Abstract
BACKGROUND Telemedicine stroke networks are mandatory to provide inter-hospital transfer for mechanical thrombectomy (MT). However, studies on patient selection in daily practice are sparse. METHODS Here, we analyzed consecutive patients from 01/2014 to 12/2018 within the supraregional stroke network \textquotedblNeurovascular Network of Southwest Bavaria\textquotedbl (NEVAS) in terms of diagnoses after consultation, inter-hospital transfer and predictors for performing MT. Degree of disability was rated by the modified Rankin Scale (mRS), good outcome was defined as mRS ≤ 2. Successful reperfusion was assumed when the modified thrombolysis in cerebral infarction (mTICI) was 2b-3. RESULTS Of 5722 telemedicine consultations, in 14.1% inter-hospital transfer was performed, mostly because of large vessel occlusion (LVO) stroke. A total of n = 350 patients with LVO were shipped via NEVAS to our center for MT. While n = 52 recanalized spontaneously, MT-treatment was performed in n = 178 patients. MT-treated patients had more severe strokes according to the median National institute of health stroke scale (NIHSS) (16 vs. 13, p < 0.001), were more often treated with intravenous thrombolysis (64.5% vs. 51.7%, p = 0.026) and arrived significantly earlier in our center (184.5 versus 228.0~min, p < 0.001). Good outcome (27.5% vs. 30.8%, p = 0.35) and mortality (32.6% versus 23.5%, p = 0.79) were comparable in MT-treated versus no-MT-treated patients. In patients with middle cerebral artery occlusion in the M1 segment or carotid artery occlusion good outcome was twice as often in the MT-group (21.8% vs. 12.8%, p = 0.184). Independent predictors for performing MT were higher NIHSS (OR 1.096), higher ASPECTS (OR 1.28), and earlier time window (OR 0.99). CONCLUSION Within a telemedicine network stroke care can successfully be organized as only a minority of patients has to be transferred. Our data provide clinical evidence that all MT-eligible patients should be shipped with the fastest transportation modality as possible.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Medizin > Munich Cluster for Systems Neurology (SyNergy) |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
URN: | urn:nbn:de:bvb:19-epub-75766-4 |
Sprache: | Englisch |
Dokumenten ID: | 75766 |
Datum der Veröffentlichung auf Open Access LMU: | 05. Mai 2021, 13:05 |
Letzte Änderungen: | 13. Jun. 2024, 13:13 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198 |