iScore for Predicting Institutional Care after XXXX . A Population-Based Study.

@entity1 @entity64 @entity204 @entity385
BACKGROUND: We assessed whether the iScore could predict the need for poststroke institutional care. METHODS: @entity1 with acute @entity385 living in Dijon, France, were recorded between 2006 and 2011, using a population-based @entity64 registry. The iScore was calculated for each @entity1 . A logistic regression model was used to assess the performance of the iScore for predicting the need for placement in a care institution. The discrimination and calibration of the model were assessed using the c statistic and the Hosmer-Lemeshow goodness-of-fit test, respectively. RESULTS: Of the 1199 @entity1 recorded, 124 were excluded because of early @entity204 and 95 because of missing for variables included in the iScore. Of the remaining 980 @entity1 , 522 (53.3%) returned home and 458 (46.7%) required placement in a care institution. The median iScore was 123 (interquartile range, 97-148), and the proportion of @entity1 who required placement in a care institution increased with each quintile of risk score. The discrimination of the model was good with a c statistic of .75 (95% confidence interval, .72-.78), as was calibration (P  =  .35). CONCLUSIONS: The iScore could be useful for predicting the need for placement in a care institution  in @entity385 @entity1 . Further studies are required to confirm this finding.
