PLoS One. 2023 Feb 24;18(2):e0282272. doi: 10.1371/journal.pone.0282272. eCollection 2023.
BACKGROUND: Discharge planning enhances the safe and timely transfer of inpatients between facilities. Predicting the discharge destination of inpatients with aspiration pneumonia is important for discharge planning. We aimed to develop and validate prediction models for the discharge destination of elderly patients with aspiration pneumonia.
METHODS: Using a nationwide inpatient database, we identified aspiration pneumonia cases for patients aged ≥65 years who had been admitted to hospital from their home or from a nursing home between April 2020 and March 2021. We divided the cases into derivation and validation cohorts according to the location of the admitting hospital. We developed two prediction models by dividing the cases based on the patient's place of residence prior to admission, one model to predict the home discharge of cases admitted from home and the other to predict the home or to a nursing home discharge of cases admitted from a nursing home. The models were internally validated with bootstrapping and internal-externally validated using a validation cohort. Nomograms that could be used easily in clinical practice were also created.
RESULTS: The derivation cohort included 19,746 cases admitted from home and 14,359 cases admitted from a nursing home. Of the former, 10,760 (54.5%) cases were discharged home; from the latter, 7,071 (49.2%) were discharged to either home or a nursing home. The validation cohort included 6,262 cases admitted from home and 6,352 cases admitted from a nursing home. In the internal-external validation, the C-statistics of the final model for the cases admitted from home and the cases admitted from a nursing home were 0.71 and 0.67, respectively.
CONCLUSIONS: We developed and validated new prediction models for the discharge of elderly patients with aspiration pneumonia either to home or to a nursing home. Our models and nomograms could facilitate the early implementation of discharge planning.
PMID:36827320 | DOI:10.1371/journal.pone.0282272