Utility of functional status for classifying community versus institutional discharges after inpatient rehabilitation for stroke

Abstract

Objective: To evaluate the ability of patient functional status to differentiate between community and institutional discharges after rehabilitation for stroke.

Design: Retrospective cross-sectional design.

Setting: Inpatient rehabilitation facilities contributing to the Uniform Data System for Medical Rehabilitation.

Participants: Patients (N=157,066) receiving inpatient rehabilitation for stroke from 2006 and 2007.

Interventions: Not applicable.

Main outcome measure: Discharge FIM rating and discharge setting (community vs institutional).

Results: Approximately 71% of the sample was discharged to the community. Receiver operating characteristic curve analyses revealed that FIM total performed as well as or better than FIM motor and FIM cognition subscales in differentiating discharge settings. Area under the curve for FIM total was .85, indicating very good ability to identify persons discharged to the community. A FIM total rating of 78 was identified as the optimal cut point for distinguishing between positive (community) and negative (institution) tests. This cut point yielded balanced sensitivity and specificity (both=.77).

Conclusions: Discharge planning is complex, involving many factors. Identifying a functional threshold for classifying discharge settings can provide important information to assist in this process. Additional research is needed to determine if the risks and benefits of classification errors justify shifting the cut point to weight either sensitivity or specificity of FIM ratings.

Fig 1

ROC curves comparing the ability of discharge FIM total, FIM motor, and FIM cognition to classify discharge setting after inpatient rehabilitation for stroke. AUC quantifies the relative strength of the test to distinguish between patients discharged to the community versus an institution.

Fig 2

ROC curve for discharge FIM total scores to differentiate between community and institutional discharges. The same cut point (FIM=78) was identified by the closest-to-(0,1) and Youden Index approaches, which locate the point on the ROC curve closest to perfect discrimination and furthest from chance discrimination, respectively.

Fig 3

Probability density functions for FIM total scores from the 2 discharge groups. The cut point shown represents the optimal FIM score for differentiating positive and negative tests when errors in sensitivity and specificity are equally weighted. The graph also shows the tradeoff between these errors (false-negative and false-positive rates) when moving the cut point from its current value.