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A predictive score to identify hospitalized patients' risk of discharge to a post-acute care facility
Martine Louis Simonet1, Michel P Kossovsky, Pierre Chopard
1Service of General Internal Medicine, University Hospitals, Geneva, Switzerland. martine.louissimonet@hcuge.ch
Insights
A new score can accurately predict if patients need post-acute care (PAC) on the third hospital day. A score of 8 or higher indicates a need for early discharge planning to improve care transitions.
Area of Science:
- Healthcare Management
- Patient Outcomes
- Clinical Prediction Models
Background:
- Effective discharge planning is crucial for patient care transitions.
- Early identification of patients requiring post-acute care (PAC) can optimize resource allocation and patient outcomes.
- Developing accurate predictive tools is essential for timely intervention.
Purpose of the Study:
- To develop and validate a predictive score for discharge to a post-acute care (PAC) facility.
- To determine the optimal time for assessing patients to predict PAC needs.
- To enhance the efficiency of discharge planning processes.
Main Methods:
- Prospective study with derivation (349 patients) and validation (161 patients) cohorts.
- Logistic regression models developed using patient data from admission (day 1) and day 3.
- Model performance evaluated using the area under the receiver operating characteristics curve (AUC).
Main Results:
- Prediction accuracy for PAC discharge was similar on day 1 (AUC: 0.81) and day 3 (AUC: 0.82).
- The day 3 model identified key predictors: partner's inability to assist, self-management of medications, number of medical problems, and dependency in bathing and transfers.
- A score >= 8 demonstrated 87% sensitivity and 63% specificity for predicting PAC discharge, and was linked to fewer inappropriate hospital days.
Conclusions:
- A simple score derived on the third hospital day accurately predicts the need for post-acute care (PAC).
- A score exceeding 8 points serves as a strong indicator for initiating early discharge planning.
- This validated score can improve the timeliness and appropriateness of post-acute care referrals.
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.
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