http://stroke.ahajournals.org/content/48/12/3308?etoc=
The Maugeri Model
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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.
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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