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.

Monday, November 2, 2020

Topographical data analysis to identify high-density clusters in stroke patients undergoing post-acute rehabilitation

 Whatever the hell this means.

Topographical data analysis to identify high-density clusters in stroke patients undergoing post-acute rehabilitation

Eliezer Bose ORCID Icon, Lisa J. Wood & Qing Mei Wang
Received 17 Jun 2020, Accepted 17 Oct 2020, Published online: 29 Oct 2020
 

ABSTRACT

Background

During acute stroke rehabilitation, the recovery of motor and cognitive function is highly variable: while some patients regain function, others do not.

Objective

Our objective was to identify data-driven subgroups of stroke patients undergoing acute rehabilitation using topological data analysis (TDA), compare TDA with K-means clustering, and to assess inter-group demographic and clinical differences among the subgroups.

Methods

This is a secondary data analysis of clinical, functional outcomes, and demographic data collected from 339 stroke patients undergoing acute rehabilitation post-stroke. We identified stroke recovery sub-groups using TDA on the point cloud, persistent homology, and finally, density clustering. We assessed inter-group differences in demographic and clinical characteristics using one-way ANOVA, Kruskal-Wallis, or χ2 tests.

Results

TDA revealed three high-density clusters among 137 subjects in the point cloud- poor-recoverers (G1(n = 34)), intermediate-recoverers (G2 (n = 88)) and good-recoverers (G3(n = 15)).

Significant differences across clusters were observed for amantadine use (p = .009), number of stroke risk factors (p = .047), creatinine (p = .015), length of stay (p < .001), discharge destination (p < .001), FIM motor, FIM cognition, and FIM total on admission and discharge (all p < .001), and motor, cognition, and total MRFS scores (all p < .001)

Conclusion

This study revealed that in addition to functional status on admission, stroke risk factors are associated with recovery outcomes. Future studies using TDA to analyze omic data, including clinical, biological, and sociodemographic factors, will accelerate the development of personalized treatment plans in post-acute stroke rehabilitation patients.

 
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