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Using admission characteristics to predict return to the community from a post-acute geriatric evaluation and
B J Naughton1, S Saltzman, R Priore
1School of Medicine and Biomedical Sciences, State University of New York at Buffalo, USA.
Objective:
To compare the Cumulative Illness Rating Scale (CIRS) and the Nursing Severity Index (NSI) as independent predictors of discharge outcome from a post-acute GEM unit and to define a multivariate model for predicting the same outcome.
Design:
Retrospective chart review for the entire sample. The sample was split into two cohorts, a derivation cohort (n = 298) and a validation cohort (n = 154).
Setting:
A 20-bed, post-acute GEM unit in a nonproprietary skilled nursing facility.
Participants:
All 452 patients admitted to the GEM from the unit's inception in December 1994 until January 1998.
Measurement:
Demographics, CIRS, NSI, functional status, and social support variables were measured using data available on admission to the GEM unit. The discharge outcome was dichotomized as return to the community or not.
Results:
A total of 99.7% of the individuals in the derivation cohort were living in the community before the index hospitalization; 75.8% of patients in the derivation cohort returned to the community. The NSI, individual "severe" items from the CIRS, age, and social support were in the final logistic regression model fitted to the derivation cohort. A total of 87.7% of the observed discharge outcomes were predicted when the model was applied to the validation cohort and the calculated probability of return to the community.
Conclusions:
Variables for severity of illness, function, social support, and age combined into a logistic regression equation that predicted more than 80% of the dichotomized discharge outcome in the derivation cohort. The proportion of discharge outcomes that were predicted with the validation cohort remained high at 87.7%. The NSI and CIRS were each important to a model that is anticipated to refine the selection of geriatric patients for post-acute services.