Safety of inter-hospital transfer of patients with acute ischemic stroke for evaluation of endovascular thrombectomy.
Lars-Peder PallesenSimon WinzerKristian BarlinnAlexandra PrakapeniaTimo SiepmannCosima GruenerJohannes C GerberKevin HaedrichJennifer LinnJessica BarlinnVolker PuetzPublished in: Scientific reports (2020)
Stroke networks facilitate access to endovascular treatment (EVT) for patients with ischemic stroke due to large vessel occlusion. In this study we aimed to determine the safety of inter-hospital transfer and included all patients with acute ischemic stroke who were transferred within our stroke network for evaluation of EVT between 06/2016 and 12/2018. Data were derived from our prospective EVT database and transfer protocols. We analyzed major complications and medical interventions associated with inter-hospital transfer. Among 615 transferred patients, 377 patients (61.3%) were transferred within our telestroke network and had transfer protocols available (median age 76 years [interquartile range, IQR 17], 190 [50.4%] male, median baseline NIHSS score 17 [IQR 8], 246 [65.3%] drip-and-ship i.v.-thrombolysis). No patient suffered from cardio-respiratory failure or required emergency intubation or cardiopulmonary resuscitation during the transfer. Among 343 patients who were not intubated prior departure, 35 patients (10.2%) required medical interventions during the transfer. The performance of medical interventions was associated with a lower EVT rate and higher mortality at three months. In conclusion, the transfer of acute stroke patients for evaluation of EVT was not associated with major complications and transfer-related medical interventions were required in a minority of patients.
Keyphrases
- acute ischemic stroke
- end stage renal disease
- healthcare
- ejection fraction
- chronic kidney disease
- newly diagnosed
- peritoneal dialysis
- cardiac arrest
- emergency department
- cardiopulmonary resuscitation
- physical activity
- prognostic factors
- atrial fibrillation
- public health
- type diabetes
- patient reported outcomes
- cardiovascular disease
- risk factors
- big data
- machine learning
- deep learning
- brain injury
- case report
- coronary artery disease
- subarachnoid hemorrhage
- electronic health record
- electron transfer