Àá½Ã¸¸ ±â´Ù·Á ÁÖ¼¼¿ä. ·ÎµùÁßÀÔ´Ï´Ù.
KMID : 1201420200130010049
Journal of Neurocritical Care
2020 Volume.13 No. 1 p.49 ~ p.56
Predicting parenchymal hematoma associated with endovascular thrombectomy for acute occlusion of anterior circulation large vessel: the GuEss-MALiGn scale
Kim Ju-Hyeon

Kim Chang-Hun
Kang Jong-Soo
Kwon Oh-Young
Abstract
Background: Endovascular thrombectomy (EVT) is an emergency treatment for stroke caused by anterior circulation large vessel occlusion (ACLVO). This study aimed to identify the predictors for post-EVT parenchymal hematoma (PH) and to develop a predictive tool using the identified factors.

Methods: Using the clinical and imaging data of consecutive patients with acute ACLVO who underwent EVT, we performed a multivariate binary logistic regression analysis to identify predictors for PH. With the predictors proved by the regression, we developed a scale for predicting PH using receiver operating characteristic (ROC) curve analyses.

Results: In 233 enrolled patients, the mean age was 72.3 years old, and the male proportion was 46.4%. The rate of PH after EVT was 18.0%: the rate of type 1 PH was 12.9%, and the rate of type 2 PH was 5.2%. The significant predictors for PH were basal ganglia involvement, embolism, male sex, antihyperlipidemic use, lobar infarction, and serum glucose level. We developed the GuEss-MALiGn scale with the six significant predictors. Each of these six items was placed on a Likert scale and scored as a 0 or 1. The ROC curve analysis revealed that the area under the curve was 0.771. The cutoff score for the risk of PH was >3. The sensitivity was 59.5%, and the specificity was 78.0%.

Conclusion: We propose the GuEss-MALiGn scale as a tool for predicting PH associated with EVT. Future external validation is needed to determine the reliability of this scale.
KEYWORD
Cerebral infarction, Middle cerebral artery, Thrombectomy, Endovascular procedures, Postoperative complications, Cerebral hemorrhage
FullTexts / Linksout information
 
Listed journal information
ÇмúÁøÈïÀç´Ü(KCI) ´ëÇÑÀÇÇÐȸ ȸ¿ø