Is there more valuable information in PWI datasets for a voxel-wise acute ischemic stroke tissue outcome prediction than what is represented by typical perfusion maps?

2014 ◽  
Author(s):  
Nils Daniel Forkert ◽  
Susanne Siemonsen ◽  
Michael Dalski ◽  
Tobias Verleger ◽  
Andre Kemmling ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction ◽  
Perfusion Maps

scholarly journals Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1909
Author(s):  
Dougho Park ◽  
Eunhwan Jeong ◽  
Haejong Kim ◽  
Hae Wook Pyun ◽  
Haemin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

ASL perfusion in acute ischemic stroke: The value of CBF in outcome prediction

2020 ◽  
Vol 194 ◽  
pp. 105908 ◽  
Author(s):  
Aleksandra Aracki-Trenkic ◽  
Bruno Law-ye ◽  
Zoran Radovanovic ◽  
Dragan Stojanov ◽  
Didier Dormont ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction

Abstract 3048: Real-World Clinical Use of CT Perfusion for Diagnosis and Prediction of Lesion Growth in Acute Ischemic Stroke

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Branko N Huisa ◽  
William P Neil ◽  
Nhu T Bruce ◽  
Marcel Maya ◽  
Benedict Pereira ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Real World ◽  
Cerebral Blood Volume ◽  
Ct Perfusion ◽  
Medical Center ◽  
Brain Mri ◽  
Academic Medical Center ◽  
Acute Ischemia ◽  
Perfusion Maps

Background: Diffusion-weighted imaging (DWI) detects acute ischemia with a high sensitivity. In research centers, qualitative CT perfusion (CTP) mapping correlates well with DWI and may accurately differentiate the infarct core from ischemic penumbra. The value of the CTP in real-world clinical practice, however, has not been fully established. We investigated the yield of CTP - derived cerebral blood volume (CBV) and mean transient time (MTT) for the detection of cerebral ischemia in a sample of acute ischemic stroke (AIS) patients. Methods: In a large metropolitan academic medical center that is a certified Primary Stroke Center (PSC) we retrospectively studied 162 patients who presented between January 2008 and July 2010 with symptoms suggestive of AIS. All patients had an initial Code Brain protocol including non-contrast head CT, CTP, and CTA. As clinically indicated, some patients underwent follow up brain MRI within 48 hours. Acute perfusion maps were derived in real time by a trained operator. From the obtained images CBV, MTT and DWI lesion volumes were manually traced using planimetry (ImageJ v1.42) by two stroke neurologists blinded to clinical information. Volumes were calculated using the Cavaleri theorem. Sensitivity, specificity and statistical analysis were calculated using Graph Pad 5.0. Results: Of 162 patients with acute stroke-like symptoms, 73 had DWI lesions. The sensitivity and specificity to detect abnormal DWI signals were 23% and 100%, for CBV; and 43.8% and 98.9% for MTT. For DWI lesions ≥5ml the yield was 59.3% for CVB and 77.8% for MTT. For lesions ≥10ml the yield was 68.4% for CBV and 89.5% for MTT. In patients with NIHSS ≥5, CBV predicted abnormal DWI in 22.6% and MTT in 35.5%. In patients with NIHSS ≥10, CBV and MTT, both had a yield of 50.0%. A CBV - MTT mismatch of >25% predicted MRI lesion extension in 81.25% of the cases. There were small but significant correlations for DWI versus CBV lesion volumes ( r 2 0.32, P= 0.0001), and for DWI versus MTT lesion volumes ( r 2 0.29, P <0.0001). Correlation between DWI and perfusion maps for MCA territory infarcts were CBV ( r 2 0.3, P <0.0001) and MTT ( r 2 0.45, P <0.0001). Conclusions: In real-world deployment during a Code Brain protocol in a busy PSC, acute imaging with CTP did not predict DWI lesions on brain MRI with sufficient accuracy. In patients with large lesions the predictive value was better.

Abstract 164: Outcome Prediction After Endovascular Reperfusion Therapy For Acute Ischemic Stroke: The SNARL Score

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Shyam Prabhakaran ◽  
Kevin N Sheth ◽  
John B Terry ◽  
Raul G Nogueira ◽  
Anat Horev ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction ◽  
Validation Cohort ◽  
Reperfusion Therapy ◽  
Anterior Circulation ◽  
Derivation Cohort ◽  
Good Outcome ◽  
C Statistic ◽  
Pre Treatment

Background: Tools to predict outcome after endovascular reperfusion therapy (ERT) for acute ischemic stroke (AIS) have previously included only pre-treatment variables. We sought to derive and validate an outcome prediction score based on readily available pre-treatment and treatment factors. Methods: The derivation cohort consisted of 516 patients with anterior circulation AIS from 9 centers from September 2009-July 2011. The validation cohort consisted of 110 patients with anterior circulation AIS from the Penumbra Pivotal Trial. Multivariable logistic regression identified predictors of good outcome, defined as a modified Rankin Score (mRS) of < 2, in the derivation sample; model beta coefficients were used to assign point scores. Discrimination was tested using C-statistics. We then validated the score in the Penumbra cohort and performed calibration (predicted versus observed good outcome) in both cohorts. Results: Good outcome at 3 months was noted in 189 (36.8%) patients in the derivation cohort. The independent predictors of good outcome were A ge (2 pts: <60; 1 pt: 60-79; 0 pts: >79), N IHSS score (4 pts: 0-10; 2 pts: 11-20; 0 pts: > 20), L ocation of clot (2 pts: M2; 1 pt: M1; 0 pts: ICA), R ecanalization (5 pts: TICI 2 or 3), and S ymptomatic hemorrhage (2 pts: none, HT1-2, or PH1; 0 pts: PH2). The outcome (SNARL) score demonstrated good discrimination in the derivation cohort (C-statistic 0.78, 95% CI 0.72-0.78) and validation cohort (C-statistic 0.74, 95% CI 0.64-0.84). There was excellent calibration in each cohort (Figure). Conclusions: The SNARL score is a validated tool to determine the probability of functional recovery among AIS treated with endovascular reperfusion strategies. Unlike previous scores that did not include treatment factors such as successful recanalization or hemorrhagic complications, our score can be applied to patients after treatment and may provide guidance to physicians, patients, and families about expected functional outcome.

scholarly journals Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88225 ◽  
Author(s):  
Hamed Asadi ◽  
Richard Dowling ◽  
Bernard Yan ◽  
Peter Mitchell
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction

scholarly journals Neural Network–derived Perfusion Maps for the Assessment of Lesions in Patients with Acute Ischemic Stroke

2019 ◽  
Vol 1 (5) ◽  
pp. e190019
Author(s):  
Raphael Meier ◽  
Paula Lux ◽  
B Med ◽  
Simon Jung ◽  
Urs Fischer ◽  
...  
Keyword(s):  
Neural Network ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Perfusion Maps

EASY Score (Eloquent, Age and baseline SYmptoms score) For Outcome Prediction In Patients With Acute Ischemic Stroke

2021 ◽  
pp. 106626
Author(s):  
Basile Kerleroux ◽  
Christophe Tomasino ◽  
Diogo Soriano ◽  
Paula G Rodrigues ◽  
Fernando SILVA Moura ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction

scholarly journals P.136 Evaluating outcome prediction models in endovascular treatment for acute ischemic stroke using baseline, treatment and post-treatment variables

2021 ◽  
Vol 48 (s3) ◽  
pp. S58-S58
Author(s):  
JM Ospel ◽  
A Ganesh ◽  
M Kappelhof ◽  
R McDonough ◽  
R Nogueira ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Endovascular Treatment ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Model Performance ◽  
Treatment Decision ◽  
Additional Treatment ◽  
Post Treatment ◽  
Considerable Uncertainty

Background: Predicting outcomes after endovascular treatment (EVT) for acute ischemic stroke with baseline variables remains a challenge. We assessed the performance of stroke outcome prediction models for EVT in acute ischemic stroke in an iterative fashion using baseline, treatment-related and post-treatment variables. Methods: Data from the ESCAPE-NA1 trial were used to build 4 outcome prediction models using multi-variable logistic regression: Model 1 included baseline variables only that are available prior to treatment decision-making, model 2 included additional treatment-related variables, model 3 additional early post-treatment variables, and model 4 additional late post-treatment variables. The primary outcome was 90-day modified Rankin Scale score 0-2. Model performance was compared using the area under the curve (AUC). Results: Among 1,105 patients, good outcome was achieved by 666 (60.3%). When using baseline variables only (model 1), the AUC was 0.74 (95%CI:0.71-0.77); this iteratively improved when treatment and post-treatment variables were added to the models (model 2: AUC 0.77,95%CI: 0.74-0.80, model 3: AUC 0.80,95%CI:0.77-0.83, model 4: AUC 0.82, 95%CI:0.79-0.85). Conclusions: Predicting EVT outcomes using baseline variables alone is inaccurate in one in four patients, and may be inappropriate for patient selection. Even the most comprehensive models with treatment-related and post-treatment factors involve considerable uncertainty.

Sign in / Sign up

Export Citation Format

Share Document