期刊
STROKE
卷 47, 期 9, 页码 2409-2412出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1161/STROKEAHA.116.014181
关键词
endovascular procedures; fibrinolysis; incidence; meta-analysis; stroke; thrombectomy
资金
- Journees de Neurologie de Langue Francaise
- Intramural Research Program of the NIH
Background and Purpose After the demonstration of efficacy of bridging therapy, reliably predicting early recanalization (ER; 3 hours after start of intravenous thrombolysis) would be essential to limit futile, resource-consuming, interhospital transfers. We present the first systematic review on the incidence and predictors of ER after intravenous thrombolysis alone. Methods We systematically searched for studies including patients solely treated by intravenous thrombolysis that reported incidence of ER and its association with baseline variables. Using meta-analyses, we estimated pooled incidence of ER, including according to occlusion site, and summarized the available evidence regarding predictors of no-ER. Results We identified 26 studies that together included 2063 patients. The overall incidence of partial or complete ER was 33% (95% confidence interval, 27-40). It varied according to occlusion site: 52% (39-64) for distal middle cerebral artery, 35% (28-42) for proximal middle cerebral artery, 13% (6-22) for intracranial carotid artery, and 13% (0-35) for basilar occlusion. Corresponding rates for complete ER were 38% (22-54), 21% (15-29), 4% (1-8), and 4% (0-22), respectively. Proximal occlusion and higher National Institute of Health Stroke Scale were the most consistent no-ER predictors. Other factors, such as long or totally occlusive thrombus and poor collateral circulation, emerged as potential predictors but will need confirmation. Conclusion The overall incidence of ER after intravenous thrombolysis is substantial, highlighting the importance of reliably predicting ER to limit futile, interhospital transfers. Incidence of no-ER is particularly high for proximal occlusion and severe strokes. Given the scarcity of published data, further studies are needed to improve no-ER prediction accuracy.
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