I63
Conditions
Interventions
Group 1: Conservative patients with suspected stroke and referred by the emergency services as well as all patients with a discharge diagnosis of stroke.
An expanded score for the detection of strokes
Sponsors
Justus-Liebig-Universität Gießen
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Inclusion criteria
Inclusion criteria: Patients who are referred to the emergency room with suspected stroke in one of the participating clinics. These patients receive a patient allocation code (PZC) from the emergency services, which they use to register electronically at the destination clinic. There are 3 codes for stroke, depending on the onset of symptoms. All patients with these codes are recorded: 421/422/423 (“Apoplexy/TIA/Hemorrhage”) In order to record patients who are not assigned as having a stroke but were diagnosed with a stroke during diagnostics, all patients with the corresponding discharge diagnosis are also recorded. For the retrospective control group, the identification of patients is carried out in the same way by screening the patients who were suspected of having a stroke in the emergency room during this time and after the discharge diagnosis. The retrospective data collection will cover the year 2023.
Exclusion criteria
Exclusion criteria: none
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| With FAST4D, more stroke patients are recognized by the emergency services than with FAST. The misassignment rate is decreasing. The time from acquisition to imaging is shortened. | — |
Secondary
| Measure | Time frame |
|---|---|
| With FAST4D, more patients will receive recanalizing therapy. FAST4D shortens the door-to-groin time (through direct allocation) and the door-to-needle time. A FAST4D score >4 is indicative of a vascular occlusion, which can be treated endovascularly. Through FAST4D, more patients with endovascular treatable vascular occlusions are referred directly to a center. FAST4D does not lead to an extension of supply times at the deployment site or transport time. Assessment of the individual FAST4D items with regard to their prediction probability or their combined significance. Validation of FAST4D on a larger collective. Potential optimization by modifying the items with different weights. Comparison of different implementation methods (purely written, additionally via video and additionally via further training). | — |
Countries
Germany
Contacts
Public ContactChristian Claudi
Justus-Liebig-Universität Gießen
Outcome results
None listed