scholarly journals Outcome Prediction for Patients With Ischemic Stroke in Acute Care: New Three-Level Model by Eating and Bladder Functions

2021 ◽  
Author(s):  
Kensaku Uchida ◽  
Yuki Uchiyama ◽  
Kazuhisa Domen ◽  
Tetsuo Koyama
Keyword(s):  
Ischemic Stroke ◽  
Acute Care ◽  
Outcome Prediction ◽  
Level Model

scholarly journals Acute Care of Ischemic Stroke Patients in the Hospital

2015 ◽  
Vol 35 (06) ◽  
pp. 629-637
Author(s):  
Ahmad Thabet ◽  
S. Josephson ◽  
Karl Meisel
Keyword(s):  
Ischemic Stroke ◽  
Acute Care ◽  
Stroke Patients

Abstract 2917: Outcome Prediction With A Population-based Ischemic Stroke Atlas

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Wieslaw L Nowinski ◽  
Varsha Gupta ◽  
Guoyu Qian ◽  
Wojciech Ambrosius ◽  
Jie He ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Outcome Prediction ◽  
Three Dimensional ◽  
Prediction Method ◽  
Population Based ◽  
Novel Approach ◽  
History Of ◽  
High Prediction ◽  
Predicted Values ◽  
One Year

Outcome prediction is critical in stroke patient management. We propose a novel approach combining imaging with parameters (including history, hospitalization, demographics, clinical and outcome) for a population of patients in the Probabilistic Stroke Atlas (PSA) along with prediction engine. The PSA aggregates multiplicity of data for a population of stroke patients and presents them in image format. The PSA is composed from a series of three-dimensional (3D) image volumes including scans and parameters. A cohort of over 700 ischemic stroke generally treated patients with 176 parameters per patient, and CT scan performed at admission and on day 7 was acquired. Outcome measurements were assessed up to one year after stroke onset. Cases with old infarcts, infarcts in both hemispheres, and hemorrhagic transformations were rejected. This data was post-processed to build the PSA and then the PSA was used for prediction. The infarcts were delineated on CT scans and their 3D surface models constructed and normalized. The PSA was calculated from the normalized 3D infarct models as frequency of stroke occurrence. Similar maps were calculated for the following parameters: Age; Sex; Survival; NIH Stroke Scale (NIHSS); Barthel Index (BI) at 30, 90, 180, 360 days; modified Rankin Scale (mRS) at 7, 30, 90, 180, 360 days; White blood cell count; C-reative protein; Glucose at emergency department; History of hypertension; and History of diabetes. The PSA was used for prediction of mRS and BI for 50 stroke subjects. For a given case to be predicted, the infarct was delineated and analyzed by the PSA mapped on the scan. The predicted values of the parameters from the PSA were compared with the actual values of the parameters measured in up to 1-year neurological follow up. The accuracy was defined as 100*(1-(actual value-predicted value)/actual value)%. The mean prediction accuracy of mRS at (7, 30, 90, 180, 360) days is (89.7, 90.7, 92.1, 87.0, 83.3)% and that for BI at (30, 90, 180, 360) days is (90.0, 95.4, 94.4, 92.2)% respectively. This novel prediction method has high prediction rates. It can be applied to any other parameters. The PSA is dynamic and its power can increase with additional cases.

scholarly journals Post–Acute Care Data for Predicting Readmission After Ischemic Stroke: A Nationwide Cohort Analysis Using the Minimum Data Set

2015 ◽  
Vol 4 (9) ◽  
Author(s):  
Corey R. Fehnel ◽  
Yoojin Lee ◽  
Linda C. Wendell ◽  
Bradford B. Thompson ◽  
N. Stevenson Potter ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Care ◽  
Cohort Analysis ◽  
Minimum Data Set ◽  
Data Set ◽  
Minimum Data ◽  
Post Acute Care

scholarly journals Multi-site laser Doppler flowmetry for assessing collateral flow in experimental ischemic stroke: Validation of outcome prediction with acute MRI

2016 ◽  
Vol 37 (6) ◽  
pp. 2159-2170 ◽  
Author(s):  
Elisa Cuccione ◽  
Alessandro Versace ◽  
Tae-Hee Cho ◽  
Davide Carone ◽  
Lise-Prune Berner ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Middle Cerebral Artery ◽  
Cerebral Artery ◽  
Laser Doppler Flowmetry ◽  
Outcome Prediction ◽  
Laser Doppler ◽  
Collateral Flow ◽  
Perfusion Deficit ◽  
Ischemic Lesion ◽  
Doppler Flowmetry

High variability in infarct size is common in experimental stroke models and affects statistical power and validity of neuroprotection trials. The aim of this study was to explore cerebral collateral flow as a stratification factor for the prediction of ischemic outcome. Transient intraluminal occlusion of the middle cerebral artery was induced for 90 min in 18 Wistar rats. Cerebral collateral flow was assessed intra-procedurally using multi-site laser Doppler flowmetry monitoring in both the lateral middle cerebral artery territory and the borderzone territory between middle cerebral artery and anterior cerebral artery. Multi-modal magnetic resonance imaging was used to assess acute ischemic lesion (diffusion-weighted imaging, DWI), acute perfusion deficit (time-to-peak, TTP), and final ischemic lesion at 24 h. Infarct volumes and typology at 24 h (large hemispheric versus basal ganglia infarcts) were predicted by both intra-ischemic collateral perfusion and acute DWI lesion volume. Collateral flow assessed by multi-site laser Doppler flowmetry correlated with the corresponding acute perfusion deficit using TTP maps. Multi-site laser Doppler flowmetry monitoring was able to predict ischemic outcome and perfusion deficit in good agreement with acute MRI. Our results support the additional value of cerebral collateral flow monitoring for outcome prediction in experimental ischemic stroke, especially when acute MRI facilities are not available.

scholarly journals PST4 DIFFERENCES IN DISCHARGE LOCATION FOR PATIENTS WITH MULTIPLE ISCHEMIC STROKE ADMISSIONS: IMPLICATIONS FOR POST-ACUTE CARE COSTS

2006 ◽  
Vol 9 (6) ◽  
pp. A330
Author(s):  
JA O'Brien ◽  
PA Duran ◽  
JJ Caro
Keyword(s):  
Ischemic Stroke ◽  
Acute Care ◽  
Discharge Location ◽  
Care Costs ◽  
Post Acute Care

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

A simple scoring system for outcome prediction of ischemic stroke

2011 ◽  
Vol 124 (5) ◽  
pp. 334-342 ◽  
Author(s):  
A. Muscari ◽  
G. M. Puddu ◽  
N. Santoro ◽  
M. Zoli
Keyword(s):  
Ischemic Stroke ◽  
Scoring System ◽  
Outcome Prediction

scholarly journals Outcome prediction in patients with putaminal hemorrhage at admission to a convalescent rehabilitation ward based on hemorrhage volume assessed with computed tomography during acute care

2021 ◽  
Vol 33 (1) ◽  
pp. 27-31
Author(s):  
Toshiki Yasukawa ◽  
Tetsuo Koyama ◽  
Yuki Uchiyama ◽  
Saya Iwasa ◽  
Jun Saito ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Acute Care ◽  
Outcome Prediction ◽  
Putaminal Hemorrhage ◽  
Hemorrhage Volume ◽  
Rehabilitation Ward

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
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