American journal of physical medicine & rehabilitation · 2007
Predicting discharge of trauma survivors to rehabilitation: a sampling frame solution for a population-based trauma-rehabilitation survey.
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Sirois Marie-Josée, Lavoie André, Dionne Clermont E
Аннотация
OBJECTIVES: To conduct a population-based survey among trauma survivors on accessibility to rehabilitation services in metropolitan, urban, and rural areas in Quebec (Canada), we attempted to use trauma registries as a sampling frame of subjects discharged to rehabilitation. Discharge destinations were inaccurate in many registries, preventing straightforward identification of the survey subjects. Using the best registry data, we aimed to identify predictors of rehabilitation discharge and to use them to specify a reliable sampling frame for the survey.
DESIGN: A logistic predictive model of rehabilitation discharge was developed. This model was applied to data from metropolitan, urban, and rural trauma centers to identify all subjects predicted to be discharged to a rehabilitation facility.
RESULTS: Age, acute-care length of stay, injury-severity score, lower-limb injuries, and seven other predictors were included in the model that generated an area under the ROC curve (AUC) of 0.83 and a classification accuracy of 76.6%. The metropolitan, urban, and rural frames were slightly different. They included, respectively, 808, 798, and 929 subjects.
CONCLUSIONS: The procedure helped us bypass largely inaccurate data from trauma registries. The sampling frames reflected severely injured trauma survivors who were likely to have been referred to postacute rehabilitation.
DESIGN: A logistic predictive model of rehabilitation discharge was developed. This model was applied to data from metropolitan, urban, and rural trauma centers to identify all subjects predicted to be discharged to a rehabilitation facility.
RESULTS: Age, acute-care length of stay, injury-severity score, lower-limb injuries, and seven other predictors were included in the model that generated an area under the ROC curve (AUC) of 0.83 and a classification accuracy of 76.6%. The metropolitan, urban, and rural frames were slightly different. They included, respectively, 808, 798, and 929 subjects.
CONCLUSIONS: The procedure helped us bypass largely inaccurate data from trauma registries. The sampling frames reflected severely injured trauma survivors who were likely to have been referred to postacute rehabilitation.