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.

Tuesday, January 30, 2018

Abstract TP158: Neural Function and Injury Combined Best Predict Behavioral Gains During Inpatient Stroke Rehabilitation

You fucking blithering idiots, stroke survivors want 100% recovery, not having you waste your time on useless predictions. My God, have you NEVER talked to any stroke survivor? Without giving them the tyranny of low expectations?
http://stroke.ahajournals.org/content/49/Suppl_1/ATP158
Jessica M Cassidy, Ramesh Srinivasan, Zainab Aziz, Derek Z Yang, Connor O’Brien, Steven C CRAMER

Abstract

Background: A better understanding of the mechanisms of recovery during rehabilitation could inform treatment decision-making. We tested two hypotheses: [1] a combination of neural function and injury measures is better than either measure alone for predicting motor gains during inpatient rehabilitation facility (IRF) admission; and [2] performance of prediction measures varies according to severity of baseline impairment.
Methods: Fifteen patients with subacute stroke (56±12 yr, 16 days post-stroke) admitted to an IRF underwent EEG [3-min, resting-state, dense-array (256-lead)] and MRI [anatomical and diffusion tensor] at IRF admission; and serial behavioral testing. Neural function was assessed using EEG measures of coherence and power from electrodes overlying ipsilesional (M1i) and contralesional (M1c) primary motor cortex, in the Delta (1-3 Hz) and high Beta (20-30 Hz) frequency bands. Neural injury was assessed as integrity of white matter in corpus callosum (CC). Change in arm Fugl-Meyer (FM) and Functional Independent Measurement motor (FIM-m) scores served as primary and secondary behavioral recovery metrics, respectively.
Results: In subjects with moderate or severe impairment (FM <55, N=11), neither neural function (M1i-M1c Delta coherence) nor neural injury (CC integrity) alone significantly predicted FIM-m score change. However, when combined into a single model, these measures did significantly predicted FIM-m score change (R2 = 0.85, p=0.024); note that baseline behavior was not a significant predictor. An identical neural injury+function model approached significance at predicting FM score change (R2 = 0.72 p=0.08). These models failed, however, when applied to all 15 patients (R2 = 0.06, p=0.81).
Conclusions: Results thus far in this ongoing study suggest that recovery during inpatient rehabilitation is best predicted by combining neural function and injury measures, and not by behavioral assessments. Performance of recovery predictors varies according to severity of baseline deficits, as adding mild strokes to moderate/severe strokes increased the sample size but diluted the model. These findings could potentially inform patient selection, treatment decisions, and discharge planning in an IRF setting.
  • Author Disclosures: J.M. Cassidy: None. R. Srinivasan: None. Z. Aziz: None. D.Z. Yang: None. C. O’Brien: None. S.C. Cramer: Consultant/Advisory Board; Modest; Roche, Dart Neuroscience, MicroTransponder.

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