Results

A total of 120 patient recordings were assessed by 30 EMS providers, two neurology residents and two neurology consultants. Using the CPSS and the PASS, a total of 1,800 assessments were completed by EMS providers, 240 by neurology residents and 240 by neurology consultants. The overall relative and absolute IRA for all items combined from the CPSS and PASS score was 0.84 (95% CI 0.80; 0.87) and 0.81 (95% CI 0.77; 0.85), respectively. Using the CPSS, the agreement on a group-level resulted in AUCs of 0.83 (95% CI 0.78; 0.88) for the EMS providers and 0.86 (95% CI 0.82; 0.90) for the neurology residents when compared with the gold standard. Using the PASS, the AUC was 0.82 (95% CI 0.77; 0.87) for the EMS providers and 0.88 (95% CI 0.84; 0.93) for the neurology residents.

Conclusion

The high relative and absolute inter-rater agreement underpins a high robustness/generalisability of the two scales. A high agreement exists across individual raters and different groups of healthcare professionals supporting widespread applicability of the stroke scales.

Geographic information system data from ambulances applied in the emergency department: effects on patient reception

BACKGROUND

Emergency departments (ED) recognize crowding and handover from prehospital to in-hospital settings to be major challenges. Prehospital Geographical Information Systems (GIS) may be a promising tool to address such issues. In this study, the use of prehospital GIS data was implemented in an ED in order to investigate its effect on 1) wait time and unprepared activations of Trauma Teams (TT) and Medical Emergency Teams (MET) and 2) nurses' perceptions regarding patient reception, workflow and resource utilization.

METHODS

INTERVENTION

From May 1st 2014 to October 31th 2014, GIS data was displayed in the ED. Data included real-time estimated time of arrival, distance to ED, dispatch criteria, patient data and ambulance contact information. Data was used by coordinating nurses for time activation of TT and MET involved in the initial treatment of severely-injured or critically-ill patients. In addition, it was used as a logistics tool for handling all other patients transported by ambulance to the ED.

STUDY DESIGN

The study followed a mixed-methods design, consisting of a quantitative study (before and after intervention) and a qualitative study (survey and interviews).

PARTICIPANTS

Participants included all patients received by TT or MET and coordinating nurses in the ED.

RESULTS

1.) Quantitative: 599 patients were included. The median wait time for TT and MET was 5 min both before and after the GIS intervention, showing no difference (p = 0.18). A significant reduction in the subgroup of waits >10 min was found (p < 0.05). No difference was found in unprepared TT and MET activations. 2.) Qualitative: Nurses perceived GIS data as a tool to optimize resource utilization and quality of all patients' reception, critically or non-critically ill. No substantial disadvantages were reported.

DISCUSSION

The contradiction of measured median wait time and nurses perceived improved timing of team activation may result from having both RT- ETA and supplemental patient information not only for seriously-injured or critically-ill patients received by the TT and MET, but for all patients transported by ambulance. The reduction in waits > 10 minutes may have contributed to the overall perception of reduced wait time, as avoidance of long waits is clinically more important than reduction in the median wait time.

CONCLUSION

A comparison of the use of prehospital GIS data in the ED with the control period showed no effect on median wait time for TT and MET, however, the number of waits of >10 min was reduced. On the other hand, nurses perceived implementation of GIS data as improving workflow, resource utilization and quality of all patients' reception, critically as well as non-critically ill. There were no substantial disadvantages to the GIS application.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT02188966).