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, June 11, 2020

Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach

What world do you live in where predictions to the failures of status quo rehab mean anything to survivors? This gives you enough time to compose your speech on breaking the bad news of the lack of recovery your patients are going to get? Hell, my doctor knew I wasn't going to recover, so he totally ran away and told me nothing. Like this.

Brave Sir Robin Ran Away

 

Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach




Abstract

Background

In clinical practice, therapists often rely on clinical outcome measures to quantify a patient’s impairment and function. Predicting a patient’s discharge outcome using baseline clinical information may help clinicians design more targeted treatment strategies and better anticipate the patient’s assistive needs and discharge care plan. The objective of this study was to develop predictive models for four standardized clinical outcome measures (Functional Independence Measure, Ten-Meter Walk Test, Six-Minute Walk Test, Berg Balance Scale) during inpatient rehabilitation.

Methods

Fifty stroke survivors admitted to a United States inpatient rehabilitation hospital participated in this study. Predictors chosen for the clinical discharge scores included demographics, stroke characteristics, and scores of clinical tests at admission. We used the Pearson product-moment and Spearman’s rank correlation coefficients to calculate correlations among clinical outcome measures and predictors, a cross-validated Lasso regression to develop predictive equations for discharge scores of each clinical outcome measure, and a Random Forest based permutation analysis to compare the relative importance of the predictors.

Results

The predictive equations explained 70–77% of the variance in discharge scores and resulted in a normalized error of 13–15% for predicting the outcomes of new patients. The most important predictors were clinical test scores at admission. Additional variables that affected the discharge score of at least one clinical outcome were time from stroke onset to rehabilitation admission, age, sex, body mass index, race, and diagnosis of dysphasia or speech impairment.

Conclusions

The models presented in this study could help clinicians and researchers to predict the discharge scores of clinical outcomes for individuals enrolled in an inpatient stroke rehabilitation program that adheres to U.S. Medicare standards.

Background

Stroke remains one of the leading causes of disability worldwide, with the majority of stroke survivors requiring specialized rehabilitation [1]. Inpatient stroke rehabilitation is a program of medical intervention and targeted therapies, which aims to maximize a patient’s functional recovery and facilitate reintegration into the community [2, 3]. To evaluate progress, clinicians use standardized assessment tools or clinical outcome measures such as the Functional Independence Measure [4] (FIM) for level of disability or the Ten-Meter Walk Test [5] (TMWT) for walking ability. Understanding the factors that affect these outcomes may help clinicians to streamline the treatment plan and efficiently allocate rehabilitation resources [6, 7]. Further, clinicians assess a patient’s functional abilities based on performance in these standardized tests, such as classifying patients as household ambulators or limited community ambulators based on walking speed score from the TMWT [8, 9]. Estimating a patient’s future discharge scores early in a rehabilitation program would help clinicians set realistic rehabilitation goals and anticipate needs for additional care or medical equipment at discharge.
Several studies have investigated predictors of clinical outcomes after acute inpatient stroke rehabilitation [10,11,12,13,14,15]. Their main focus was to predict individual’s ability to perform activities of daily living, as measured by the FIM and the Barthel Index [16], or to predict walking speed as measured by the TMWT [14]. These studies found that the clinical assessment scored at discharge could be predicted based on patient demographics such as age [10,11,12,13, 15] and sex [11], medical information such as the time from stroke onset to rehabilitation admission [11, 13] and the admission score of the predicted outcome [10,11,12,13,14]. However, there are some notable gaps in our knowledge and understanding of these outcomes. Specifically, previous studies have primarily investigated predictors of a single clinical outcome measure, while therapists often use multiple standardized tests to gauge functional abilities. The American Physical Therapy Association highly recommends additional tests [6], including the Berg Balance Scale [17] (BBS), which assesses balance outcomes and fall risk, and the Six-Minute Walk Test [18] (SMWT), which assesses walking endurance and aerobic capacity. Understanding interactions among different clinical outcomes may help identify the tests that provide unique information about specific functional abilities compared to tests that may be redundant or unrelated to those abilities. Second, studies have predicted the discharge score of a clinical outcome using admission scores from a small subset of other clinical outcomes [14, 19]. For example, discharge walking speed has been predicted from admission scores of BBS and the Motor Assessment Scale [20]. Considering additional admission assessments should improve predictive accuracy, while including additional discharge assessments should provide a more comprehensive overview of a patient’s functional outcomes. Finally, previous studies developed predictive models for clinical outcomes using stepwise methods based on the predictors’ significance level (p-value). However, the ability of the p-value to determine the importance of predictors and to output the optimal set of predictors is limited, especially for small sample sizes, small ratio of sample size to predictors, and correlated predictors [21,22,23,24,25,26,27]. Conversely, certain machine learning approaches aim to reduce model error by selecting a targeted set of predictors based on relative importance [28] and incorporate regularization mechanisms to produce more accurate and generalizable predictions [29].
The objective of this study was to use machine-learning algorithms to develop predictive models for discharge scores of four standardized clinical tests (FIM, TMWT, SMWT, BBS) after inpatient stroke rehabilitation. Potential predictors included patient demographics, stroke characteristics, and the scores of each of the four tests at admission. We also investigated the correlations between the clinical outcomes and the predictors, stated the predictors’ significance level and compared their relative importance in effecting the discharge scores.


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