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, November 28, 2017

Development and Validation of a Predictive Model for Functional Outcome After Stroke Rehabilitation

You fucking bastards are once again wasting time and money on predictions rather than solving all the problems in stroke. Your mentors and senior researchers need to be keel-hauled.
http://stroke.ahajournals.org/content/48/12/3308?etoc=

The Maugeri Model

Domenico Scrutinio, Bernardo Lanzillo, Pietro Guida, Filippo Mastropasqua, Vincenzo Monitillo, Monica Pusineri, Roberto Formica, Giovanna Russo, Caterina Guarnaschelli, Chiara Ferretti, Gianluigi Calabrese
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Abstract

Background and Purpose—Prediction of outcome after stroke rehabilitation may help clinicians in decision-making and planning rehabilitation care. We developed and validated a predictive tool to estimate the probability of achieving improvement in physical functioning (model 1) and a level of independence requiring no more than supervision (model 2) after stroke rehabilitation.
Methods—The models were derived from 717 patients admitted for stroke rehabilitation. We used multivariable logistic regression analysis to build each model. Then, each model was prospectively validated in 875 patients.
Results—Model 1 included age, time from stroke occurrence to rehabilitation admission, admission motor and cognitive Functional Independence Measure scores, and neglect. Model 2 included age, male gender, time since stroke onset, and admission motor and cognitive Functional Independence Measure score. Both models demonstrated excellent discrimination. In the derivation cohort, the area under the curve was 0.883 (95% confidence intervals, 0.858–0.910) for model 1 and 0.913 (95% confidence intervals, 0.884–0.942) for model 2. The Hosmer–Lemeshow χ2 was 4.12 (P=0.249) and 1.20 (P=0.754), respectively. In the validation cohort, the area under the curve was 0.866 (95% confidence intervals, 0.840–0.892) for model 1 and 0.850 (95% confidence intervals, 0.815–0.885) for model 2. The Hosmer–Lemeshow χ2 was 8.86 (P=0.115) and 34.50 (P=0.001), respectively. Both improvement in physical functioning (hazard ratios, 0.43; 0.25–0.71; P=0.001) and a level of independence requiring no more than supervision (hazard ratios, 0.32; 0.14–0.68; P=0.004) were independently associated with improved 4-year survival. A calculator is freely available for download at https://goo.gl/fEAp81.
Conclusions—This study provides researchers and clinicians with an easy-to-use, accurate, and validated predictive tool for potential application in rehabilitation research and stroke management.

1 comment:

  1. So scary because there are always people in every group who's outcome does not agree with the statistical prediction. A client's response to Rx is a better way to decide to continue Rx than baseline scores. Can you imagine the uproar if a doctor refused to give a drug to someone with cancer because of a statistical formula?

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