scholarly journals Perfusion Parameter Thresholds That Discriminate Ischemic Core Vary with Time from Onset in Acute Ischemic Stroke

2020 ◽  
Vol 41 (10) ◽  
pp. 1809-1815
Author(s):  
T. Yoshie ◽  
Y. Yu ◽  
H. Jiang ◽  
T. Honda ◽  
H. Trieu ◽  
...  
Neurology ◽  
2020 ◽  
pp. 10.1212/WNL.0000000000011258
Author(s):  
Longting Lin ◽  
Jianhong Yang ◽  
Chushuang Chen ◽  
Huiqiao Tian ◽  
Andrew Bivard ◽  
...  

ObjectiveTo test the hypothesis that acute ischemic patients with poorer collaterals would have faster ischemic core growth, we included 2 cohorts in the study, cohort 1 of 342 patients for derivation and cohort 2 of 414 patients for validation purpose.MethodsAcute ischemic stroke patients with large vessel occlusion were included. Core growth rate was calculated by the following equation: Core growth rate = Acute core volume on CTP/Time from stroke onset to CTP. Collateral status was assessed by the ratio of severe hypoperfusion volume within the hypoperfusion region of CTP. The CTP collateral index was categorized in tertiles; for each tertile, core growth rate was summarized as median and inter-quartile range. Simple linear regressions were then performed to measure the predictive power of CTP collateral index in core growth rate.ResultsFor patients allocated to good collateral on CT perfusion (tertile 1 of collateral index), moderate collateral (tertile 2), and poor collateral (tertile 3), the median core growth rate was 2.93 mL/h (1.10–7.94), 8.65 mL/h (4.53–18.13), and 25.41 mL/h (12.83–45.07) respectively. Increments in the collateral index by 1% resulted in an increase of core growth by 0.57 mL/h (coefficient = 0.57, 95% confidence interval = [0.46, 0.68], p < 0.001). The relationship of core growth and CTP collateral index was validated in cohort 2. An increment in collateral index by 1% resulted in an increase of core growth by 0.59 mL/h (coefficient = 0.59 [0.48–0.71], p < 0.001) in cohort 2.ConclusionCollateral status is a major determinant of ischemic core growth.


Stroke ◽  
2021 ◽  
Vol 52 (2) ◽  
Author(s):  
Gabriel Broocks ◽  
Jens Minnerup ◽  
Rosalie McDonough ◽  
Fabian Flottmann ◽  
Andre Kemmling

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Adam H de Havenon ◽  
Eva Mistry ◽  
Mohammad Anadani ◽  
Shadi Yaghi ◽  
Farhaan Vahidy ◽  
...  

Background: Research has shown that increased blood pressure variability (BPV) correlates with worse outcome after stroke. However, the mechanism is unknown. Methods: In this secondary analysis of the Albumin in Acute Ischemic Stroke (ALIAS) trial, we calculated BPV for SBP over the first 120 hours. The primary outcome was 90-day modified Rankin Scale of 2-6 (unfavorable outcome). The secondary outcome was difference between the CT ASPECTS at baseline and 24 hours. We fit regression models to the outcomes and used the Baron and Kenny method to estimate causal mediation effects. Results: We included 508 patients with a mean (SD) age of 64.3 (12.3) years, 56.1% male, median NIHSS of 11, and mean SBP measurements of 19.8. Unfavorable outcome was seen in 309 (60.8%). BPV was significantly higher in patients with unfavorable outcome (Table 1). In adjusted models, increased BPV was independently associated with unfavorable outcome (Table 2) and ASPECTS decline (Table 3). Mediation analysis revealed that ASPECTS decline accounted for 23.5% of the effect of BPV on outcome, with 16.4% as direct effect. Conclusion: We found that increased BPV was associated with both unfavorable outcome and growth of the ischemic core. Future prospective studies are needed to establish causality and confirm BPV’s effect on stroke recovery.


Stroke ◽  
2020 ◽  
Vol 51 (10) ◽  
pp. 3147-3155 ◽  
Author(s):  
Mayank Goyal ◽  
Johanna M. Ospel ◽  
Bijoy Menon ◽  
Mohammed Almekhlafi ◽  
Mahesh Jayaraman ◽  
...  

Endovascular treatment is a highly effective therapy for acute ischemic stroke due to large vessel occlusion and has recently revolutionized stroke care. Oftentimes, ischemic core extent on baseline imaging is used to determine endovascular treatment-eligibility. There are, however, 3 fundamental issues with the core concept: First, computed tomography and magnetic resonance imaging, which are mostly used in the acute stroke setting, are not able to precisely determine whether and to what extent brain tissue is infarcted (core) or still viable, due to variability in tissue vulnerability, the phenomenon of selective neuronal loss and lack of a reliable gold standard. Second, treatment decision-making in acute stroke is multifactorial, and as such, the relative importance of single variables, including imaging factors, is reduced. Third, there are often discrepancies between core volume and clinical outcome. This review will address the uncertainty in terminology and proposes a direction towards more clarity. This theoretical exercise needs empirical data that clarify the definitions further and prove its value.


Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Andria L Ford ◽  
Hongyu An ◽  
Katie D Vo ◽  
William J Powers ◽  
Weili Lin ◽  
...  

Background: Early reperfusion is associated with improved clinical outcome in acute ischemic stroke; however, there is no consensus regarding which perfusion parameter may best serve as a marker of clinical improvement. We compared three commonly used MRI perfusion parameters, mean transit time (MTT), time-to-peak (TTP), and Tmax, to identify which method of measuring reperfusion best predicted clinical improvement. Methods: Acute ischemic stroke patients underwent two MR scans: within 4.5 hours (tp1) and at 6 hours (tp2) after stroke onset. Co-registered MTT, TTP, and Tmax maps were generated to measure regions of perfusion deficit at tp1 and tp2. Perfusion deficit was defined as prolongation of MTT, TTP, or Tmax beyond four pre-specified thresholds for each parameter (4 thresholds were chosen to ensure results were not spuriously based on one threshold). Commonly-used thresholds (relative to contralateral median) were selected for each parameter: for MTT: >3, 4, 5, or 6 seconds (s), for TTP: >2, 4, 6, or 8s, and for Tmax: >2, 4, 6, and 8s. The volume of reperfusion (Vreperf) was defined as the volume of tissue with perfusion deficit at tp1 and no perfusion deficit at tp2. Clinical improvement was defined as: Admission NIH Stroke Scale (NIHSS) - 1 month NIHSS (ΔNIHSS). A multivariable linear regression model identified if Vreperf as measured by MTT, TTP, or Tmax was an independent predictor of clinical improvement after adjusting for patient age, admission NIHSS, tPA treatment, and volume of tp1 perfusion deficit. Results: Thirty-nine acute ischemic stroke patients were prospectively scanned at 2.8±.8hr (tp1) and 6.4±.4hr (tp2) after stroke onset (mean age=64, 44% female, 36% Black, mean NIHSS=14, 74% received IV tPA). Across the four thresholds, mean volume of perfusion deficit ranged from 58-96ml for MTT, 56-116ml for TTP, and 51-113ml for Tmax. Mean Vreperf ranged from 15-22ml for MTT, 15-23ml for TTP, and 14-21ml for Tmax. In the multivariable linear regression analysis, after adjusting for age, admission NIHSS, tPA treatment, and volume of tp1 perfusion deficit, Vreperf predicted ΔNIHSS for MTT=4s (p=0.007), MTT=5s (p=0.005), and MTT=6s (p=0.010), whereas Vreperf did not predict ΔNIHSS for any TTP or Tmax threshold ( Table ). Conclusion: Reperfusion, defined by MTT, was an independent predictor of clinical improvement, while reperfusion defined by TTP and Tmax were not. Therefore, MTT may be the best time-based perfusion parameter to define clinically-relevant reperfusion after stroke.


PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0119409 ◽  
Author(s):  
Jordi Borst ◽  
Henk A. Marquering ◽  
Ludo F. M. Beenen ◽  
Olvert A. Berkhemer ◽  
Jan Willem Dankbaar ◽  
...  

Stroke ◽  
2019 ◽  
Vol 50 (Suppl_1) ◽  
Author(s):  
Kambiz Nael ◽  
Ehsan Tadayon ◽  
Amy Daoud ◽  
Johanna Fifi ◽  
Stanley Tuhrim ◽  
...  

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0141571 ◽  
Author(s):  
Ralph R. E. G. Geuskens ◽  
Jordi Borst ◽  
Marit Lucas ◽  
A. M. Merel Boers ◽  
Olvert A. Berkhemer ◽  
...  

Radiology ◽  
2019 ◽  
Vol 291 (2) ◽  
pp. 451-458 ◽  
Author(s):  
Paul Reidler ◽  
Kolja M. Thierfelder ◽  
Lukas T. Rotkopf ◽  
Matthias P. Fabritius ◽  
Daniel Puhr-Westerheide ◽  
...  

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