A predictive score to identify hospitalized patients' risk of discharge to a post-acute care facility.
BACKGROUND
Early identification of patients who need post-acute care (PAC) may improve discharge planning. The purposes of the study were to develop and validate a score predicting discharge to a post-acute care (PAC) facility and to determine its best assessment time.


METHODS
We conducted a prospective study including 349 (derivation cohort) and 161 (validation cohort) consecutive patients in a general internal medicine service of a teaching hospital. We developed logistic regression models predicting discharge to a PAC facility, based on patient variables measured on admission (day 1) and on day 3. The value of each model was assessed by its area under the receiver operating characteristics curve (AUC). A simple numerical score was derived from the best model, and was validated in a separate cohort.


RESULTS
Prediction of discharge to a PAC facility was as accurate on day 1 (AUC: 0.81) as on day 3 (AUC: 0.82). The day-3 model was more parsimonious, with 5 variables: patient's partner inability to provide home help (4 pts); inability to self-manage drug regimen (4 pts); number of active medical problems on admission (1 pt per problem); dependency in bathing (4 pts) and in transfers from bed to chair (4 pts) on day 3. A score > or = 8 points predicted discharge to a PAC facility with a sensitivity of 87% and a specificity of 63%, and was significantly associated with inappropriate hospital days due to discharge delays. Internal and external validations confirmed these results.


CONCLUSIONS
A simple score computed on the 3rd hospital day predicted discharge to a PAC facility with good accuracy. A score > 8 points should prompt early discharge planning.
Louis Simonet
Martine
M
Service of General Internal Medicine, University Hospitals, Geneva, Switzerland. martine.louissimonet@hcuge.ch
Kossovsky
Michel P
MP
Chopard
Pierre
P
Sigaud
Philippe
P
Perneger
Thomas V
TV
Gaspoz
Jean-Michel
JM
D000368:Aged; D000704:Analysis of Variance; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D010351:Patient Discharge; D011446:Prospective Studies; D012720:Severity of Illness Index; D019467:Subacute Care