AI Article Synopsis
- - The study aimed to validate a new method for predicting the Functional Independence Measure (FIM) scores at discharge for stroke patients upon their admission to a rehabilitation hospital.
- - Researchers analyzed data from 93 patients with cerebral infarction (CI) and 60 with intracerebral hemorrhage (ICH) and compared the accuracy of newly created predictive equations with previous models.
- - Results showed that the new predictive equation was more accurate at estimating discharge FIM scores, especially for CI patients, indicating that updating prediction methods based on current data is beneficial.
Article Abstract
[Purpose] The purpose of this study was to confirm the accuracy of a revised method for predicting the Functional Independence Measure (FIM) at discharge when stroke patients are first admitted to a rehabilitation hospital. [Subjects and Methods] The predictive equation with logarithmic trend line was calculated based on the total score of the FIM at admission and discharge in 93 patients with cerebral infarction (CI) and 60 patients with intracerebral hemorrhage (ICH). In other patients with CI or ICH (validation group), the differences between the actual FIM and the predicted FIM at discharge calculated by the CI or ICH equation and the combined (CI + ICH) equation, as well as by the CI or ICH equation and combined equation used in a previous study, were calculated. [Results] The multiple correlation coefficients of the CI equation, ICH equation, and combined equation were 0.87, 0.71, and 0.8. The residual of the actual FIM and predicted FIM at discharge calculated by the CI equation was the smallest in the CI validation group. In the ICH validation group, the residual calculated for ICH patients alone was smaller than that calculated by the previous ICH equation. [Conclusion] This easy-to-use method using a new equation for prediction was more precise than the previous equation. Therefore, we should revise the equation for predicting stroke patient outcome strata according to data from within the governing medical administration system.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4175250 | PMC |
| http://dx.doi.org/10.1589/jpts.26.1429 | DOI Listing |
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