Use the labels in the right column to find what you want. Or you can go thru them one by one, there are only 29,027 posts. Searching is done in the search box in upper left corner. I blog on anything to do with stroke.DO NOT DO ANYTHING SUGGESTED HERE AS I AM NOT MEDICALLY TRAINED, YOUR DOCTOR IS, LISTEN TO THEM. BUT I BET THEY DON'T KNOW HOW TO GET YOU 100% RECOVERED. I DON'T EITHER, BUT HAVE PLENTY OF QUESTIONS FOR YOUR DOCTOR TO ANSWER.
This study develops a simple predictive model for identifying stroke patients who have a better chance of showing improved activities of daily living (ADL) outcomes following a stroke.
The cohort of 489 stroke patients was divided into testing and training groups. Multivariate logistic regression analysis was conducted for each model. Four models were compared using the C statistic (AUC), Akaike’s information criterion (AIC), and other metrics. The best model was assessed using a nomogram.
Univariate analysis revealed that several variables measured significantly higher at discharge than at admission, including manual muscle testing, standing, and so on. Multivariate logistic regression analysis revealed that activities-specific balance confidence, Brunnstrom recovery stage for lower extremities, standing, the mini-balance evaluation systems test, and the Hamilton anxiety scale were independent predictors of ADL. Model 1 was found to be more accurate for the prediction of ADL (AUC: training, 0.916 [0.889−0.943] and test, 0.887 [0.806−0.968]; AIC: training, 257.42 and test, 76.79) than model 2 (AUC: training, 0.850 [0.894−0.806] and test, 0.819 [0.715−0.923]; AIC: training, 314.44 and test, 83.78), model 3 (AUC: training, 0.862 [0.901−0.823] and test, 0.830 [0.731−0.929]; AIC: training, 307.76 and test, 86.55), and model 4 (AUC: training, 0.862 [0.901−0.823] and test, 0.833 [0.733−0.932]; AIC: training, 305.8 and test, 86.28).
A multivariate model can be used to predict functionality improvement, as measured by ADL, following hospitalization with a stroke.
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