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Predicting the length of hospital stay of post-acute care patients in Taiwan using the Chinese version of the continuity assessment record and evaluation item set.

Predicting the length of hospital stay of post-acute care patients in Taiwan using the Chinese version of the continuity assessment record and evaluation item set.
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Hung CY, Wu WT, Chang KV, Wang TG, Han DS,


Hung CY, Wu WT, Chang KV, Wang TG, Han DS, (click to view)

Hung CY, Wu WT, Chang KV, Wang TG, Han DS,

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PloS one 2017 08 2312(8) e0183612 doi 10.1371/journal.pone.0183612
Abstract
BACKGROUND
The Chinese version of the Continuity Assessment Record and Evaluation (CARE-C) item set was developed to facilitate the assessment of post-acute care (PAC) patients in Taiwan. Considering that the length of hospital stay (LOS) has a significant effect on the total healthcare cost, determining whether the CARE-C scores could predict the LOS of PAC patients is of great interest to the PAC providers.

METHODS
This prospective trial included PAC patients with stroke or central nervous system injuries. The demographic data and CARE-C scores were collected after admission and before discharge. A multivariable stepwise linear regression model was used to identify the predictors of the LOS using age, sex, tube placement status, CARE-C component scores at admission, and score differences between admission and discharge as independent variables.

RESULTS
This study included 178 patients (66 women and 112 men), with a mean age of 61.9 ± 15.6 years. Indwelling urinary catheter placement status at admission (β = 0.241, p = 0.002) was a positive predictor of the LOS, whereas age (β = -0.189, p = 0.010), core transfer subscale score at admission (β = -0.176, p = 0.020), and difference in continence subscale score (β = -0.203, p = 0.008) were negative predictors of the LOS. The model explained 14% of the total variance.

CONCLUSIONS
Indwelling urinary catheter placement status at admission, age, core transfer subscale score at admission, and difference in the CARE-C continence subscale score were identified as predictors of the LOS. The explanatory power of these predictors might be limited due to the regulations of Taiwan’s National Health Insurance.

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