Deans' stroke musings: Predicting Ischemic Stroke Outcomes With Advanced Imaging

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Thursday, February 9, 2017

Predicting Ischemic Stroke Outcomes With Advanced Imaging

It has taken how many years for the stroke world to realize that objective scans of dead and damaged brain would be more useful in predicting recovery that any stroke scale? But they seem fixated on using it to determine usefulness of tPA rather than mapping stroke protocols to recovery from damage in specific locations.
http://www.neurologyadvisor.com/stroke/advanced-imaging-helps-predict-stroke-outcomes/article/636154/
Investigators have developed a model using advanced imaging and clinical measures that can accurately predict outcomes of ischemic stroke and may help identify patients who may benefit from treatment with intravenous tissue plasminogen activator.¹
According to the Centers for Disease Control and Prevention, nearly 800,000 people in the United States experience a stroke each year. Of those, 87% are identified as ischemic strokes.²
Because of the heterogeneity of ischemic strokes, it remains difficult to accurately predict the functional outcome of the patient after treatment. Advanced imaging, however, such as computed tomography (CT) angiography and CT perfusion, can be used to identify patients who would specifically benefit from a particular reperfusion treatment, those who have more extensive involvement and have a low likelihood of benefit, and those who are at higher risk for hemorrhagic transformation after treatment.
Andrew Bivard, PhD, from the Department of Neurology at John Hunter Hospital in Newcastle, Australia, and colleagues sought to identify a predictive model for functional outcome in ischemic stroke, using both advanced imaging and clinical measures.
Data from the INSPIRE registry database, which included prospective information on patients with ischemic stroke from 5 medical centers, were analyzed. The patients included in the study had ischemic stroke symptoms within 4.5 hours of onset and were considered for thrombolysis and underwent either CT perfusion or CT angiography.
Patients were treated with thrombolysis based on the treating physician's judgment and local guidelines. The National Institutes of Health Stroke Scale score was calculated at baseline and repeat imaging, with follow-up magnetic resonance imaging at 24 hours. Investigators then performed backward logistic regression to develop a model to predict patients' modified Rankin scale (mRS) scores at 0 to 1 and 5 to 6, taking into account the advanced imaging and clinical measures.
In total, data from 1519 patients were analyzed, with 635 and 884 in the derivation and validation cohorts, respectively.
The investigators applied a Classification and Regression Tree analysis on the derivation cohort to identify optimal cutpoints in the model to predict excellent (mRS score 0-1), good (mRS 0-2), and poor (mRS 5-6) outcomes. The cutpoints included core volume, penumbra volume, mismatch ratio, DT6 volume, and onset-to-door time. The predictive model accurately predicted excellent outcome (mRS score 0-1) for those being considered for thrombolysis (area under the curve [AUC], 0.91), those treated with thrombolysis (AUC, 0.88), and those who achieved recanalization (AUC, 0.89). Similarly, the model also accurately predicted poor outcome (mRS, 5-6) for those being considered for thrombolysis (AUC, 0.91), those treated with thrombolysis (AUC, 0.89), and those who achieved recanalization (AUC, 0.91).
The patients who received thrombolysis and achieved an excellent outcome score (mRS, 0-2) or poor outcome score (mRS, 5-6) had an odds ratio of 17.89 (4.59-36.35; P <.001) and 8.23 (2.57-26.97; P <.001), respectively. Analysis of the validation cohort revealed similar results for the predictive model.
"Our data [indicate] that advanced CT imaging...such as ischemic core volume provides several magnitudes greater precision in identifying the likelihood of a beneficial recombinant tissue type plasminogen activator treatment response. Therefore, it is important to consider the effects of avoiding patient assessment with advanced imaging to save on time to treatment," the investigators wrote. "Of potentially greater importance is the ability for clinicians to identify a high likelihood of intravenous thrombolysis futility."

Bivard A, Levi C, Lin L, et al. Validating a predictive model of acute advanced imaging biomarkers in ischemic stroke [published online January 19, 2017]. Stroke. doi: 10.1161/STROKEAHA.116.015143.4
Stroke Facts. Centers for Disease Control and Prevention website. https://www.cdc.gov/stroke/facts.htm. Updated December 30, 2016. Accessed February 1, 2017.