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A predictive score to identify hospitalized patients' risk of discharge to a post-acute care facility

Kossovsky, Michel
Chopard, Pierre
Sigaud, Philippe
Published in BMC health services research. 2008, vol. 8, no. 154
Abstract 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. CONCLUSION: 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.
Keywords Analysis of VarianceFemaleHealth Status IndicatorsHumansLogistic ModelsMaleMiddle AgedOutcome Assessment (Health Care)Patient DischargeProspective StudiesSeverity of Illness IndexSubacute Care
PMID: 18647410
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Research groups Epidémiologie clinique et biostatistique (115)
Evaluation en qualité des soins (1012)
Groupe Jean-Michel Gaspoz (23)
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LOUIS SIMONET, Martine et al. A predictive score to identify hospitalized patients' risk of discharge to a post-acute care facility. In: BMC health services research, 2008, vol. 8, n° 154. doi: 10.1186/1472-6963-8-154 https://archive-ouverte.unige.ch/unige:647

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Deposited on : 2009-01-20

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